{"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/prediction/papers/14","list_of":"/task/prediction","task":"Prediction","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":14,"pages_in_order":88,"rows_per_page":100,"rows":[1301,1400],"of":8760,"counts":{"archive_papers_tagged":8760,"with_a_code_link":2835,"where_syntology_ran_a_sample":607,"not_listed_spam_title":0,"listed":8760,"listed_where_code_ran":607,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":519,"every_run_a_failure_of_syntologys_instrument":88,"listed_with_a_run_with_no_instrument_failure":519,"listed_every_run_a_failure_of_syntologys_instrument":88,"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/prediction","prev":"/task/prediction/papers/13","next":"/task/prediction/papers/15","papers":[{"url":"/paper/crepe-learnable-prompting-with-clip-improves","slug":"crepe-learnable-prompting-with-clip-improves","title":"CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction","date":"2023-07-10","arxiv_id":"2307.04838","repositories_listed":1,"syntology":null},{"url":"/paper/merging-diverging-hybrid-transformer-networks","slug":"merging-diverging-hybrid-transformer-networks","title":"Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer","date":"2023-07-07","arxiv_id":"2307.03427","repositories_listed":1,"syntology":null},{"url":"/paper/trac-trustworthy-retrieval-augmented-chatbot","slug":"trac-trustworthy-retrieval-augmented-chatbot","title":"TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction","date":"2023-07-07","arxiv_id":"2307.04642","repositories_listed":1,"syntology":null},{"url":"/paper/when-no-rejection-learning-is-optimal-for","slug":"when-no-rejection-learning-is-optimal-for","title":"When No-Rejection Learning is Consistent for Regression with Rejection","date":"2023-07-06","arxiv_id":"2307.02932","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/when-no-rejection-learning-is-optimal-for#ran","syntology_url":"https://syntology.ai/paper/2307.02932","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.02932"}},"official":{"repos":["hanzhao-wang/rwr"],"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/emoji-prediction-using-transformer-models","slug":"emoji-prediction-using-transformer-models","title":"Emoji Prediction in Tweets using BERT","date":"2023-07-05","arxiv_id":"2307.02054","repositories_listed":1,"syntology":null},{"url":"/paper/overconfidence-is-a-dangerous-thing","slug":"overconfidence-is-a-dangerous-thing","title":"Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction","date":"2023-07-04","arxiv_id":"2307.01610","repositories_listed":1,"syntology":null},{"url":"/paper/depth-video-data-enabled-predictions-of","slug":"depth-video-data-enabled-predictions-of","title":"Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms","date":"2023-07-03","arxiv_id":"2307.01383","repositories_listed":1,"syntology":null},{"url":"/paper/assembled-openml-creating-efficient","slug":"assembled-openml-creating-efficient","title":"Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML","date":"2023-07-01","arxiv_id":"2307.00285","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/assembled-openml-creating-efficient#ran","syntology_url":"https://syntology.ai/paper/2307.00285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.00285"}},"official":{"repos":["isg-siegen/assembled"],"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/generative-data-augmentation-for-aspect","slug":"generative-data-augmentation-for-aspect","title":"Generative Data Augmentation for Aspect Sentiment Quad Prediction","date":"2023-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/mtr-multi-agent-motion-prediction-with","slug":"mtr-multi-agent-motion-prediction-with","title":"MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying","date":"2023-06-30","arxiv_id":"2306.17770","repositories_listed":1,"syntology":null},{"url":"/paper/rdsoba-rendered-shadow-object-association","slug":"rdsoba-rendered-shadow-object-association","title":"Shadow Generation with Decomposed Mask Prediction and Attentive Shadow Filling","date":"2023-06-30","arxiv_id":"2306.17358","repositories_listed":1,"syntology":null},{"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/conformal-link-prediction-to-control-the","slug":"conformal-link-prediction-to-control-the","title":"Conformal link prediction for false discovery rate control","date":"2023-06-26","arxiv_id":"2306.14693","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/conformal-link-prediction-to-control-the#ran","syntology_url":"https://syntology.ai/paper/2306.14693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14693"}},"official":{"repos":["arianemarandon/linkpredconf"],"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/towards-few-shot-inductive-link-prediction-on","slug":"towards-few-shot-inductive-link-prediction-on","title":"Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach","date":"2023-06-26","arxiv_id":"2307.01204","repositories_listed":1,"syntology":null},{"url":"/paper/community-aware-transformer-for-autism","slug":"community-aware-transformer-for-autism","title":"Community-Aware Transformer for Autism Prediction in fMRI Connectome","date":"2023-06-24","arxiv_id":"2307.10181","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"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) · 4 unverified","sample_list":"/paper/community-aware-transformer-for-autism#ran","syntology_url":"https://syntology.ai/paper/2307.10181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10181"}},"official":{"repos":["ubc-tea/com-braintf"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/curvature-enhanced-graph-convolutional","slug":"curvature-enhanced-graph-convolutional","title":"Curvature-enhanced Graph Convolutional Network for Biomolecular Interaction Prediction","date":"2023-06-23","arxiv_id":"2306.13699","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-efficiently-adapt-large-segmentation","slug":"how-to-efficiently-adapt-large-segmentation","title":"How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images","date":"2023-06-23","arxiv_id":"2306.13731","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":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) · 1 unverified","sample_list":"/paper/how-to-efficiently-adapt-large-segmentation#ran","syntology_url":"https://syntology.ai/paper/2306.13731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.13731"}},"official":{"repos":["xhu248/autosam"],"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/the-mi-motion-dataset-and-benchmark-for-3d","slug":"the-mi-motion-dataset-and-benchmark-for-3d","title":"The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction","date":"2023-06-23","arxiv_id":"2306.13566","repositories_listed":1,"syntology":null},{"url":"/paper/valid-inference-after-prediction","slug":"valid-inference-after-prediction","title":"Revisiting inference after prediction","date":"2023-06-23","arxiv_id":"2306.13746","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-representations-for-relation","slug":"explainable-representations-for-relation","title":"Explainable Representations for Relation Prediction in Knowledge Graphs","date":"2023-06-22","arxiv_id":"2306.12687","repositories_listed":1,"syntology":null},{"url":"/paper/otter-knowledge-benchmarks-of-multimodal","slug":"otter-knowledge-benchmarks-of-multimodal","title":"Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery","date":"2023-06-22","arxiv_id":"2306.12802","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-adversarial-robustness-of","slug":"evaluating-adversarial-robustness-of","title":"Physics-constrained Attack against Convolution-based Human Motion Prediction","date":"2023-06-21","arxiv_id":"2306.11990","repositories_listed":1,"syntology":null},{"url":"/paper/improving-long-horizon-imitation-through-1","slug":"improving-long-horizon-imitation-through-1","title":"Improving Long-Horizon Imitation Through Instruction Prediction","date":"2023-06-21","arxiv_id":"2306.12554","repositories_listed":1,"syntology":null},{"url":"/paper/neural-multigrid-memory-for-computational","slug":"neural-multigrid-memory-for-computational","title":"Neural Multigrid Memory For Computational Fluid Dynamics","date":"2023-06-21","arxiv_id":"2306.12545","repositories_listed":1,"syntology":null},{"url":"/paper/skygpt-probabilistic-short-term-solar","slug":"skygpt-probabilistic-short-term-solar","title":"SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT","date":"2023-06-20","arxiv_id":"2306.11682","repositories_listed":1,"syntology":null},{"url":"/paper/a-lightweight-causal-model-for-interpretable","slug":"a-lightweight-causal-model-for-interpretable","title":"A Lightweight Generative Model for Interpretable Subject-level Prediction","date":"2023-06-19","arxiv_id":"2306.11107","repositories_listed":1,"syntology":null},{"url":"/paper/forest-parameter-prediction-by-multiobjective","slug":"forest-parameter-prediction-by-multiobjective","title":"Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation","date":"2023-06-19","arxiv_id":"2306.11103","repositories_listed":1,"syntology":null},{"url":"/paper/powerbev-a-powerful-yet-lightweight-framework","slug":"powerbev-a-powerful-yet-lightweight-framework","title":"PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View","date":"2023-06-19","arxiv_id":"2306.10761","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-gru-network-for-seasonal","slug":"convolutional-gru-network-for-seasonal","title":"Convolutional GRU Network for Seasonal Prediction of the El Niño-Southern Oscillation","date":"2023-06-18","arxiv_id":"2306.10443","repositories_listed":1,"syntology":null},{"url":"/paper/qcnext-a-next-generation-framework-for-joint","slug":"qcnext-a-next-generation-framework-for-joint","title":"QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction","date":"2023-06-18","arxiv_id":"2306.10508","repositories_listed":1,"syntology":null},{"url":"/paper/fast-fourier-inception-networks-for-occluded","slug":"fast-fourier-inception-networks-for-occluded","title":"Fast Fourier Inception Networks for Occluded Video Prediction","date":"2023-06-17","arxiv_id":"2306.10346","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-speech-representations-for-mos","slug":"evaluation-of-speech-representations-for-mos","title":"Evaluation of Speech Representations for MOS prediction","date":"2023-06-16","arxiv_id":"2306.09979","repositories_listed":1,"syntology":null},{"url":"/paper/class-conditional-conformal-prediction-with-1","slug":"class-conditional-conformal-prediction-with-1","title":"Class-Conditional Conformal Prediction with Many Classes","date":"2023-06-15","arxiv_id":"2306.09335","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-shift-inversion-for-out-of-1","slug":"distribution-shift-inversion-for-out-of-1","title":"Distribution Shift Inversion for Out-of-Distribution Prediction","date":"2023-06-14","arxiv_id":"2306.08328","repositories_listed":1,"syntology":{"n":16,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 8 unverified","sample_list":"/paper/distribution-shift-inversion-for-out-of-1#ran","syntology_url":"https://syntology.ai/paper/2306.08328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08328"}},"official":{"repos":["yu-rp/distribution-shift-iverson"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-learning-based-vehicle-trajectory","slug":"federated-learning-based-vehicle-trajectory","title":"Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks","date":"2023-06-14","arxiv_id":"2306.08566","repositories_listed":1,"syntology":null},{"url":"/paper/automated-3d-pre-training-for-molecular","slug":"automated-3d-pre-training-for-molecular","title":"Automated 3D Pre-Training for Molecular Property Prediction","date":"2023-06-13","arxiv_id":"2306.07812","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"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) · 6 unverified","sample_list":"/paper/automated-3d-pre-training-for-molecular#ran","syntology_url":"https://syntology.ai/paper/2306.07812","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07812"}},"official":{"repos":["lars-research/3d-pgt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamic-causal-graph-convolutional-network","slug":"dynamic-causal-graph-convolutional-network","title":"Dynamic Causal Graph Convolutional Network for Traffic Prediction","date":"2023-06-12","arxiv_id":"2306.07019","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-expected-size-of-conformal-prediction","slug":"on-the-expected-size-of-conformal-prediction","title":"On the Expected Size of Conformal Prediction Sets","date":"2023-06-12","arxiv_id":"2306.07254","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-expected-size-of-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2306.07254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07254"}},"official":{"repos":["guneet-dhillon/expected-conformal-prediction-set-size"],"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/quert-continual-pre-training-of-language","slug":"quert-continual-pre-training-of-language","title":"QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search","date":"2023-06-11","arxiv_id":"2306.06707","repositories_listed":1,"syntology":null},{"url":"/paper/crysmmnet-multimodal-representation-for","slug":"crysmmnet-multimodal-representation-for","title":"CrysMMNet: Multimodal Representation for Crystal Property Prediction","date":"2023-06-09","arxiv_id":"2307.05390","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/crysmmnet-multimodal-representation-for#ran","syntology_url":"https://syntology.ai/paper/2307.05390","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.05390"}},"official":{"repos":["kdmsit/crysmmnet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/feature-programming-for-multivariate-time","slug":"feature-programming-for-multivariate-time","title":"Feature Programming for Multivariate Time Series Prediction","date":"2023-06-09","arxiv_id":"2306.06252","repositories_listed":1,"syntology":null},{"url":"/paper/transformer-based-time-to-event-prediction","slug":"transformer-based-time-to-event-prediction","title":"Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration","date":"2023-06-09","arxiv_id":"2306.05779","repositories_listed":1,"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":4,"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/transformer-based-time-to-event-prediction#ran","syntology_url":"https://syntology.ai/paper/2306.05779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05779"}},"official":{"repos":["dviraran/strafe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/conservative-prediction-via-data-driven","slug":"conservative-prediction-via-data-driven","title":"Conservative Prediction via Data-Driven Confidence Minimization","date":"2023-06-08","arxiv_id":"2306.04974","repositories_listed":1,"syntology":null},{"url":"/paper/genomic-interpreter-a-hierarchical-genomic","slug":"genomic-interpreter-a-hierarchical-genomic","title":"Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer","date":"2023-06-08","arxiv_id":"2306.05143","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/genomic-interpreter-a-hierarchical-genomic#ran","syntology_url":"https://syntology.ai/paper/2306.05143","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05143"}},"official":{"repos":["zehui127/1d-swin"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-bioassay-pre-training-for-protein","slug":"multi-task-bioassay-pre-training-for-protein","title":"Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction","date":"2023-06-08","arxiv_id":"2306.04886","repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-prediction-with-observations-of","slug":"trajectory-prediction-with-observations-of","title":"Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios","date":"2023-06-08","arxiv_id":"2306.05478","repositories_listed":1,"syntology":null},{"url":"/paper/bioblp-a-modular-framework-for-learning-on","slug":"bioblp-a-modular-framework-for-learning-on","title":"BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs","date":"2023-06-06","arxiv_id":"2306.03606","repositories_listed":1,"syntology":null},{"url":"/paper/designing-decision-support-systems-using","slug":"designing-decision-support-systems-using","title":"Designing Decision Support Systems Using Counterfactual Prediction Sets","date":"2023-06-06","arxiv_id":"2306.03928","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"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 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) · 0 unverified","sample_list":"/paper/designing-decision-support-systems-using#ran","syntology_url":"https://syntology.ai/paper/2306.03928","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03928"}},"official":{"repos":["networks-learning/counterfactual-prediction-sets"],"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/mutual-information-regularization-for-weakly","slug":"mutual-information-regularization-for-weakly","title":"Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection","date":"2023-06-06","arxiv_id":"2306.03630","repositories_listed":1,"syntology":null},{"url":"/paper/transition-role-of-entangled-data-in-quantum","slug":"transition-role-of-entangled-data-in-quantum","title":"Transition Role of Entangled Data in Quantum Machine Learning","date":"2023-06-06","arxiv_id":"2306.03481","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-with-missing-values","slug":"conformal-prediction-with-missing-values","title":"Conformal Prediction with Missing Values","date":"2023-06-05","arxiv_id":"2306.02732","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conformal-prediction-with-missing-values#ran","syntology_url":"https://syntology.ai/paper/2306.02732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02732"}},"official":{"repos":["mzaffran/conformalpredictionmissingvalues"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/video-diffusion-models-with-local-global","slug":"video-diffusion-models-with-local-global","title":"Video Diffusion Models with Local-Global Context Guidance","date":"2023-06-05","arxiv_id":"2306.02562","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":3,"n_no_contract":11,"n_pointer_only":3,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 2 honoured, 3 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/video-diffusion-models-with-local-global#ran","syntology_url":"https://syntology.ai/paper/2306.02562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02562"}},"official":{"repos":["exisas/lgc-vd"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/one-for-all-unified-workload-prediction-for","slug":"one-for-all-unified-workload-prediction-for","title":"One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms","date":"2023-06-02","arxiv_id":"2306.01507","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-with-partially-labeled","slug":"conformal-prediction-with-partially-labeled","title":"Conformal Prediction with Partially Labeled Data","date":"2023-06-01","arxiv_id":"2306.01191","repositories_listed":1,"syntology":null},{"url":"/paper/learning-spatial-temporal-consistency-for","slug":"learning-spatial-temporal-consistency-for","title":"Learning spatial- temporal consistency for satellite image sequence prediction","date":"2023-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/predicting-temporal-aspects-of-movement-for","slug":"predicting-temporal-aspects-of-movement-for","title":"Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments","date":"2023-06-01","arxiv_id":"2306.00575","repositories_listed":1,"syntology":null},{"url":"/paper/permutation-aware-action-segmentation-via","slug":"permutation-aware-action-segmentation-via","title":"Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignment","date":"2023-05-31","arxiv_id":"2305.19478","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-error-based-classification-for","slug":"prediction-error-based-classification-for","title":"Prediction Error-based Classification for Class-Incremental Learning","date":"2023-05-30","arxiv_id":"2305.18806","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":5,"n_ran_checked":5,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"8 ran (of which 5 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) · 3 unverified","sample_list":"/paper/prediction-error-based-classification-for#ran","syntology_url":"https://syntology.ai/paper/2305.18806","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18806"}},"official":{"repos":["michalzajac-ml/pec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/table-detection-for-visually-rich-document","slug":"table-detection-for-visually-rich-document","title":"Table Detection for Visually Rich Document Images","date":"2023-05-30","arxiv_id":"2305.19181","repositories_listed":1,"syntology":null},{"url":"/paper/chatgpt-informed-graph-neural-network-for","slug":"chatgpt-informed-graph-neural-network-for","title":"ChatGPT Informed Graph Neural Network for Stock Movement Prediction","date":"2023-05-28","arxiv_id":"2306.03763","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-with-large-language","slug":"conformal-prediction-with-large-language","title":"Conformal Prediction with Large Language Models for Multi-Choice Question Answering","date":"2023-05-28","arxiv_id":"2305.18404","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":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) · 2 unverified","sample_list":"/paper/conformal-prediction-with-large-language#ran","syntology_url":"https://syntology.ai/paper/2305.18404","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18404"}},"official":{"repos":["bhaweshiitk/conformalllm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/incentivizing-honest-performative-predictions","slug":"incentivizing-honest-performative-predictions","title":"Incentivizing honest performative predictions with proper scoring rules","date":"2023-05-28","arxiv_id":"2305.17601","repositories_listed":1,"syntology":null},{"url":"/paper/an-effective-wgan-based-anomaly-detection","slug":"an-effective-wgan-based-anomaly-detection","title":"An Effective WGAN-based Anomaly Detection Model for loT Multivariate Time Series Published on Pacific-Asia Conference on Knowledge Discovery and Data Mining","date":"2023-05-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explainable-brain-age-prediction-using","slug":"explainable-brain-age-prediction-using","title":"Explainable Brain Age Prediction using coVariance Neural Networks","date":"2023-05-27","arxiv_id":"2305.18370","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/explainable-brain-age-prediction-using#ran","syntology_url":"https://syntology.ai/paper/2305.18370","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18370"}},"official":{"repos":["sihags/vnn_brain_age"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/federated-conformal-predictors-for","slug":"federated-conformal-predictors-for","title":"Federated Conformal Predictors for Distributed Uncertainty Quantification","date":"2023-05-27","arxiv_id":"2305.17564","repositories_listed":1,"syntology":null},{"url":"/paper/xgrad-boosting-gradient-based-optimizers-with","slug":"xgrad-boosting-gradient-based-optimizers-with","title":"XGrad: Boosting Gradient-Based Optimizers With Weight Prediction","date":"2023-05-26","arxiv_id":"2305.18240","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-visual-question-answering-with","slug":"zero-shot-visual-question-answering-with","title":"Zero-shot Visual Question Answering with Language Model Feedback","date":"2023-05-26","arxiv_id":"2305.17006","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-not-link-exploring-nil-prediction-in","slug":"learn-to-not-link-exploring-nil-prediction-in","title":"Learn to Not Link: Exploring NIL Prediction in Entity Linking","date":"2023-05-25","arxiv_id":"2305.15725","repositories_listed":1,"syntology":null},{"url":"/paper/ovo-open-vocabulary-occupancy","slug":"ovo-open-vocabulary-occupancy","title":"OVO: Open-Vocabulary Occupancy","date":"2023-05-25","arxiv_id":"2305.16133","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/ovo-open-vocabulary-occupancy#ran","syntology_url":"https://syntology.ai/paper/2305.16133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16133"}},"official":{"repos":["dzcgaara/OVO"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/type-prediction-with-program-decomposition","slug":"type-prediction-with-program-decomposition","title":"Type Prediction With Program Decomposition and Fill-in-the-Type Training","date":"2023-05-25","arxiv_id":"2305.17145","repositories_listed":1,"syntology":null},{"url":"/paper/safety-aware-semi-end-to-end-coordinated","slug":"safety-aware-semi-end-to-end-coordinated","title":"Safety-aware Semi-end-to-end Coordinated Decision Model for Voltage Regulation in Active Distribution Network","date":"2023-05-24","arxiv_id":"2305.15395","repositories_listed":1,"syntology":null},{"url":"/paper/2305-14517","slug":"2305-14517","title":"CongFu: Conditional Graph Fusion for Drug Synergy Prediction","date":"2023-05-23","arxiv_id":"2305.14517","repositories_listed":1,"syntology":null},{"url":"/paper/anchor-prediction-automatic-refinement-of","slug":"anchor-prediction-automatic-refinement-of","title":"Anchor Prediction: Automatic Refinement of Internet Links","date":"2023-05-23","arxiv_id":"2305.14337","repositories_listed":1,"syntology":null},{"url":"/paper/speech-structured-prediction-with-energy","slug":"speech-structured-prediction-with-energy","title":"SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres","date":"2023-05-23","arxiv_id":"2305.13617","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"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 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) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/speech-structured-prediction-with-energy#ran","syntology_url":"https://syntology.ai/paper/2305.13617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13617"}},"official":{"repos":["zjunlp/speech"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tecs-a-dataset-and-benchmark-for-tense","slug":"tecs-a-dataset-and-benchmark-for-tense","title":"TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation","date":"2023-05-23","arxiv_id":"2305.13740","repositories_listed":1,"syntology":null},{"url":"/paper/towards-early-prediction-of-human-ipsc","slug":"towards-early-prediction-of-human-ipsc","title":"Towards Early Prediction of Human iPSC Reprogramming Success","date":"2023-05-23","arxiv_id":"2305.14575","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-over-graph-with-1","slug":"uncertainty-quantification-over-graph-with-1","title":"Uncertainty Quantification over Graph with Conformalized Graph Neural Networks","date":"2023-05-23","arxiv_id":"2305.14535","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"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) · 3 unverified","sample_list":"/paper/uncertainty-quantification-over-graph-with-1#ran","syntology_url":"https://syntology.ai/paper/2305.14535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14535"}},"official":{"repos":["snap-stanford/conformalized-gnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/capturing-conversion-rate-fluctuation-during","slug":"capturing-conversion-rate-fluctuation-during","title":"Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach","date":"2023-05-22","arxiv_id":"2305.12837","repositories_listed":1,"syntology":null},{"url":"/paper/friendly-neighbors-contextualized-sequence-to","slug":"friendly-neighbors-contextualized-sequence-to","title":"Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction","date":"2023-05-22","arxiv_id":"2305.13059","repositories_listed":1,"syntology":null},{"url":"/paper/relabel-minimal-training-subset-to-flip-a","slug":"relabel-minimal-training-subset-to-flip-a","title":"Relabeling Minimal Training Subset to Flip a Prediction","date":"2023-05-22","arxiv_id":"2305.12809","repositories_listed":1,"syntology":null},{"url":"/paper/learning-large-graph-property-prediction-via","slug":"learning-large-graph-property-prediction-via","title":"Learning Large Graph Property Prediction via Graph Segment Training","date":"2023-05-21","arxiv_id":"2305.12322","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-large-graph-property-prediction-via#ran","syntology_url":"https://syntology.ai/paper/2305.12322","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12322"}},"official":{"repos":["kaidic/gst"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/anypredict-foundation-model-for-tabular","slug":"anypredict-foundation-model-for-tabular","title":"MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement","date":"2023-05-20","arxiv_id":"2305.12081","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/anypredict-foundation-model-for-tabular#ran","syntology_url":"https://syntology.ai/paper/2305.12081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12081"}},"official":{"repos":["ryanwangzf/meditab"],"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/pastnet-introducing-physical-inductive-biases","slug":"pastnet-introducing-physical-inductive-biases","title":"PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction","date":"2023-05-19","arxiv_id":"2305.11421","repositories_listed":1,"syntology":null},{"url":"/paper/query-performance-prediction-from-ad-hoc-to","slug":"query-performance-prediction-from-ad-hoc-to","title":"Query Performance Prediction: From Ad-hoc to Conversational Search","date":"2023-05-18","arxiv_id":"2305.10923","repositories_listed":1,"syntology":null},{"url":"/paper/bike2vec-vector-embedding-representations-of","slug":"bike2vec-vector-embedding-representations-of","title":"Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races","date":"2023-05-17","arxiv_id":"2305.10471","repositories_listed":1,"syntology":null},{"url":"/paper/generative-table-pre-training-empowers-models","slug":"generative-table-pre-training-empowers-models","title":"Generative Table Pre-training Empowers Models for Tabular Prediction","date":"2023-05-16","arxiv_id":"2305.09696","repositories_listed":1,"syntology":null},{"url":"/paper/mimex-intrinsic-rewards-from-masked-input-1","slug":"mimex-intrinsic-rewards-from-masked-input-1","title":"MIMEx: Intrinsic Rewards from Masked Input Modeling","date":"2023-05-15","arxiv_id":"2305.08932","repositories_listed":1,"syntology":null},{"url":"/paper/a-critical-view-of-vision-based-long-term","slug":"a-critical-view-of-vision-based-long-term","title":"A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment","date":"2023-05-12","arxiv_id":"2305.07648","repositories_listed":1,"syntology":null},{"url":"/paper/hahe-hierarchical-attention-for-hyper","slug":"hahe-hierarchical-attention-for-hyper","title":"HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level","date":"2023-05-11","arxiv_id":"2305.06588","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 3 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/hahe-hierarchical-attention-for-hyper#ran","syntology_url":"https://syntology.ai/paper/2305.06588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06588"}},"official":{"repos":["lhrlab/hahe"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-dynamic-point-cloud-compression-via","slug":"learning-dynamic-point-cloud-compression-via","title":"Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block Matching","date":"2023-05-09","arxiv_id":"2305.05356","repositories_listed":1,"syntology":null},{"url":"/paper/multilevel-sentence-embeddings-for","slug":"multilevel-sentence-embeddings-for","title":"Multilevel Sentence Embeddings for Personality Prediction","date":"2023-05-09","arxiv_id":"2305.05748","repositories_listed":1,"syntology":null},{"url":"/paper/autoencoder-based-prediction-of-icu-clinical","slug":"autoencoder-based-prediction-of-icu-clinical","title":"Autoencoder-based prediction of ICU clinical codes","date":"2023-05-08","arxiv_id":"2305.04992","repositories_listed":1,"syntology":null},{"url":"/paper/physbench-a-benchmark-framework-for-remote","slug":"physbench-a-benchmark-framework-for-remote","title":"Camera-Based HRV Prediction for Remote Learning Environments","date":"2023-05-07","arxiv_id":"2305.04161","repositories_listed":1,"syntology":null},{"url":"/paper/masked-trajectory-models-for-prediction","slug":"masked-trajectory-models-for-prediction","title":"Masked Trajectory Models for Prediction, Representation, and Control","date":"2023-05-04","arxiv_id":"2305.02968","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/masked-trajectory-models-for-prediction#ran","syntology_url":"https://syntology.ai/paper/2305.02968","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02968"}},"official":{"repos":["facebookresearch/mtm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/meat-freshness-prediction","slug":"meat-freshness-prediction","title":"Meat Freshness Prediction","date":"2023-05-01","arxiv_id":"2305.00986","repositories_listed":1,"syntology":null},{"url":"/paper/o-gnn-incorporating-ring-priors-into","slug":"o-gnn-incorporating-ring-priors-into","title":"O-GNN: Incorporating Ring Priors into Molecular Modeling","date":"2023-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/performative-prediction-with-bandit-feedback","slug":"performative-prediction-with-bandit-feedback","title":"Performative Prediction with Bandit Feedback: Learning through Reparameterization","date":"2023-05-01","arxiv_id":"2305.01094","repositories_listed":1,"syntology":null},{"url":"/paper/a-positive-feedback-method-based-on-f-measure","slug":"a-positive-feedback-method-based-on-f-measure","title":"A positive feedback method based on F-measure value for Salient Object Detection","date":"2023-04-28","arxiv_id":"2304.14619","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-then-correction-an-abductive","slug":"prediction-then-correction-an-abductive","title":"Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation","date":"2023-04-27","arxiv_id":"2304.14050","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-neural-network-from","slug":"uncertainty-aware-neural-network-from","title":"Uncertainty Aware Neural Network from Similarity and Sensitivity","date":"2023-04-27","arxiv_id":"2304.14925","repositories_listed":1,"syntology":null}],"record_sha256":"9d2c17662e174a524e527e9825348a37e7dc9a7517855eb381644e0cb2bde72d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}