{"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/3","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":3,"pages_in_order":88,"rows_per_page":100,"rows":[201,300],"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/2","next":"/task/prediction/papers/4","papers":[{"url":"/paper/conformal-language-modeling","slug":"conformal-language-modeling","title":"Conformal Language Modeling","date":"2023-06-16","arxiv_id":"2306.10193","repositories_listed":2,"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":1,"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/conformal-language-modeling#ran","syntology_url":"https://syntology.ai/paper/2306.10193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10193"}},"official":{"repos":["varal7/conformal-language-modeling"],"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/probabilistic-concept-bottleneck-models","slug":"probabilistic-concept-bottleneck-models","title":"Probabilistic Concept Bottleneck Models","date":"2023-06-02","arxiv_id":"2306.01574","repositories_listed":2,"syntology":{"n":17,"n_ran":10,"n_constructed":4,"n_ran_checked":5,"n_instrument":5,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"10 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/probabilistic-concept-bottleneck-models#ran","syntology_url":"https://syntology.ai/paper/2306.01574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01574"}},"official":{"repos":["ejkim47/prob-cbm"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/deepmss-deep-multi-modality-segmentation-to","slug":"deepmss-deep-multi-modality-segmentation-to","title":"AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images","date":"2023-05-17","arxiv_id":"2305.09946","repositories_listed":2,"syntology":null},{"url":"/paper/shotgun-crystal-structure-prediction-using","slug":"shotgun-crystal-structure-prediction-using","title":"Shotgun crystal structure prediction using machine-learned formation energies","date":"2023-05-03","arxiv_id":"2305.02158","repositories_listed":2,"syntology":null},{"url":"/paper/towards-efficient-and-comprehensive-urban-1","slug":"towards-efficient-and-comprehensive-urban-1","title":"LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction","date":"2023-04-27","arxiv_id":"2304.14343","repositories_listed":2,"syntology":{"n":25,"n_ran":22,"n_constructed":0,"n_ran_checked":22,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":22,"n_pointer_only":2,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/towards-efficient-and-comprehensive-urban-1#ran","syntology_url":"https://syntology.ai/paper/2304.14343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.14343"}},"official":{"repos":["libcity/bigscity-libcity-datasets","libcity/bigscity-libcity"],"state":"official (archive's flag): 22 ran","n_ran":22,"n_constructed":0,"n_ran_no_instrument_failure":22,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/an-investigation-of-speaker-independent","slug":"an-investigation-of-speaker-independent","title":"An investigation of phrase break prediction in an End-to-End TTS system","date":"2023-04-09","arxiv_id":"2304.04157","repositories_listed":2,"syntology":null},{"url":"/paper/traffnet-learning-causality-of-traffic","slug":"traffnet-learning-causality-of-traffic","title":"TraffNet: Learning Causality of Traffic Generation for What-if Prediction","date":"2023-03-28","arxiv_id":"2303.15954","repositories_listed":2,"syntology":null},{"url":"/paper/eqmotion-equivariant-multi-agent-motion","slug":"eqmotion-equivariant-multi-agent-motion","title":"EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning","date":"2023-03-20","arxiv_id":"2303.10876","repositories_listed":2,"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/eqmotion-equivariant-multi-agent-motion#ran","syntology_url":"https://syntology.ai/paper/2303.10876","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10876"}},"official":{"repos":["mediabrain-sjtu/eqmotion"],"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/highly-accurate-quantum-chemical-property","slug":"highly-accurate-quantum-chemical-property","title":"Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+","date":"2023-03-16","arxiv_id":"2303.16982","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/highly-accurate-quantum-chemical-property#ran","syntology_url":"https://syntology.ai/paper/2303.16982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16982"}},"official":{"repos":["dptech-corp/Uni-Mol"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"url":"/paper/surroundocc-multi-camera-3d-occupancy","slug":"surroundocc-multi-camera-3d-occupancy","title":"SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving","date":"2023-03-16","arxiv_id":"2303.09551","repositories_listed":2,"syntology":{"n":24,"n_ran":18,"n_constructed":0,"n_ran_checked":8,"n_instrument":10,"n_unverified":6,"n_honours":4,"n_violates":0,"n_no_contract":4,"n_pointer_only":21,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 4 honoured, 0 violated, 4 with no contract checked; 10 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/surroundocc-multi-camera-3d-occupancy#ran","syntology_url":"https://syntology.ai/paper/2303.09551","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09551"}},"official":{"repos":["weiyithu/surroundocc"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/improving-adaptive-conformal-prediction-using","slug":"improving-adaptive-conformal-prediction-using","title":"Improving Adaptive Conformal Prediction Using Self-Supervised Learning","date":"2023-02-23","arxiv_id":"2302.12238","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/improving-adaptive-conformal-prediction-using#ran","syntology_url":"https://syntology.ai/paper/2302.12238","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.12238"}},"official":{"repos":["seedatnabeel/sscp","vanderschaarlab/sscp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/improved-online-conformal-prediction-via","slug":"improved-online-conformal-prediction-via","title":"Improved Online Conformal Prediction via Strongly Adaptive Online Learning","date":"2023-02-15","arxiv_id":"2302.07869","repositories_listed":2,"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/improved-online-conformal-prediction-via#ran","syntology_url":"https://syntology.ai/paper/2302.07869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07869"}},"official":{"repos":["salesforce/online_conformal"],"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/enhancing-deep-knowledge-tracing-with","slug":"enhancing-deep-knowledge-tracing-with","title":"Enhancing Deep Knowledge Tracing with Auxiliary Tasks","date":"2023-02-14","arxiv_id":"2302.07942","repositories_listed":2,"syntology":null},{"url":"/paper/improving-interpretability-of-deep-sequential","slug":"improving-interpretability-of-deep-sequential","title":"Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations","date":"2023-02-14","arxiv_id":"2302.06885","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":5,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 5 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) · 3 unverified","sample_list":"/paper/improving-interpretability-of-deep-sequential#ran","syntology_url":"https://syntology.ai/paper/2302.06885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06885"}},"official":null}},{"url":"/paper/analyzing-the-effectiveness-of-the-underlying","slug":"analyzing-the-effectiveness-of-the-underlying","title":"Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering","date":"2023-02-12","arxiv_id":"2302.05963","repositories_listed":2,"syntology":null},{"url":"/paper/fully-transformer-based-biomarker-prediction","slug":"fully-transformer-based-biomarker-prediction","title":"Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study","date":"2023-01-23","arxiv_id":"2301.09617","repositories_listed":2,"syntology":null},{"url":"/paper/there-is-no-big-brother-or-small-brother","slug":"there-is-no-big-brother-or-small-brother","title":"There is No Big Brother or Small Brother: Knowledge Infusion in Language Models for Link Prediction and Question Answering","date":"2023-01-10","arxiv_id":"2301.04013","repositories_listed":2,"syntology":null},{"url":"/paper/dual-accuracy-quality-driven-neural-network","slug":"dual-accuracy-quality-driven-neural-network","title":"Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation","date":"2022-12-13","arxiv_id":"2212.06370","repositories_listed":2,"syntology":null},{"url":"/paper/bayesian-simultaneous-factorization-and","slug":"bayesian-simultaneous-factorization-and","title":"Bayesian Simultaneous Factorization and Prediction Using Multi-Omic Data","date":"2022-11-29","arxiv_id":"2211.16403","repositories_listed":2,"syntology":null},{"url":"/paper/hierarchical-graph-structures-for-congestion","slug":"hierarchical-graph-structures-for-congestion","title":"Hierarchical Graph Structures for Congestion and ETA Prediction","date":"2022-11-21","arxiv_id":"2211.11762","repositories_listed":2,"syntology":null},{"url":"/paper/radiomics-enhanced-deep-multi-task-learning","slug":"radiomics-enhanced-deep-multi-task-learning","title":"Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer","date":"2022-11-10","arxiv_id":"2211.05409","repositories_listed":2,"syntology":null},{"url":"/paper/multiplicity-adjusted-bootstrap-tilting-lower","slug":"multiplicity-adjusted-bootstrap-tilting-lower","title":"Post-Selection Confidence Bounds for Prediction Performance","date":"2022-10-24","arxiv_id":"2210.13206","repositories_listed":2,"syntology":null},{"url":"/paper/amgnet-multi-scale-graph-neural-networks-for","slug":"amgnet-multi-scale-graph-neural-networks-for","title":"AMGNET: multi-scale graph neural networks for flow field prediction","date":"2022-10-13","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/selection-by-prediction-with-conformal-p","slug":"selection-by-prediction-with-conformal-p","title":"Selection by Prediction with Conformal p-values","date":"2022-10-04","arxiv_id":"2210.01408","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":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/selection-by-prediction-with-conformal-p#ran","syntology_url":"https://syntology.ai/paper/2210.01408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01408"}},"official":{"repos":["ying531/selcf_paper"],"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/expanding-the-deployment-envelope-of-behavior","slug":"expanding-the-deployment-envelope-of-behavior","title":"Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning","date":"2022-09-23","arxiv_id":"2209.11820","repositories_listed":2,"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/expanding-the-deployment-envelope-of-behavior#ran","syntology_url":"https://syntology.ai/paper/2209.11820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.11820"}},"official":{"repos":["nvlabs/adaptive-prediction","nvr-avg/adaptive-prediction"],"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/periodic-graph-transformers-for-crystal","slug":"periodic-graph-transformers-for-crystal","title":"Periodic Graph Transformers for Crystal Material Property Prediction","date":"2022-09-23","arxiv_id":"2209.11807","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"6 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/periodic-graph-transformers-for-crystal#ran","syntology_url":"https://syntology.ai/paper/2209.11807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.11807"}},"official":{"repos":["YKQ98/Matformer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/mtr-a-1st-place-solution-for-2022-waymo-open","slug":"mtr-a-1st-place-solution-for-2022-waymo-open","title":"MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction","date":"2022-09-20","arxiv_id":"2209.10033","repositories_listed":2,"syntology":null},{"url":"/paper/prediction-based-one-shot-dynamic-parking","slug":"prediction-based-one-shot-dynamic-parking","title":"Prediction-based One-shot Dynamic Parking Pricing","date":"2022-08-30","arxiv_id":"2208.14231","repositories_listed":2,"syntology":null},{"url":"/paper/conformal-risk-control","slug":"conformal-risk-control","title":"Conformal Risk Control","date":"2022-08-04","arxiv_id":"2208.02814","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/conformal-risk-control#ran","syntology_url":"https://syntology.ai/paper/2208.02814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.02814"}},"official":{"repos":["aangelopoulos/conformal-risk"],"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/interpretable-bilinear-attention-network-with","slug":"interpretable-bilinear-attention-network-with","title":"Interpretable bilinear attention network with domain adaptation improves drug-target prediction","date":"2022-08-03","arxiv_id":"2208.02194","repositories_listed":2,"syntology":null},{"url":"/paper/psp-million-level-protein-sequence-dataset","slug":"psp-million-level-protein-sequence-dataset","title":"PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction","date":"2022-06-24","arxiv_id":"2206.12240","repositories_listed":2,"syntology":null},{"url":"/paper/smt-dta-improving-drug-target-affinity","slug":"smt-dta-improving-drug-target-affinity","title":"SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction","date":"2022-06-20","arxiv_id":"2206.09818","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/smt-dta-improving-drug-target-affinity#ran","syntology_url":"https://syntology.ai/paper/2206.09818","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.09818"}},"official":{"repos":["qizhipei/smt-dta","qizhipei/ssm-dta"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/conformal-prediction-set-for-time-series","slug":"conformal-prediction-set-for-time-series","title":"Conformal prediction set for time-series","date":"2022-06-15","arxiv_id":"2206.07851","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/conformal-prediction-set-for-time-series#ran","syntology_url":"https://syntology.ai/paper/2206.07851","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.07851"}},"official":{"repos":["hamrel-cxu/ensemble-regularized-adaptive-prediction-set-eraps"],"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/masked-conditional-video-diffusion-for","slug":"masked-conditional-video-diffusion-for","title":"MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation","date":"2022-05-19","arxiv_id":"2205.09853","repositories_listed":2,"syntology":{"n":16,"n_ran":7,"n_constructed":1,"n_ran_checked":4,"n_instrument":3,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/masked-conditional-video-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2205.09853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.09853"}},"official":{"repos":["voletiv/mcvd-pytorch"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/stock-price-prediction-based-on-natural","slug":"stock-price-prediction-based-on-natural","title":"Stock Price Prediction Based on Natural Language Processing","date":"2022-05-06","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/hybrid-cnn-based-attention-with-category","slug":"hybrid-cnn-based-attention-with-category","title":"Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling","date":"2022-05-05","arxiv_id":"2205.02711","repositories_listed":2,"syntology":null},{"url":"/paper/multivariate-prediction-intervals-for-random","slug":"multivariate-prediction-intervals-for-random","title":"Multivariate Prediction Intervals for Random Forests","date":"2022-05-04","arxiv_id":"2205.02260","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/multivariate-prediction-intervals-for-random#ran","syntology_url":"https://syntology.ai/paper/2205.02260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.02260"}},"official":{"repos":["CitrineInformatics/lolo","citrineinformatics/multivariate-prediction-intervals"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/from-noisy-prediction-to-true-label-noisy","slug":"from-noisy-prediction-to-true-label-noisy","title":"From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model","date":"2022-05-02","arxiv_id":"2205.00690","repositories_listed":2,"syntology":{"n":13,"n_ran":6,"n_constructed":2,"n_ran_checked":5,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"6 ran (of which 2 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) · 7 unverified","sample_list":"/paper/from-noisy-prediction-to-true-label-noisy#ran","syntology_url":"https://syntology.ai/paper/2205.00690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.00690"}},"official":{"repos":["BaeHeeSun/NPC"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/where-and-what-driver-attention-based-object","slug":"where-and-what-driver-attention-based-object","title":"Where and What: Driver Attention-based Object Detection","date":"2022-04-26","arxiv_id":"2204.12150","repositories_listed":2,"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":0,"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/where-and-what-driver-attention-based-object#ran","syntology_url":"https://syntology.ai/paper/2204.12150","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.12150"}},"official":{"repos":["yaorong0921/driver-gaze-yolov5"],"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/meta-transfer-learning-for-early-success","slug":"meta-transfer-learning-for-early-success","title":"Meta Transfer Learning for Early Success Prediction in MOOCs","date":"2022-04-25","arxiv_id":"2205.01064","repositories_listed":2,"syntology":null},{"url":"/paper/faces-ai-blitz-xiii-solutions","slug":"faces-ai-blitz-xiii-solutions","title":"Faces: AI Blitz XIII Solutions","date":"2022-04-03","arxiv_id":"2204.01081","repositories_listed":2,"syntology":null},{"url":"/paper/reinforcement-learning-with-action-free-pre","slug":"reinforcement-learning-with-action-free-pre","title":"Reinforcement Learning with Action-Free Pre-Training from Videos","date":"2022-03-25","arxiv_id":"2203.13880","repositories_listed":2,"syntology":null},{"url":"/paper/protein-structure-representation-learning-by","slug":"protein-structure-representation-learning-by","title":"Protein Representation Learning by Geometric Structure Pretraining","date":"2022-03-11","arxiv_id":"2203.06125","repositories_listed":2,"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":2,"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/protein-structure-representation-learning-by#ran","syntology_url":"https://syntology.ai/paper/2203.06125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.06125"}},"official":{"repos":["deepgraphlearning/gearnet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/syntax-aware-network-for-handwritten","slug":"syntax-aware-network-for-handwritten","title":"Syntax-Aware Network for Handwritten Mathematical Expression Recognition","date":"2022-03-03","arxiv_id":"2203.01601","repositories_listed":2,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/syntax-aware-network-for-handwritten#ran","syntology_url":"https://syntology.ai/paper/2203.01601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01601"}},"official":{"repos":["tal-tech/san","phymond/hme100k"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/contextual-semantic-embeddings-for-ontology","slug":"contextual-semantic-embeddings-for-ontology","title":"Contextual Semantic Embeddings for Ontology Subsumption Prediction","date":"2022-02-20","arxiv_id":"2202.09791","repositories_listed":2,"syntology":null},{"url":"/paper/extracting-label-specific-key-input-features","slug":"extracting-label-specific-key-input-features","title":"Extracting Label-specific Key Input Features for Neural Code Intelligence Models","date":"2022-02-14","arxiv_id":"2202.06474","repositories_listed":2,"syntology":null},{"url":"/paper/structured-prediction-problem-archive","slug":"structured-prediction-problem-archive","title":"Structured Prediction Problem Archive","date":"2022-02-04","arxiv_id":"2202.03574","repositories_listed":2,"syntology":null},{"url":"/paper/matchmaker-a-deep-learning-framework-for-drug","slug":"matchmaker-a-deep-learning-framework-for-drug","title":"MatchMaker: A Deep Learning Framework for Drug Synergy Prediction","date":"2022-02-02","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/improving-vae-based-molecular-representations","slug":"improving-vae-based-molecular-representations","title":"Improving VAE based molecular representations for compound property prediction","date":"2022-01-13","arxiv_id":"2201.04929","repositories_listed":2,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/improving-vae-based-molecular-representations#ran","syntology_url":"https://syntology.ai/paper/2201.04929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.04929"}},"official":{"repos":["znavoyan/vae-embeddings","znavoyan/vaedatasets"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/hivt-hierarchical-vector-transformer-for","slug":"hivt-hierarchical-vector-transformer-for","title":"HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction","date":"2022-01-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/interpretable-knowledge-tracing-simple-and","slug":"interpretable-knowledge-tracing-simple-and","title":"Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations","date":"2021-12-15","arxiv_id":"2112.11209","repositories_listed":2,"syntology":null},{"url":"/paper/prediction-of-adverse-biological-effects-of","slug":"prediction-of-adverse-biological-effects-of","title":"Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings","date":"2021-12-08","arxiv_id":"2112.04605","repositories_listed":2,"syntology":null},{"url":"/paper/pairwise-learning-for-neural-link-prediction","slug":"pairwise-learning-for-neural-link-prediction","title":"Pairwise Learning for Neural Link Prediction","date":"2021-12-06","arxiv_id":"2112.02936","repositories_listed":2,"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/pairwise-learning-for-neural-link-prediction#ran","syntology_url":"https://syntology.ai/paper/2112.02936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.02936"}},"official":{"repos":["zhitao-wang/plnlp"],"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/online-search-with-best-price-and-query-based","slug":"online-search-with-best-price-and-query-based","title":"Online Search With Best-Price and Query-Based Predictions","date":"2021-12-02","arxiv_id":"2112.01592","repositories_listed":2,"syntology":null},{"url":"/paper/a-simple-equivariant-machine-learning-method","slug":"a-simple-equivariant-machine-learning-method","title":"A simple equivariant machine learning method for dynamics based on scalars","date":"2021-10-07","arxiv_id":"2110.03761","repositories_listed":2,"syntology":null},{"url":"/paper/geometric-transformers-for-protein-interface","slug":"geometric-transformers-for-protein-interface","title":"Geometric Transformers for Protein Interface Contact Prediction","date":"2021-10-06","arxiv_id":"2110.02423","repositories_listed":2,"syntology":null},{"url":"/paper/deepmts-deep-multi-task-learning-for-survival","slug":"deepmts-deep-multi-task-learning-for-survival","title":"DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT","date":"2021-09-16","arxiv_id":"2109.07711","repositories_listed":2,"syntology":null},{"url":"/paper/multi-task-balanced-and-recalibrated-network","slug":"multi-task-balanced-and-recalibrated-network","title":"Multitask Balanced and Recalibrated Network for Medical Code Prediction","date":"2021-09-06","arxiv_id":"2109.02418","repositories_listed":2,"syntology":null},{"url":"/paper/are-socially-aware-trajectory-prediction","slug":"are-socially-aware-trajectory-prediction","title":"Are socially-aware trajectory prediction models really socially-aware?","date":"2021-08-24","arxiv_id":"2108.10879","repositories_listed":2,"syntology":null},{"url":"/paper/densetnt-end-to-end-trajectory-prediction","slug":"densetnt-end-to-end-trajectory-prediction","title":"DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets","date":"2021-08-22","arxiv_id":"2108.09640","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/densetnt-end-to-end-trajectory-prediction#ran","syntology_url":"https://syntology.ai/paper/2108.09640","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.09640"}},"official":null}},{"url":"/paper/paint-transformer-feed-forward-neural","slug":"paint-transformer-feed-forward-neural","title":"Paint Transformer: Feed Forward Neural Painting with Stroke Prediction","date":"2021-08-09","arxiv_id":"2108.03798","repositories_listed":2,"syntology":null},{"url":"/paper/document-level-event-extraction-via-parallel","slug":"document-level-event-extraction-via-parallel","title":"Document-level Event Extraction via Parallel Prediction Networks","date":"2021-08-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/improving-inductive-link-prediction-using","slug":"improving-inductive-link-prediction-using","title":"Improving Inductive Link Prediction Using Hyper-Relational Facts","date":"2021-07-10","arxiv_id":"2107.04894","repositories_listed":2,"syntology":null},{"url":"/paper/edge-proposal-sets-for-link-prediction","slug":"edge-proposal-sets-for-link-prediction","title":"Edge Proposal Sets for Link Prediction","date":"2021-06-30","arxiv_id":"2106.15810","repositories_listed":2,"syntology":null},{"url":"/paper/convolutional-hypercomplex-embeddings-for","slug":"convolutional-hypercomplex-embeddings-for","title":"Convolutional Hypercomplex Embeddings for Link Prediction","date":"2021-06-29","arxiv_id":"2106.15230","repositories_listed":2,"syntology":null},{"url":"/paper/neural-controlled-differential-equations-for-1","slug":"neural-controlled-differential-equations-for-1","title":"Neural Controlled Differential Equations for Online Prediction Tasks","date":"2021-06-21","arxiv_id":"2106.11028","repositories_listed":2,"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/neural-controlled-differential-equations-for-1#ran","syntology_url":"https://syntology.ai/paper/2106.11028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11028"}},"official":{"repos":["jambo6/online-neural-cdes","patrick-kidger/torchcde"],"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/tracing-back-music-emotion-predictions-to","slug":"tracing-back-music-emotion-predictions-to","title":"Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities","date":"2021-06-14","arxiv_id":"2106.07787","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tracing-back-music-emotion-predictions-to#ran","syntology_url":"https://syntology.ai/paper/2106.07787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07787"}},"official":{"repos":["CPJKU/audioLIME","shreyanc/model_debugging"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/materials-representation-and-transfer","slug":"materials-representation-and-transfer","title":"Materials Representation and Transfer Learning for Multi-Property Prediction","date":"2021-06-04","arxiv_id":"2106.02225","repositories_listed":2,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"9 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/materials-representation-and-transfer#ran","syntology_url":"https://syntology.ai/paper/2106.02225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.02225"}},"official":{"repos":["gomes-lab/H-CLMP"],"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/calibrated-prediction-in-and-out-of-domain","slug":"calibrated-prediction-in-and-out-of-domain","title":"DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling","date":"2021-05-26","arxiv_id":"2105.12441","repositories_listed":2,"syntology":{"n":20,"n_ran":13,"n_constructed":9,"n_ran_checked":9,"n_instrument":4,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":16,"phrase":"13 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/calibrated-prediction-in-and-out-of-domain#ran","syntology_url":"https://syntology.ai/paper/2105.12441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12441"}},"official":{"repos":["matthias-k/DeepGaze"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/dfpn-deformable-frame-prediction-network","slug":"dfpn-deformable-frame-prediction-network","title":"DFPN: Deformable Frame Prediction Network","date":"2021-05-26","arxiv_id":"2105.12794","repositories_listed":2,"syntology":null},{"url":"/paper/dependency-parsing-as-mrc-based-span-span","slug":"dependency-parsing-as-mrc-based-span-span","title":"Dependency Parsing as MRC-based Span-Span Prediction","date":"2021-05-17","arxiv_id":"2105.07654","repositories_listed":2,"syntology":null},{"url":"/paper/predicting-traffic-signals-on-transportation","slug":"predicting-traffic-signals-on-transportation","title":"Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs","date":"2021-04-27","arxiv_id":"2104.13414","repositories_listed":2,"syntology":null},{"url":"/paper/cross-validation-what-does-it-estimate-and","slug":"cross-validation-what-does-it-estimate-and","title":"Cross-validation: what does it estimate and how well does it do it?","date":"2021-04-01","arxiv_id":"2104.00673","repositories_listed":2,"syntology":null},{"url":"/paper/completer-incomplete-multi-view-clustering","slug":"completer-incomplete-multi-view-clustering","title":"COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction","date":"2021-03-22","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/multimodal-motion-prediction-with-stacked","slug":"multimodal-motion-prediction-with-stacked","title":"Multimodal Motion Prediction with Stacked Transformers","date":"2021-03-22","arxiv_id":"2103.11624","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/multimodal-motion-prediction-with-stacked#ran","syntology_url":"https://syntology.ai/paper/2103.11624","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.11624"}},"official":{"repos":["decisionforce/mmTransformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/warp-q-quality-prediction-for-generative","slug":"warp-q-quality-prediction-for-generative","title":"WARP-Q: Quality Prediction For Generative Neural Speech Codecs","date":"2021-02-20","arxiv_id":"2102.10449","repositories_listed":2,"syntology":null},{"url":"/paper/latent-variable-nested-set-transformers","slug":"latent-variable-nested-set-transformers","title":"Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction","date":"2021-02-19","arxiv_id":"2104.00563","repositories_listed":2,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":2,"phrase":"11 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; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/latent-variable-nested-set-transformers#ran","syntology_url":"https://syntology.ai/paper/2104.00563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.00563"}},"official":{"repos":["roggirg/AutoBots"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/principled-simplicial-neural-networks-for","slug":"principled-simplicial-neural-networks-for","title":"Principled Simplicial Neural Networks for Trajectory Prediction","date":"2021-02-19","arxiv_id":"2102.10058","repositories_listed":2,"syntology":{"n":8,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"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) · 7 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/principled-simplicial-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/2102.10058","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.10058"}},"official":{"repos":["nglaze00/SCoNe_GCN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":6,"ran_from_kinds":["listed"]}}},{"url":"/paper/clockwork-variational-autoencoders-for-video","slug":"clockwork-variational-autoencoders-for-video","title":"Clockwork Variational Autoencoders","date":"2021-02-18","arxiv_id":"2102.09532","repositories_listed":2,"syntology":null},{"url":"/paper/quartile-based-prediction-of-event-types-and","slug":"quartile-based-prediction-of-event-types-and","title":"A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes","date":"2021-02-11","arxiv_id":"2102.07838","repositories_listed":2,"syntology":null},{"url":"/paper/structured-prediction-as-translation-between-1","slug":"structured-prediction-as-translation-between-1","title":"Structured Prediction as Translation between Augmented Natural Languages","date":"2021-01-14","arxiv_id":"2101.05779","repositories_listed":2,"syntology":null},{"url":"/paper/disentangled-self-attentive-neural-networks","slug":"disentangled-self-attentive-neural-networks","title":"Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction","date":"2021-01-11","arxiv_id":"2101.03654","repositories_listed":2,"syntology":null},{"url":"/paper/from-goals-waypoints-paths-to-long-term-human","slug":"from-goals-waypoints-paths-to-long-term-human","title":"From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting","date":"2020-12-02","arxiv_id":"2012.01526","repositories_listed":2,"syntology":null},{"url":"/paper/disentangling-label-distribution-for-long","slug":"disentangling-label-distribution-for-long","title":"Disentangling Label Distribution for Long-tailed Visual Recognition","date":"2020-12-01","arxiv_id":"2012.00321","repositories_listed":2,"syntology":null},{"url":"/paper/lipophilicity-prediction-with-multitask","slug":"lipophilicity-prediction-with-multitask","title":"Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation","date":"2020-11-24","arxiv_id":"2011.12117","repositories_listed":2,"syntology":null},{"url":"/paper/end-to-end-lane-shape-prediction-with","slug":"end-to-end-lane-shape-prediction-with","title":"End-to-end Lane Shape Prediction with Transformers","date":"2020-11-09","arxiv_id":"2011.04233","repositories_listed":2,"syntology":null},{"url":"/paper/exploring-dynamic-context-for-multi-path","slug":"exploring-dynamic-context-for-multi-path","title":"Exploring Dynamic Context for Multi-path Trajectory Prediction","date":"2020-10-30","arxiv_id":"2010.16267","repositories_listed":2,"syntology":null},{"url":"/paper/line-graph-neural-networks-for-link","slug":"line-graph-neural-networks-for-link","title":"Line Graph Neural Networks for Link Prediction","date":"2020-10-20","arxiv_id":"2010.10046","repositories_listed":2,"syntology":null},{"url":"/paper/conformal-prediction-interval-for-dynamic","slug":"conformal-prediction-interval-for-dynamic","title":"Conformal prediction interval for dynamic time-series","date":"2020-10-18","arxiv_id":"2010.09107","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/conformal-prediction-interval-for-dynamic#ran","syntology_url":"https://syntology.ai/paper/2010.09107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.09107"}},"official":{"repos":["hamrel-cxu/EnbPI"],"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/training-independent-subnetworks-for-robust-1","slug":"training-independent-subnetworks-for-robust-1","title":"Training independent subnetworks for robust prediction","date":"2020-10-13","arxiv_id":"2010.06610","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/training-independent-subnetworks-for-robust-1#ran","syntology_url":"https://syntology.ai/paper/2010.06610","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06610"}},"official":{"repos":["google/uncertainty-baselines"],"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/automated-concatenation-of-embeddings-for-1","slug":"automated-concatenation-of-embeddings-for-1","title":"Automated Concatenation of Embeddings for Structured Prediction","date":"2020-10-10","arxiv_id":"2010.05006","repositories_listed":2,"syntology":null},{"url":"/paper/inductive-entity-representations-from-text","slug":"inductive-entity-representations-from-text","title":"Inductive Entity Representations from Text via Link Prediction","date":"2020-10-07","arxiv_id":"2010.03496","repositories_listed":2,"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/inductive-entity-representations-from-text#ran","syntology_url":"https://syntology.ai/paper/2010.03496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.03496"}},"official":{"repos":["dfdazac/blp"],"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/generative-model-enhanced-human-motion-1","slug":"generative-model-enhanced-human-motion-1","title":"Generative Model-Enhanced Human Motion Prediction","date":"2020-10-05","arxiv_id":"2010.11699","repositories_listed":2,"syntology":{"n":10,"n_ran":6,"n_constructed":5,"n_ran_checked":6,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"phrase":"6 ran (of which 5 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) · 4 unverified","sample_list":"/paper/generative-model-enhanced-human-motion-1#ran","syntology_url":"https://syntology.ai/paper/2010.11699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.11699"}},"official":{"repos":["bouracha/OoDMotion"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":5,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/opentraj-assessing-prediction-complexity-in","slug":"opentraj-assessing-prediction-complexity-in","title":"OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets","date":"2020-10-02","arxiv_id":"2010.00890","repositories_listed":2,"syntology":null},{"url":"/paper/owl2vec-embedding-of-owl-ontologies","slug":"owl2vec-embedding-of-owl-ontologies","title":"OWL2Vec*: Embedding of OWL Ontologies","date":"2020-09-30","arxiv_id":"2009.14654","repositories_listed":2,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/owl2vec-embedding-of-owl-ontologies#ran","syntology_url":"https://syntology.ai/paper/2009.14654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14654"}},"official":{"repos":["KRR-Oxford/OWL2Vec-Star"],"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":["listed","official"]}}},{"url":"/paper/fairness-matters-a-data-driven-framework","slug":"fairness-matters-a-data-driven-framework","title":"Fair and accurate age prediction using distribution aware data curation and augmentation","date":"2020-09-11","arxiv_id":"2009.05283","repositories_listed":2,"syntology":null},{"url":"/paper/convgru-in-fine-grained-pitching-action","slug":"convgru-in-fine-grained-pitching-action","title":"ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction","date":"2020-08-18","arxiv_id":"2008.07819","repositories_listed":2,"syntology":null},{"url":"/paper/graph-neural-network-based-coarse-grained","slug":"graph-neural-network-based-coarse-grained","title":"Graph Neural Network Based Coarse-Grained Mapping Prediction","date":"2020-06-24","arxiv_id":"2007.04921","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/graph-neural-network-based-coarse-grained#ran","syntology_url":"https://syntology.ai/paper/2007.04921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.04921"}},"official":{"repos":["rochesterxugroup/DSGPM","rochesterxugroup/HAM_dataset"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/clinical-risk-prediction-with-temporal","slug":"clinical-risk-prediction-with-temporal","title":"Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning","date":"2020-06-23","arxiv_id":"2006.12777","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/clinical-risk-prediction-with-temporal#ran","syntology_url":"https://syntology.ai/paper/2006.12777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12777"}},"official":{"repos":["anhtuan5696/TPAMTL"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/predicting-temporal-sets-with-deep-neural","slug":"predicting-temporal-sets-with-deep-neural","title":"Predicting Temporal Sets with Deep Neural Networks","date":"2020-06-20","arxiv_id":"2006.11483","repositories_listed":2,"syntology":null}],"record_sha256":"5ec63912ab0b5ab859554c9a85fb3ee3e88b302b0ac7848b5a3f6b3b3d19c013","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}