{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/denoising/papers/ran/5","list_of":"/task/denoising","task":"Denoising","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":5,"pages_in_order":9,"rows_per_page":100,"rows":[401,500],"of":832,"counts":{"archive_papers_tagged":7282,"with_a_code_link":2838,"where_syntology_ran_a_sample":832,"not_listed_spam_title":0,"listed":7282,"listed_where_code_ran":832,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":720,"every_run_a_failure_of_syntologys_instrument":112,"listed_with_a_run_with_no_instrument_failure":720,"listed_every_run_a_failure_of_syntologys_instrument":112,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/denoising/papers/ran/1","prev":"/task/denoising/papers/ran/4","next":"/task/denoising/papers/ran/6","papers":[{"url":"/paper/denoising-diffusion-bridge-models","slug":"denoising-diffusion-bridge-models","title":"Denoising Diffusion Bridge Models","date":"2023-09-29","arxiv_id":"2309.16948","repositories_listed":4,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/denoising-diffusion-bridge-models#ran","syntology_url":"https://syntology.ai/paper/2309.16948","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16948"}},"official":{"repos":["alexzhou907/DDBM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/on-the-posterior-distribution-in-denoising","slug":"on-the-posterior-distribution-in-denoising","title":"On the Posterior Distribution in Denoising: Application to Uncertainty Quantification","date":"2023-09-24","arxiv_id":"2309.13598","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-the-posterior-distribution-in-denoising#ran","syntology_url":"https://syntology.ai/paper/2309.13598","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13598"}},"official":{"repos":["HilaManor/GaussianDenoisingPosterior"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/score-mismatching-for-generative-modeling","slug":"score-mismatching-for-generative-modeling","title":"Score Mismatching for Generative Modeling","date":"2023-09-20","arxiv_id":"2309.11043","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":3,"n_violates":1,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/score-mismatching-for-generative-modeling#ran","syntology_url":"https://syntology.ai/paper/2309.11043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11043"}},"official":{"repos":["senmaoy/Score-Mismatching"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/accelerating-diffusion-based-text-to-audio","slug":"accelerating-diffusion-based-text-to-audio","title":"ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation","date":"2023-09-19","arxiv_id":"2309.10740","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":2,"n_pointer_only":3,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/accelerating-diffusion-based-text-to-audio#ran","syntology_url":"https://syntology.ai/paper/2309.10740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.10740"}},"official":{"repos":["Bai-YT/ConsistencyTTA"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/beta-diffusion","slug":"beta-diffusion","title":"Beta Diffusion","date":"2023-09-14","arxiv_id":"2309.07867","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":9,"n_instrument":4,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/beta-diffusion#ran","syntology_url":"https://syntology.ai/paper/2309.07867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07867"}},"official":{"repos":["mingyuanzhou/Beta-Diffusion"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hat-hybrid-attention-transformer-for-image","slug":"hat-hybrid-attention-transformer-for-image","title":"HAT: Hybrid Attention Transformer for Image Restoration","date":"2023-09-11","arxiv_id":"2309.05239","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hat-hybrid-attention-transformer-for-image#ran","syntology_url":"https://syntology.ai/paper/2309.05239","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05239"}},"official":{"repos":["xpixelgroup/hat"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/diffusion-model-is-secretly-a-training-free","slug":"diffusion-model-is-secretly-a-training-free","title":"Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter","date":"2023-09-06","arxiv_id":"2309.02773","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-model-is-secretly-a-training-free#ran","syntology_url":"https://syntology.ai/paper/2309.02773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.02773"}},"official":{"repos":["VCG-team/DiffSegmenter"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lfads-torch-a-modular-and-extensible","slug":"lfads-torch-a-modular-and-extensible","title":"lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems","date":"2023-09-03","arxiv_id":"2309.01230","repositories_listed":3,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lfads-torch-a-modular-and-extensible#ran","syntology_url":"https://syntology.ai/paper/2309.01230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.01230"}},"official":{"repos":["arsedler9/lfads-torch"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/diffsmooth-certifiably-robust-learning-via","slug":"diffsmooth-certifiably-robust-learning-via","title":"DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing","date":"2023-08-28","arxiv_id":"2308.14333","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffsmooth-certifiably-robust-learning-via#ran","syntology_url":"https://syntology.ai/paper/2308.14333","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14333"}},"official":{"repos":["javyduck/diffsmooth"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/steerable-conditional-diffusion-for-out-of","slug":"steerable-conditional-diffusion-for-out-of","title":"Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction","date":"2023-08-28","arxiv_id":"2308.14409","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/steerable-conditional-diffusion-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2308.14409","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14409"}},"official":{"repos":["alexdenker/SteerableConditionalDiffusion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/sampling-with-flows-diffusion-and","slug":"sampling-with-flows-diffusion-and","title":"Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective","date":"2023-08-27","arxiv_id":"2308.14085","repositories_listed":1,"syntology":{"n":18,"n_ran":18,"n_constructed":0,"n_ran_checked":18,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":18,"n_pointer_only":18,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 0 violated, 18 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sampling-with-flows-diffusion-and#ran","syntology_url":"https://syntology.ai/paper/2308.14085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14085"}},"official":{"repos":["idephics/diffsamp"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":18,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/region-disentangled-diffusion-model-for-high","slug":"region-disentangled-diffusion-model-for-high","title":"Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation","date":"2023-08-25","arxiv_id":"2308.13568","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/region-disentangled-diffusion-model-for-high#ran","syntology_url":"https://syntology.ai/paper/2308.13568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13568"}},"official":{"repos":["debadityaqu/rddm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/residual-denoising-diffusion-models","slug":"residual-denoising-diffusion-models","title":"Residual Denoising Diffusion Models","date":"2023-08-25","arxiv_id":"2308.13712","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/residual-denoising-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2308.13712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13712"}},"official":{"repos":["nachifur/rddm"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/inversesr-3d-brain-mri-super-resolution-using","slug":"inversesr-3d-brain-mri-super-resolution-using","title":"InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model","date":"2023-08-23","arxiv_id":"2308.12465","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":4,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inversesr-3d-brain-mri-super-resolution-using#ran","syntology_url":"https://syntology.ai/paper/2308.12465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12465"}},"official":{"repos":["biomedai-ucsc/inversesr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusiontrack-diffusion-model-for-multi","slug":"diffusiontrack-diffusion-model-for-multi","title":"DiffusionTrack: Diffusion Model For Multi-Object Tracking","date":"2023-08-19","arxiv_id":"2308.09905","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":2,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 3 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/diffusiontrack-diffusion-model-for-multi#ran","syntology_url":"https://syntology.ai/paper/2308.09905","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09905"}},"official":{"repos":["rainbowluocs/diffusiontrack"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-implicit-variational-inference-via-score","slug":"semi-implicit-variational-inference-via-score","title":"Semi-Implicit Variational Inference via Score Matching","date":"2023-08-19","arxiv_id":"2308.10014","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":6,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semi-implicit-variational-inference-via-score#ran","syntology_url":"https://syntology.ai/paper/2308.10014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10014"}},"official":{"repos":["longinyu/sivism"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":6,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/far3d-expanding-the-horizon-for-surround-view","slug":"far3d-expanding-the-horizon-for-surround-view","title":"Far3D: Expanding the Horizon for Surround-view 3D Object Detection","date":"2023-08-18","arxiv_id":"2308.09616","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":8,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/far3d-expanding-the-horizon-for-surround-view#ran","syntology_url":"https://syntology.ai/paper/2308.09616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09616"}},"official":{"repos":["megvii-research/far3d"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffcharge-generating-ev-charging-scenarios","slug":"diffcharge-generating-ev-charging-scenarios","title":"DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model","date":"2023-08-18","arxiv_id":"2308.09857","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffcharge-generating-ev-charging-scenarios#ran","syntology_url":"https://syntology.ai/paper/2308.09857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.09857"}},"official":{"repos":["lsy-cython/diffcharge"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/precipitation-nowcasting-with-generative","slug":"precipitation-nowcasting-with-generative","title":"Precipitation nowcasting with generative diffusion models","date":"2023-08-13","arxiv_id":"2308.06733","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/precipitation-nowcasting-with-generative#ran","syntology_url":"https://syntology.ai/paper/2308.06733","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06733"}},"official":{"repos":["fmerizzi/precipitation-nowcasting-with-generative-diffusion-models"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cyclic-test-time-adaptation-on-monocular","slug":"cyclic-test-time-adaptation-on-monocular","title":"Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction","date":"2023-08-12","arxiv_id":"2308.06554","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/cyclic-test-time-adaptation-on-monocular#ran","syntology_url":"https://syntology.ai/paper/2308.06554","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06554"}},"official":{"repos":["hygenie1228/cycleadapt_release"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/modelscope-text-to-video-technical-report","slug":"modelscope-text-to-video-technical-report","title":"ModelScope Text-to-Video Technical Report","date":"2023-08-12","arxiv_id":"2308.06571","repositories_listed":5,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/modelscope-text-to-video-technical-report#ran","syntology_url":"https://syntology.ai/paper/2308.06571","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06571"}},"official":{"repos":["exponentialml/text-to-video-finetuning"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/taming-the-power-of-diffusion-models-for-high","slug":"taming-the-power-of-diffusion-models-for-high","title":"Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow","date":"2023-08-11","arxiv_id":"2308.06101","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":3,"n_no_contract":6,"n_pointer_only":2,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 3 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/taming-the-power-of-diffusion-models-for-high#ran","syntology_url":"https://syntology.ai/paper/2308.06101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.06101"}},"official":{"repos":["bcmi/DCI-VTON-Virtual-Try-On"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/lighting-every-darkness-in-two-pairs-a","slug":"lighting-every-darkness-in-two-pairs-a","title":"Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model","date":"2023-08-07","arxiv_id":"2308.03448","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/lighting-every-darkness-in-two-pairs-a#ran","syntology_url":"https://syntology.ai/paper/2308.03448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03448"}},"official":{"repos":["srameo/led"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-models-for-counterfactual-1","slug":"diffusion-models-for-counterfactual-1","title":"Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images","date":"2023-08-03","arxiv_id":"2308.02062","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffusion-models-for-counterfactual-1#ran","syntology_url":"https://syntology.ai/paper/2308.02062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.02062"}},"official":{"repos":["alessandro-f/dif-fuse"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/patched-denoising-diffusion-models-for-high","slug":"patched-denoising-diffusion-models-for-high","title":"Patched Denoising Diffusion Models For High-Resolution Image Synthesis","date":"2023-08-02","arxiv_id":"2308.01316","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/patched-denoising-diffusion-models-for-high#ran","syntology_url":"https://syntology.ai/paper/2308.01316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01316"}},"official":{"repos":["mlpc-ucsd/patch-dm"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ec-conf-an-ultra-fast-diffusion-model-for","slug":"ec-conf-an-ultra-fast-diffusion-model-for","title":"EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency","date":"2023-08-01","arxiv_id":"2308.00237","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ec-conf-an-ultra-fast-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2308.00237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.00237"}},"official":{"repos":["deeplearningps/ecconf"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-general-low-light-raw-noise-synthesis","slug":"towards-general-low-light-raw-noise-synthesis","title":"Towards General Low-Light Raw Noise Synthesis and Modeling","date":"2023-07-31","arxiv_id":"2307.16508","repositories_listed":1,"syntology":{"n":20,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/towards-general-low-light-raw-noise-synthesis#ran","syntology_url":"https://syntology.ai/paper/2307.16508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16508"}},"official":{"repos":["fengzhang427/LRD"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/random-sub-samples-generation-for-self","slug":"random-sub-samples-generation-for-self","title":"Random Sub-Samples Generation for Self-Supervised Real Image Denoising","date":"2023-07-31","arxiv_id":"2307.16825","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":3,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/random-sub-samples-generation-for-self#ran","syntology_url":"https://syntology.ai/paper/2307.16825","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16825"}},"official":{"repos":["p1y2z3/sdap"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/crystal-structure-prediction-by-joint-1","slug":"crystal-structure-prediction-by-joint-1","title":"Crystal Structure Prediction by Joint Equivariant Diffusion","date":"2023-07-30","arxiv_id":"2309.04475","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/crystal-structure-prediction-by-joint-1#ran","syntology_url":"https://syntology.ai/paper/2309.04475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.04475"}},"official":{"repos":["jiaor17/DiffCSP"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-format-consistency-for-instruction","slug":"exploring-format-consistency-for-instruction","title":"Exploring Format Consistency for Instruction Tuning","date":"2023-07-28","arxiv_id":"2307.15504","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-format-consistency-for-instruction#ran","syntology_url":"https://syntology.ai/paper/2307.15504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.15504"}},"official":{"repos":["thunlp/unifiedinstructiontuning"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-ai-for-medical-imaging-extending","slug":"generative-ai-for-medical-imaging-extending","title":"Generative AI for Medical Imaging: extending the MONAI Framework","date":"2023-07-27","arxiv_id":"2307.15208","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generative-ai-for-medical-imaging-extending#ran","syntology_url":"https://syntology.ai/paper/2307.15208","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.15208"}},"official":{"repos":["project-monai/generativemodels","warvito/generative_monai"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/interpolating-between-images-with-diffusion","slug":"interpolating-between-images-with-diffusion","title":"Interpolating between Images with Diffusion Models","date":"2023-07-24","arxiv_id":"2307.12560","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/interpolating-between-images-with-diffusion#ran","syntology_url":"https://syntology.ai/paper/2307.12560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.12560"}},"official":null}},{"url":"/paper/dpm-ot-a-new-diffusion-probabilistic-model","slug":"dpm-ot-a-new-diffusion-probabilistic-model","title":"DPM-OT: A New Diffusion Probabilistic Model Based on Optimal Transport","date":"2023-07-21","arxiv_id":"2307.11308","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":13,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dpm-ot-a-new-diffusion-probabilistic-model#ran","syntology_url":"https://syntology.ai/paper/2307.11308","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.11308"}},"official":{"repos":["cognaclee/dpm-ot"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/fractional-denoising-for-3d-molecular-pre","slug":"fractional-denoising-for-3d-molecular-pre","title":"Fractional Denoising for 3D Molecular Pre-training","date":"2023-07-20","arxiv_id":"2307.10683","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/fractional-denoising-for-3d-molecular-pre#ran","syntology_url":"https://syntology.ai/paper/2307.10683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10683"}},"official":{"repos":["fengshikun/frad"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/boxdiff-text-to-image-synthesis-with-training","slug":"boxdiff-text-to-image-synthesis-with-training","title":"BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion","date":"2023-07-20","arxiv_id":"2307.10816","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/boxdiff-text-to-image-synthesis-with-training#ran","syntology_url":"https://syntology.ai/paper/2307.10816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10816"}},"official":{"repos":["showlab/boxdiff","sierkinhane/boxdiff"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-prompt-model-for-weakly-supervised","slug":"generative-prompt-model-for-weakly-supervised","title":"Generative Prompt Model for Weakly Supervised Object Localization","date":"2023-07-19","arxiv_id":"2307.09756","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/generative-prompt-model-for-weakly-supervised#ran","syntology_url":"https://syntology.ai/paper/2307.09756","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09756"}},"official":{"repos":["callsys/genpromp"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/prediff-precipitation-nowcasting-with-latent-1","slug":"prediff-precipitation-nowcasting-with-latent-1","title":"PreDiff: Precipitation Nowcasting with Latent Diffusion Models","date":"2023-07-19","arxiv_id":"2307.10422","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":6,"n_instrument":3,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/prediff-precipitation-nowcasting-with-latent-1#ran","syntology_url":"https://syntology.ai/paper/2307.10422","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10422"}},"official":null}},{"url":"/paper/towards-authentic-face-restoration-with","slug":"towards-authentic-face-restoration-with","title":"Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond","date":"2023-07-18","arxiv_id":"2307.08996","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/towards-authentic-face-restoration-with#ran","syntology_url":"https://syntology.ai/paper/2307.08996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08996"}},"official":null}},{"url":"/paper/not-all-steps-are-created-equal-selective","slug":"not-all-steps-are-created-equal-selective","title":"Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation","date":"2023-07-17","arxiv_id":"2307.08448","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":1,"n_instrument":6,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/not-all-steps-are-created-equal-selective#ran","syntology_url":"https://syntology.ai/paper/2307.08448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08448"}},"official":{"repos":["andysonys/selective-diffusion-distillation"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/image-denoising-and-the-generative","slug":"image-denoising-and-the-generative","title":"Image Denoising and the Generative Accumulation of Photons","date":"2023-07-13","arxiv_id":"2307.06607","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/image-denoising-and-the-generative#ran","syntology_url":"https://syntology.ai/paper/2307.06607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06607"}},"official":{"repos":["krulllab/gap"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/geometric-neural-diffusion-processes-1","slug":"geometric-neural-diffusion-processes-1","title":"Geometric Neural Diffusion Processes","date":"2023-07-11","arxiv_id":"2307.05431","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/geometric-neural-diffusion-processes-1#ran","syntology_url":"https://syntology.ai/paper/2307.05431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05431"}},"official":{"repos":["cambridge-mlg/neural_diffusion_processes"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/metropolis-sampling-for-constrained-diffusion","slug":"metropolis-sampling-for-constrained-diffusion","title":"Metropolis Sampling for Constrained Diffusion Models","date":"2023-07-11","arxiv_id":"2307.05439","repositories_listed":0,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metropolis-sampling-for-constrained-diffusion#ran","syntology_url":"https://syntology.ai/paper/2307.05439","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05439"}},"official":null}},{"url":"/paper/deep-speech-synthesis-from-mri-based","slug":"deep-speech-synthesis-from-mri-based","title":"Deep Speech Synthesis from MRI-Based Articulatory Representations","date":"2023-07-05","arxiv_id":"2307.02471","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deep-speech-synthesis-from-mri-based#ran","syntology_url":"https://syntology.ai/paper/2307.02471","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.02471"}},"official":{"repos":["articulatory/articulatory"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/swingnn-rethinking-permutation-invariance-in","slug":"swingnn-rethinking-permutation-invariance-in","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","date":"2023-07-04","arxiv_id":"2307.01646","repositories_listed":2,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/swingnn-rethinking-permutation-invariance-in#ran","syntology_url":"https://syntology.ai/paper/2307.01646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01646"}},"official":{"repos":["qiyan98/swingnn"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/dit-3d-exploring-plain-diffusion-transformers-1","slug":"dit-3d-exploring-plain-diffusion-transformers-1","title":"DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation","date":"2023-07-04","arxiv_id":"2307.01831","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dit-3d-exploring-plain-diffusion-transformers-1#ran","syntology_url":"https://syntology.ai/paper/2307.01831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01831"}},"official":null}},{"url":"/paper/a-synthetic-electrocardiogram-ecg-image","slug":"a-synthetic-electrocardiogram-ecg-image","title":"ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization","date":"2023-07-04","arxiv_id":"2307.01946","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/a-synthetic-electrocardiogram-ecg-image#ran","syntology_url":"https://syntology.ai/paper/2307.01946","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01946"}},"official":{"repos":["alphanumericslab/ecg-image-kit"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/solving-linear-inverse-problems-provably-via-1","slug":"solving-linear-inverse-problems-provably-via-1","title":"Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models","date":"2023-07-02","arxiv_id":"2307.00619","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/solving-linear-inverse-problems-provably-via-1#ran","syntology_url":"https://syntology.ai/paper/2307.00619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00619"}},"official":{"repos":["liturout/psld"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/re-think-and-re-design-graph-neural-networks","slug":"re-think-and-re-design-graph-neural-networks","title":"Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals","date":"2023-07-01","arxiv_id":"2307.00222","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/re-think-and-re-design-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2307.00222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00222"}},"official":{"repos":["Dandy5721/GNN-PDE-COV"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/graph-denoising-diffusion-for-inverse-protein","slug":"graph-denoising-diffusion-for-inverse-protein","title":"Graph Denoising Diffusion for Inverse Protein Folding","date":"2023-06-29","arxiv_id":"2306.16819","repositories_listed":1,"syntology":{"n":21,"n_ran":18,"n_constructed":0,"n_ran_checked":15,"n_instrument":3,"n_unverified":3,"n_honours":2,"n_violates":2,"n_no_contract":11,"n_pointer_only":3,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 2 violated, 11 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/graph-denoising-diffusion-for-inverse-protein#ran","syntology_url":"https://syntology.ai/paper/2306.16819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16819"}},"official":{"repos":["ykiiiiii/grade_if"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dosediff-distance-aware-diffusion-model-for","slug":"dosediff-distance-aware-diffusion-model-for","title":"DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy","date":"2023-06-28","arxiv_id":"2306.16324","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":4,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dosediff-distance-aware-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2306.16324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16324"}},"official":{"repos":["whisney/dosediff"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/prores-exploring-degradation-aware-visual","slug":"prores-exploring-degradation-aware-visual","title":"ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration","date":"2023-06-23","arxiv_id":"2306.13653","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/prores-exploring-degradation-aware-visual#ran","syntology_url":"https://syntology.ai/paper/2306.13653","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13653"}},"official":{"repos":["leonmakise/prores"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semi-implicit-denoising-diffusion-models","slug":"semi-implicit-denoising-diffusion-models","title":"Semi-Implicit Denoising Diffusion Models (SIDDMs)","date":"2023-06-21","arxiv_id":"2306.12511","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semi-implicit-denoising-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2306.12511","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12511"}},"official":{"repos":["xuyanwu/SIDDMs-UFOGen"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/masked-diffusion-models-are-fast-learners","slug":"masked-diffusion-models-are-fast-learners","title":"Masked Diffusion Models Are Fast Distribution Learners","date":"2023-06-20","arxiv_id":"2306.11363","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":2,"n_no_contract":5,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/masked-diffusion-models-are-fast-learners#ran","syntology_url":"https://syntology.ai/paper/2306.11363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11363"}},"official":{"repos":["jiachenlei/maskdm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-with-forward-models-solving","slug":"diffusion-with-forward-models-solving","title":"Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision","date":"2023-06-20","arxiv_id":"2306.11719","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffusion-with-forward-models-solving#ran","syntology_url":"https://syntology.ai/paper/2306.11719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11719"}},"official":null}},{"url":"/paper/fast-training-of-diffusion-models-with-masked","slug":"fast-training-of-diffusion-models-with-masked","title":"Fast Training of Diffusion Models with Masked Transformers","date":"2023-06-15","arxiv_id":"2306.09305","repositories_listed":1,"syntology":{"n":17,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fast-training-of-diffusion-models-with-masked#ran","syntology_url":"https://syntology.ai/paper/2306.09305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.09305"}},"official":{"repos":["anima-lab/maskdit"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/3d-molecule-generation-by-denoising-voxel","slug":"3d-molecule-generation-by-denoising-voxel","title":"3D molecule generation by denoising voxel grids","date":"2023-06-13","arxiv_id":"2306.07473","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/3d-molecule-generation-by-denoising-voxel#ran","syntology_url":"https://syntology.ai/paper/2306.07473","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07473"}},"official":{"repos":["genentech/voxmol"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/villandiffusion-a-unified-backdoor-attack-1","slug":"villandiffusion-a-unified-backdoor-attack-1","title":"VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models","date":"2023-06-12","arxiv_id":"2306.06874","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":5,"n_honours":1,"n_violates":2,"n_no_contract":4,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 2 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/villandiffusion-a-unified-backdoor-attack-1#ran","syntology_url":"https://syntology.ai/paper/2306.06874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06874"}},"official":{"repos":["ibm/villandiffusion"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-surface-statistics-scene","slug":"beyond-surface-statistics-scene","title":"Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model","date":"2023-06-09","arxiv_id":"2306.05720","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/beyond-surface-statistics-scene#ran","syntology_url":"https://syntology.ai/paper/2306.05720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05720"}},"official":{"repos":["yc015/scene-representation-diffusion-model"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/interpreting-and-improving-diffusion-models","slug":"interpreting-and-improving-diffusion-models","title":"Interpreting and Improving Diffusion Models from an Optimization Perspective","date":"2023-06-08","arxiv_id":"2306.04848","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":2,"n_ran_checked":9,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"12 ran (of which 2 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/interpreting-and-improving-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2306.04848","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04848"}},"official":{"repos":["toyotaresearchinstitute/gradient-estimation-sampler"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":2,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/addp-learning-general-representations-for","slug":"addp-learning-general-representations-for","title":"ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process","date":"2023-06-08","arxiv_id":"2306.05423","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/addp-learning-general-representations-for#ran","syntology_url":"https://syntology.ai/paper/2306.05423","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05423"}},"official":{"repos":["changyaotian/addp"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/protein-discovery-with-discrete-walk-jump","slug":"protein-discovery-with-discrete-walk-jump","title":"Protein Discovery with Discrete Walk-Jump Sampling","date":"2023-06-08","arxiv_id":"2306.12360","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/protein-discovery-with-discrete-walk-jump#ran","syntology_url":"https://syntology.ai/paper/2306.12360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.12360"}},"official":{"repos":["prescient-design/walk-jump"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/dformer-diffusion-guided-transformer-for","slug":"dformer-diffusion-guided-transformer-for","title":"DFormer: Diffusion-guided Transformer for Universal Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03437","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dformer-diffusion-guided-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2306.03437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03437"}},"official":{"repos":["cp3wan/dformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/score-based-enhanced-sampling-for-protein","slug":"score-based-enhanced-sampling-for-protein","title":"Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling","date":"2023-06-05","arxiv_id":"2306.03117","repositories_listed":1,"syntology":{"n":24,"n_ran":22,"n_constructed":0,"n_ran_checked":20,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":20,"n_pointer_only":3,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 0 violated, 20 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/score-based-enhanced-sampling-for-protein#ran","syntology_url":"https://syntology.ai/paper/2306.03117","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03117"}},"official":{"repos":["lujiarui/str2str"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":20,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-autoencoders-as-watermark","slug":"generative-autoencoders-as-watermark","title":"Invisible Image Watermarks Are Provably Removable Using Generative AI","date":"2023-06-02","arxiv_id":"2306.01953","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generative-autoencoders-as-watermark#ran","syntology_url":"https://syntology.ai/paper/2306.01953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01953"}},"official":{"repos":["xuandongzhao/watermarkattacker"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/low-light-image-enhancement-with-wavelet","slug":"low-light-image-enhancement-with-wavelet","title":"Low-Light Image Enhancement with Wavelet-based Diffusion Models","date":"2023-06-01","arxiv_id":"2306.00306","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":8,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/low-light-image-enhancement-with-wavelet#ran","syntology_url":"https://syntology.ai/paper/2306.00306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00306"}},"official":{"repos":["JianghaiSCU/Diffusion-Low-Light"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/addressing-negative-transfer-in-diffusion-1","slug":"addressing-negative-transfer-in-diffusion-1","title":"Addressing Negative Transfer in Diffusion Models","date":"2023-06-01","arxiv_id":"2306.00354","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/addressing-negative-transfer-in-diffusion-1#ran","syntology_url":"https://syntology.ai/paper/2306.00354","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00354"}},"official":{"repos":["gohyojun15/ANT_diffusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffpack-a-torsional-diffusion-model-for","slug":"diffpack-a-torsional-diffusion-model-for","title":"DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing","date":"2023-06-01","arxiv_id":"2306.01794","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/diffpack-a-torsional-diffusion-model-for#ran","syntology_url":"https://syntology.ai/paper/2306.01794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01794"}},"official":{"repos":["deepgraphlearning/diffpack"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/synthetic-ct-generation-from-mri-using-3d","slug":"synthetic-ct-generation-from-mri-using-3d","title":"Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model","date":"2023-05-31","arxiv_id":"2305.19467","repositories_listed":1,"syntology":{"n":14,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/synthetic-ct-generation-from-mri-using-3d#ran","syntology_url":"https://syntology.ai/paper/2305.19467","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19467"}},"official":{"repos":["shaoyanpan/Synthetic-CT-generation-from-MRI-using-3D-transformer-based-denoising-diffusion-model"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/protein-design-with-guided-discrete-diffusion-1","slug":"protein-design-with-guided-discrete-diffusion-1","title":"Protein Design with Guided Discrete Diffusion","date":"2023-05-31","arxiv_id":"2305.20009","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 1 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/protein-design-with-guided-discrete-diffusion-1#ran","syntology_url":"https://syntology.ai/paper/2305.20009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.20009"}},"official":{"repos":["ngruver/nos"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hifa-high-fidelity-text-to-3d-with-advanced","slug":"hifa-high-fidelity-text-to-3d-with-advanced","title":"HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance","date":"2023-05-30","arxiv_id":"2305.18766","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hifa-high-fidelity-text-to-3d-with-advanced#ran","syntology_url":"https://syntology.ai/paper/2305.18766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18766"}},"official":{"repos":["JunzheJosephZhu/HiFA"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/which-models-have-perceptually-aligned","slug":"which-models-have-perceptually-aligned","title":"Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness","date":"2023-05-30","arxiv_id":"2305.19101","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/which-models-have-perceptually-aligned#ran","syntology_url":"https://syntology.ai/paper/2305.19101","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19101"}},"official":{"repos":["tml-tuebingen/pags"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/implicit-transfer-operator-learning-multiple","slug":"implicit-transfer-operator-learning-multiple","title":"Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics","date":"2023-05-29","arxiv_id":"2305.18046","repositories_listed":0,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/implicit-transfer-operator-learning-multiple#ran","syntology_url":"https://syntology.ai/paper/2305.18046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18046"}},"official":null}},{"url":"/paper/gen-l-video-multi-text-to-long-video","slug":"gen-l-video-multi-text-to-long-video","title":"Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising","date":"2023-05-29","arxiv_id":"2305.18264","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/gen-l-video-multi-text-to-long-video#ran","syntology_url":"https://syntology.ai/paper/2305.18264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18264"}},"official":{"repos":["g-u-n/gen-l-video"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/make-an-audio-2-temporal-enhanced-text-to","slug":"make-an-audio-2-temporal-enhanced-text-to","title":"Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation","date":"2023-05-29","arxiv_id":"2305.18474","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":4,"n_pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/make-an-audio-2-temporal-enhanced-text-to#ran","syntology_url":"https://syntology.ai/paper/2305.18474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18474"}},"official":null}},{"url":"/paper/on-diffusion-modeling-for-anomaly-detection","slug":"on-diffusion-modeling-for-anomaly-detection","title":"On Diffusion Modeling for Anomaly Detection","date":"2023-05-29","arxiv_id":"2305.18593","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-diffusion-modeling-for-anomaly-detection#ran","syntology_url":"https://syntology.ai/paper/2305.18593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18593"}},"official":{"repos":["vicliv/dte"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/conditional-score-based-diffusion-models-for-2","slug":"conditional-score-based-diffusion-models-for-2","title":"Conditional score-based diffusion models for Bayesian inference in infinite dimensions","date":"2023-05-28","arxiv_id":"2305.19147","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/conditional-score-based-diffusion-models-for-2#ran","syntology_url":"https://syntology.ai/paper/2305.19147","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19147"}},"official":{"repos":["alisiahkoohi/csgm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/confronting-ambiguity-in-6d-object-pose","slug":"confronting-ambiguity-in-6d-object-pose","title":"Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)","date":"2023-05-25","arxiv_id":"2305.15873","repositories_listed":1,"syntology":{"n":23,"n_ran":17,"n_constructed":0,"n_ran_checked":17,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/confronting-ambiguity-in-6d-object-pose#ran","syntology_url":"https://syntology.ai/paper/2305.15873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15873"}},"official":{"repos":["Ending2015a/liepose-diffusion"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/udpm-upsampling-diffusion-probabilistic","slug":"udpm-upsampling-diffusion-probabilistic","title":"UDPM: Upsampling Diffusion Probabilistic Models","date":"2023-05-25","arxiv_id":"2305.16269","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/udpm-upsampling-diffusion-probabilistic#ran","syntology_url":"https://syntology.ai/paper/2305.16269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16269"}},"official":{"repos":["shadyabh/udpm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/are-diffusion-models-vision-and-language-1","slug":"are-diffusion-models-vision-and-language-1","title":"Are Diffusion Models Vision-And-Language Reasoners?","date":"2023-05-25","arxiv_id":"2305.16397","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/are-diffusion-models-vision-and-language-1#ran","syntology_url":"https://syntology.ai/paper/2305.16397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16397"}},"official":{"repos":["mcgill-nlp/diffusion-itm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sin3dm-learning-a-diffusion-model-from-a","slug":"sin3dm-learning-a-diffusion-model-from-a","title":"Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape","date":"2023-05-24","arxiv_id":"2305.15399","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":2,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 2 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sin3dm-learning-a-diffusion-model-from-a#ran","syntology_url":"https://syntology.ai/paper/2305.15399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15399"}},"official":{"repos":["sin3dm/sin3dm"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":2,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/mp-senet-a-speech-enhancement-model-with","slug":"mp-senet-a-speech-enhancement-model-with","title":"MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra","date":"2023-05-23","arxiv_id":"2305.13686","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mp-senet-a-speech-enhancement-model-with#ran","syntology_url":"https://syntology.ai/paper/2305.13686","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13686"}},"official":{"repos":["yxlu-0102/MP-SENet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/training-diffusion-models-with-reinforcement","slug":"training-diffusion-models-with-reinforcement","title":"Training Diffusion Models with Reinforcement Learning","date":"2023-05-22","arxiv_id":"2305.13301","repositories_listed":3,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/training-diffusion-models-with-reinforcement#ran","syntology_url":"https://syntology.ai/paper/2305.13301","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13301"}},"official":{"repos":["kvablack/ddpo-pytorch"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/moment-matching-denoising-gibbs-sampling","slug":"moment-matching-denoising-gibbs-sampling","title":"Moment Matching Denoising Gibbs Sampling","date":"2023-05-19","arxiv_id":"2305.11650","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/moment-matching-denoising-gibbs-sampling#ran","syntology_url":"https://syntology.ai/paper/2305.11650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11650"}},"official":{"repos":["zmtomorrow/mmdgs_neurips"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/information-screening-whilst-exploiting","slug":"information-screening-whilst-exploiting","title":"Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling","date":"2023-05-19","arxiv_id":"2305.11719","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/information-screening-whilst-exploiting#ran","syntology_url":"https://syntology.ai/paper/2305.11719","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11719"}},"official":{"repos":["chocowu/mre-ise"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/ptqd-accurate-post-training-quantization-for-1","slug":"ptqd-accurate-post-training-quantization-for-1","title":"PTQD: Accurate Post-Training Quantization for Diffusion Models","date":"2023-05-18","arxiv_id":"2305.10657","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ptqd-accurate-post-training-quantization-for-1#ran","syntology_url":"https://syntology.ai/paper/2305.10657","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10657"}},"official":{"repos":["ziplab/ptqd"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/one-peace-exploring-one-general","slug":"one-peace-exploring-one-general","title":"ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities","date":"2023-05-18","arxiv_id":"2305.11172","repositories_listed":2,"syntology":{"n":7,"n_ran":5,"n_constructed":2,"n_ran_checked":4,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/one-peace-exploring-one-general#ran","syntology_url":"https://syntology.ai/paper/2305.11172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11172"}},"official":{"repos":["OFA-Sys/ONE-PEACE"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pyramid-diffusion-models-for-low-light-image","slug":"pyramid-diffusion-models-for-low-light-image","title":"Pyramid Diffusion Models For Low-light Image Enhancement","date":"2023-05-17","arxiv_id":"2305.10028","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pyramid-diffusion-models-for-low-light-image#ran","syntology_url":"https://syntology.ai/paper/2305.10028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10028"}},"official":{"repos":["limuloo/pydiff"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fastcomposer-tuning-free-multi-subject-image","slug":"fastcomposer-tuning-free-multi-subject-image","title":"FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention","date":"2023-05-17","arxiv_id":"2305.10431","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":7,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fastcomposer-tuning-free-multi-subject-image#ran","syntology_url":"https://syntology.ai/paper/2305.10431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10431"}},"official":{"repos":["mit-han-lab/fastcomposer"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/discrete-diffusion-probabilistic-models-for","slug":"discrete-diffusion-probabilistic-models-for","title":"Discrete Diffusion Probabilistic Models for Symbolic Music Generation","date":"2023-05-16","arxiv_id":"2305.09489","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/discrete-diffusion-probabilistic-models-for#ran","syntology_url":"https://syntology.ai/paper/2305.09489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.09489"}},"official":{"repos":["plassma/symbolic-music-discrete-diffusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/denoising-diffusion-models-for-plug-and-play","slug":"denoising-diffusion-models-for-plug-and-play","title":"Denoising Diffusion Models for Plug-and-Play Image Restoration","date":"2023-05-15","arxiv_id":"2305.08995","repositories_listed":3,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":11,"n_instrument":4,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 3 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/denoising-diffusion-models-for-plug-and-play#ran","syntology_url":"https://syntology.ai/paper/2305.08995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08995"}},"official":{"repos":["yuanzhi-zhu/diffpir"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/comospeech-one-step-speech-and-singing-voice","slug":"comospeech-one-step-speech-and-singing-voice","title":"CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model","date":"2023-05-11","arxiv_id":"2305.06908","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/comospeech-one-step-speech-and-singing-voice#ran","syntology_url":"https://syntology.ai/paper/2305.06908","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06908"}},"official":{"repos":["zhenye234/CoMoSpeech"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-and-degree-guided-graph-generation","slug":"efficient-and-degree-guided-graph-generation","title":"Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling","date":"2023-05-06","arxiv_id":"2305.04111","repositories_listed":1,"syntology":{"n":15,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":11,"n_pointer_only":4,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-and-degree-guided-graph-generation#ran","syntology_url":"https://syntology.ai/paper/2305.04111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04111"}},"official":{"repos":["tufts-ml/graph-generation-edge"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/guided-image-synthesis-via-initial-image","slug":"guided-image-synthesis-via-initial-image","title":"Guided Image Synthesis via Initial Image Editing in Diffusion Model","date":"2023-05-05","arxiv_id":"2305.03382","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/guided-image-synthesis-via-initial-image#ran","syntology_url":"https://syntology.ai/paper/2305.03382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03382"}},"official":{"repos":["UT-Mao/Initial-Noise-Editing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/difftraj-generating-gps-trajectory-with-1","slug":"difftraj-generating-gps-trajectory-with-1","title":"DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model","date":"2023-04-23","arxiv_id":"2304.11582","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/difftraj-generating-gps-trajectory-with-1#ran","syntology_url":"https://syntology.ai/paper/2304.11582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.11582"}},"official":{"repos":["Yasoz/DiffTraj"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/revisiting-implicit-neural-representations-in","slug":"revisiting-implicit-neural-representations-in","title":"Revisiting Implicit Neural Representations in Low-Level Vision","date":"2023-04-20","arxiv_id":"2304.10250","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/revisiting-implicit-neural-representations-in#ran","syntology_url":"https://syntology.ai/paper/2304.10250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.10250"}},"official":{"repos":["wentxul/linr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-image-denoising-with","slug":"self-supervised-image-denoising-with","title":"Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network","date":"2023-04-19","arxiv_id":"2304.09507","repositories_listed":0,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/self-supervised-image-denoising-with#ran","syntology_url":"https://syntology.ai/paper/2304.09507","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.09507"}},"official":null}},{"url":"/paper/ovtrack-open-vocabulary-multiple-object","slug":"ovtrack-open-vocabulary-multiple-object","title":"OVTrack: Open-Vocabulary Multiple Object Tracking","date":"2023-04-17","arxiv_id":"2304.08408","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ovtrack-open-vocabulary-multiple-object#ran","syntology_url":"https://syntology.ai/paper/2304.08408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.08408"}},"official":null}},{"url":"/paper/an-edit-friendly-ddpm-noise-space-inversion","slug":"an-edit-friendly-ddpm-noise-space-inversion","title":"An Edit Friendly DDPM Noise Space: Inversion and Manipulations","date":"2023-04-12","arxiv_id":"2304.06140","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-edit-friendly-ddpm-noise-space-inversion#ran","syntology_url":"https://syntology.ai/paper/2304.06140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06140"}},"official":{"repos":["inbarhub/ddpm_inversion"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/a-cheaper-and-better-diffusion-language-model","slug":"a-cheaper-and-better-diffusion-language-model","title":"A Cheaper and Better Diffusion Language Model with Soft-Masked Noise","date":"2023-04-10","arxiv_id":"2304.04746","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-cheaper-and-better-diffusion-language-model#ran","syntology_url":"https://syntology.ai/paper/2304.04746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04746"}},"official":{"repos":["amazon-science/masked-diffusion-lm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/humansd-a-native-skeleton-guided-diffusion","slug":"humansd-a-native-skeleton-guided-diffusion","title":"HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation","date":"2023-04-09","arxiv_id":"2304.04269","repositories_listed":3,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":4,"n_instrument":7,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 7 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/humansd-a-native-skeleton-guided-diffusion#ran","syntology_url":"https://syntology.ai/paper/2304.04269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04269"}},"official":{"repos":["IDEA-Research/HumanSD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}}],"record_sha256":"cea876f1b2a6be1fd86031ff8a995568eeb61f512e6b6fcb84a641747780ce1d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}