{"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/8","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":8,"pages_in_order":88,"rows_per_page":100,"rows":[701,800],"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/7","next":"/task/prediction/papers/9","papers":[{"url":"/paper/proxi-challenging-the-gnns-for-link","slug":"proxi-challenging-the-gnns-for-link","title":"PROXI: Challenging the GNNs for Link Prediction","date":"2024-10-02","arxiv_id":"2410.01802","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-optimism-and-model-complexity-in","slug":"revisiting-optimism-and-model-complexity-in","title":"Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning","date":"2024-10-02","arxiv_id":"2410.01259","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/revisiting-optimism-and-model-complexity-in#ran","syntology_url":"https://syntology.ai/paper/2410.01259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.01259"}},"official":{"repos":["jaydu1/model-complexity"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-deep-regression-models-for-wsi","slug":"evaluating-deep-regression-models-for-wsi","title":"Evaluating Deep Regression Models for WSI-Based Gene-Expression Prediction","date":"2024-10-01","arxiv_id":"2410.00945","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-for-dose-response-models","slug":"conformal-prediction-for-dose-response-models","title":"Conformal Prediction for Dose-Response Models with Continuous Treatments","date":"2024-09-30","arxiv_id":"2409.20412","repositories_listed":1,"syntology":null},{"url":"/paper/delving-deep-into-engagement-prediction-of","slug":"delving-deep-into-engagement-prediction-of","title":"Delving Deep into Engagement Prediction of Short Videos","date":"2024-09-30","arxiv_id":"2410.00289","repositories_listed":1,"syntology":null},{"url":"/paper/open-source-periorbital-segmentation-dataset","slug":"open-source-periorbital-segmentation-dataset","title":"Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications","date":"2024-09-30","arxiv_id":"2409.20407","repositories_listed":1,"syntology":null},{"url":"/paper/reevaluation-of-inductive-link-prediction","slug":"reevaluation-of-inductive-link-prediction","title":"Reevaluation of Inductive Link Prediction","date":"2024-09-30","arxiv_id":"2409.20130","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-graph-neural-networks-for-1","slug":"a-survey-on-graph-neural-networks-for-1","title":"A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends","date":"2024-09-29","arxiv_id":"2409.19629","repositories_listed":1,"syntology":null},{"url":"/paper/a-universal-deep-learning-framework-for","slug":"a-universal-deep-learning-framework-for","title":"OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra","date":"2024-09-29","arxiv_id":"2409.19552","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-time-series-forecasting-a","slug":"optimizing-time-series-forecasting-a","title":"Optimizing Time Series Forecasting: A Comparative Study of Adam and Nesterov Accelerated Gradient on LSTM and GRU networks Using Stock Market data","date":"2024-09-28","arxiv_id":"2410.01843","repositories_listed":1,"syntology":null},{"url":"/paper/hr-extreme-a-high-resolution-dataset-for","slug":"hr-extreme-a-high-resolution-dataset-for","title":"HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting","date":"2024-09-27","arxiv_id":"2409.18885","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":9,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/hr-extreme-a-high-resolution-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2409.18885","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18885"}},"official":{"repos":["HuskyNian/HR-Extreme"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-time-series-is-worth-five-experts-1","slug":"a-time-series-is-worth-five-experts-1","title":"A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction","date":"2024-09-26","arxiv_id":"2409.17440","repositories_listed":1,"syntology":null},{"url":"/paper/adjusting-regression-models-for-conditional","slug":"adjusting-regression-models-for-conditional","title":"Adjusting Regression Models for Conditional Uncertainty Calibration","date":"2024-09-26","arxiv_id":"2409.17466","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-graph-conformal-prediction","slug":"benchmarking-graph-conformal-prediction","title":"Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs","date":"2024-09-26","arxiv_id":"2409.18332","repositories_listed":1,"syntology":null},{"url":"/paper/local-prediction-powered-inference","slug":"local-prediction-powered-inference","title":"Local Prediction-Powered Inference","date":"2024-09-26","arxiv_id":"2409.18321","repositories_listed":1,"syntology":null},{"url":"/paper/lotus-diffusion-based-visual-foundation-model","slug":"lotus-diffusion-based-visual-foundation-model","title":"Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction","date":"2024-09-26","arxiv_id":"2409.18124","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":12,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/lotus-diffusion-based-visual-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2409.18124","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18124"}},"official":null}},{"url":"/paper/mamba-meets-financial-markets-a-graph-mamba","slug":"mamba-meets-financial-markets-a-graph-mamba","title":"Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction","date":"2024-09-26","arxiv_id":"2410.03707","repositories_listed":1,"syntology":null},{"url":"/paper/what-would-happen-next-predicting","slug":"what-would-happen-next-predicting","title":"What Would Happen Next? Predicting Consequences from An Event Causality Graph","date":"2024-09-26","arxiv_id":"2409.17480","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-motion-prediction-a-lightweight","slug":"efficient-motion-prediction-a-lightweight","title":"Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed","date":"2024-09-24","arxiv_id":"2409.16154","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-pedestrian-trajectory-prediction","slug":"enhancing-pedestrian-trajectory-prediction","title":"Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information","date":"2024-09-23","arxiv_id":"2409.15224","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-multivariate-time-series-based","slug":"enhancing-multivariate-time-series-based","title":"Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive Learning","date":"2024-09-21","arxiv_id":"2409.14016","repositories_listed":1,"syntology":null},{"url":"/paper/cvt-occ-cost-volume-temporal-fusion-for-3d","slug":"cvt-occ-cost-volume-temporal-fusion-for-3d","title":"CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction","date":"2024-09-20","arxiv_id":"2409.13430","repositories_listed":1,"syntology":null},{"url":"/paper/computational-imaging-for-long-term","slug":"computational-imaging-for-long-term","title":"Computational Imaging for Long-Term Prediction of Solar Irradiance","date":"2024-09-18","arxiv_id":"2409.12016","repositories_listed":1,"syntology":null},{"url":"/paper/hmf-a-hybrid-multi-factor-framework-for","slug":"hmf-a-hybrid-multi-factor-framework-for","title":"A Hybrid Multi-Factor Network with Dynamic Sequence Modeling for Early Warning of Intraoperative Hypotension","date":"2024-09-17","arxiv_id":"2409.11064","repositories_listed":1,"syntology":null},{"url":"/paper/ultimatedo-an-efficient-framework-to-marry","slug":"ultimatedo-an-efficient-framework-to-marry","title":"UltimateDO: An Efficient Framework to Marry Occupancy Prediction with 3D Object Detection via Channel2height","date":"2024-09-17","arxiv_id":"2409.11160","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-and-prediction-quality-estimation","slug":"uncertainty-and-prediction-quality-estimation","title":"Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks","date":"2024-09-17","arxiv_id":"2409.11373","repositories_listed":1,"syntology":null},{"url":"/paper/prose-fd-a-multimodal-pde-foundation-model","slug":"prose-fd-a-multimodal-pde-foundation-model","title":"PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics","date":"2024-09-15","arxiv_id":"2409.09811","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":0,"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/prose-fd-a-multimodal-pde-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2409.09811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09811"}},"official":null}},{"url":"/paper/opus-occupancy-prediction-using-a-sparse-set","slug":"opus-occupancy-prediction-using-a-sparse-set","title":"OPUS: Occupancy Prediction Using a Sparse Set","date":"2024-09-14","arxiv_id":"2409.09350","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/opus-occupancy-prediction-using-a-sparse-set#ran","syntology_url":"https://syntology.ai/paper/2409.09350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09350"}},"official":{"repos":["jbwang1997/OPUS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/optimal-operation-of-a-building-with","slug":"optimal-operation-of-a-building-with","title":"Optimal Operation of a Building with Electricity-Heat Networks and Seasonal Storage","date":"2024-09-13","arxiv_id":"2409.08721","repositories_listed":1,"syntology":null},{"url":"/paper/sauc-sparsity-aware-uncertainty-calibration","slug":"sauc-sparsity-aware-uncertainty-calibration","title":"SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks","date":"2024-09-13","arxiv_id":"2409.08766","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":2,"n_no_contract":3,"n_pointer_only":11,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 2 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/sauc-sparsity-aware-uncertainty-calibration#ran","syntology_url":"https://syntology.ai/paper/2409.08766","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.08766"}},"official":{"repos":["AnonymousSAUC/SAUC"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-role-of-context-in-reading-time","slug":"on-the-role-of-context-in-reading-time","title":"On the Role of Context in Reading Time Prediction","date":"2024-09-12","arxiv_id":"2409.08160","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-via-latent-diffusion-for","slug":"data-augmentation-via-latent-diffusion-for","title":"Data Augmentation via Latent Diffusion for Saliency Prediction","date":"2024-09-11","arxiv_id":"2409.07307","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/data-augmentation-via-latent-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2409.07307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07307"}},"official":{"repos":["ivrl/augsal"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/legal-fact-prediction-task-definition-and","slug":"legal-fact-prediction-task-definition-and","title":"Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction","date":"2024-09-11","arxiv_id":"2409.07055","repositories_listed":1,"syntology":null},{"url":"/paper/easyst-a-simple-framework-for-spatio-temporal","slug":"easyst-a-simple-framework-for-spatio-temporal","title":"EasyST: A Simple Framework for Spatio-Temporal Prediction","date":"2024-09-10","arxiv_id":"2409.06748","repositories_listed":1,"syntology":null},{"url":"/paper/focusing-viral-risk-ranking-tool-on","slug":"focusing-viral-risk-ranking-tool-on","title":"Focusing Viral Risk Ranking Tool on Prediction","date":"2024-09-07","arxiv_id":"2409.04932","repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-llms-for-cross-city-od-flow","slug":"harnessing-llms-for-cross-city-od-flow","title":"Harnessing LLMs for Cross-City OD Flow Prediction","date":"2024-09-05","arxiv_id":"2409.03937","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-multi-country-gdp","slug":"deep-learning-for-multi-country-gdp","title":"Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact","date":"2024-09-04","arxiv_id":"2409.02551","repositories_listed":1,"syntology":null},{"url":"/paper/a-multimodal-object-level-contrast-learning","slug":"a-multimodal-object-level-contrast-learning","title":"A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction","date":"2024-09-03","arxiv_id":"2409.02145","repositories_listed":1,"syntology":null},{"url":"/paper/biochemical-prostate-cancer-recurrence","slug":"biochemical-prostate-cancer-recurrence","title":"Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slow","date":"2024-09-03","arxiv_id":"2409.02284","repositories_listed":1,"syntology":null},{"url":"/paper/federated-prediction-powered-inference-from","slug":"federated-prediction-powered-inference-from","title":"Federated Prediction-Powered Inference from Decentralized Data","date":"2024-09-03","arxiv_id":"2409.01730","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-aware-conformal-prediction-for","slug":"spatial-aware-conformal-prediction-for","title":"Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification","date":"2024-09-02","arxiv_id":"2409.01236","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":4,"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/spatial-aware-conformal-prediction-for#ran","syntology_url":"https://syntology.ai/paper/2409.01236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01236"}},"official":{"repos":["j4ckliu/sacp"],"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/dap-diffusion-based-affordance-prediction-for","slug":"dap-diffusion-based-affordance-prediction-for","title":"DAP: Diffusion-based Affordance Prediction for Multi-modality Storage","date":"2024-08-31","arxiv_id":"2409.00499","repositories_listed":1,"syntology":null},{"url":"/paper/dynamical-system-prediction-from-sparse","slug":"dynamical-system-prediction-from-sparse","title":"Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint","date":"2024-08-31","arxiv_id":"2409.00458","repositories_listed":1,"syntology":null},{"url":"/paper/in-context-imitation-learning-via-next-token","slug":"in-context-imitation-learning-via-next-token","title":"In-Context Imitation Learning via Next-Token Prediction","date":"2024-08-28","arxiv_id":"2408.15980","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":1,"n_no_contract":8,"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, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/in-context-imitation-learning-via-next-token#ran","syntology_url":"https://syntology.ai/paper/2408.15980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.15980"}},"official":{"repos":["Max-Fu/icrt"],"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/rgda-ddi-residual-graph-attention-network-and","slug":"rgda-ddi-residual-graph-attention-network-and","title":"RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction","date":"2024-08-27","arxiv_id":"2408.15310","repositories_listed":1,"syntology":null},{"url":"/paper/agentmove-predicting-human-mobility-anywhere","slug":"agentmove-predicting-human-mobility-anywhere","title":"AgentMove: Predicting Human Mobility Anywhere Using Large Language Model based Agentic Framework","date":"2024-08-26","arxiv_id":"2408.13986","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/agentmove-predicting-human-mobility-anywhere#ran","syntology_url":"https://syntology.ai/paper/2408.13986","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.13986"}},"official":{"repos":["tsinghua-fib-lab/agentmove"],"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/an-evaluation-of-explanation-methods-for","slug":"an-evaluation-of-explanation-methods-for","title":"An Evaluation of Explanation Methods for Black-Box Detectors of Machine-Generated Text","date":"2024-08-26","arxiv_id":"2408.14252","repositories_listed":1,"syntology":null},{"url":"/paper/dual-path-adversarial-lifting-for-domain","slug":"dual-path-adversarial-lifting-for-domain","title":"Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation","date":"2024-08-26","arxiv_id":"2408.13983","repositories_listed":1,"syntology":{"n":20,"n_ran":16,"n_constructed":0,"n_ran_checked":13,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/dual-path-adversarial-lifting-for-domain#ran","syntology_url":"https://syntology.ai/paper/2408.13983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.13983"}},"official":{"repos":["yushuntang/dpal"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/limp-large-language-model-enhanced-intent","slug":"limp-large-language-model-enhanced-intent","title":"LIMP: Large Language Model Enhanced Intent-aware Mobility Prediction","date":"2024-08-23","arxiv_id":"2408.12832","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-good-reliability-of-an-interval-based","slug":"on-the-good-reliability-of-an-interval-based","title":"On the good reliability of an interval-based metric to validate prediction uncertainty for machine learning regression tasks","date":"2024-08-23","arxiv_id":"2408.13089","repositories_listed":1,"syntology":null},{"url":"/paper/drexplainer-quantifiable-interpretability-in","slug":"drexplainer-quantifiable-interpretability-in","title":"DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network","date":"2024-08-22","arxiv_id":"2408.12139","repositories_listed":1,"syntology":null},{"url":"/paper/copra-bridging-cross-domain-pretrained","slug":"copra-bridging-cross-domain-pretrained","title":"CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction","date":"2024-08-21","arxiv_id":"2409.03773","repositories_listed":1,"syntology":null},{"url":"/paper/fate-focal-modulated-attention-encoder-for","slug":"fate-focal-modulated-attention-encoder-for","title":"FATE: Focal-modulated Attention Encoder for Temperature Prediction","date":"2024-08-21","arxiv_id":"2408.11336","repositories_listed":1,"syntology":null},{"url":"/paper/conformalized-interval-arithmetic-with","slug":"conformalized-interval-arithmetic-with","title":"Conformalized Interval Arithmetic with Symmetric Calibration","date":"2024-08-20","arxiv_id":"2408.10939","repositories_listed":1,"syntology":null},{"url":"/paper/a-population-to-individual-tuning-framework","slug":"a-population-to-individual-tuning-framework","title":"A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction","date":"2024-08-19","arxiv_id":"2408.09815","repositories_listed":1,"syntology":null},{"url":"/paper/ensemble-prediction-via-covariate-dependent","slug":"ensemble-prediction-via-covariate-dependent","title":"Ensemble Prediction via Covariate-dependent Stacking","date":"2024-08-19","arxiv_id":"2408.09755","repositories_listed":1,"syntology":null},{"url":"/paper/occmamba-semantic-occupancy-prediction-with","slug":"occmamba-semantic-occupancy-prediction-with","title":"OccMamba: Semantic Occupancy Prediction with State Space Models","date":"2024-08-19","arxiv_id":"2408.09859","repositories_listed":1,"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/occmamba-semantic-occupancy-prediction-with#ran","syntology_url":"https://syntology.ai/paper/2408.09859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.09859"}},"official":{"repos":["USTCLH/OccMamba"],"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/tdnetgen-empowering-complex-network","slug":"tdnetgen-empowering-complex-network","title":"TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics","date":"2024-08-19","arxiv_id":"2408.09825","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-of-pre-trained-and","slug":"uncertainty-quantification-of-pre-trained-and","title":"Uncertainty Quantification of Surrogate Models using Conformal Prediction","date":"2024-08-19","arxiv_id":"2408.09881","repositories_listed":1,"syntology":null},{"url":"/paper/webcam-based-pupil-diameter-prediction","slug":"webcam-based-pupil-diameter-prediction","title":"Webcam-based Pupil Diameter Prediction Benefits from Upscaling","date":"2024-08-19","arxiv_id":"2408.10397","repositories_listed":1,"syntology":null},{"url":"/paper/a-theoretical-framework-for-reservoir","slug":"a-theoretical-framework-for-reservoir","title":"A theoretical framework for reservoir computing on networks of organic electrochemical transistors","date":"2024-08-17","arxiv_id":"2408.09223","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-kan-introducing-karsein-for-adaptive","slug":"beyond-kan-introducing-karsein-for-adaptive","title":"CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling","date":"2024-08-16","arxiv_id":"2408.08713","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-autoregressive-audio-modeling-via","slug":"efficient-autoregressive-audio-modeling-via","title":"Efficient Autoregressive Audio Modeling via Next-Scale Prediction","date":"2024-08-16","arxiv_id":"2408.09027","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/efficient-autoregressive-audio-modeling-via#ran","syntology_url":"https://syntology.ai/paper/2408.09027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.09027"}},"official":{"repos":["qiuk2/aar"],"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/emodynamix-emotional-support-dialogue","slug":"emodynamix-emotional-support-dialogue","title":"EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics","date":"2024-08-16","arxiv_id":"2408.08782","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-polygonal-footprints-in-off-nadir","slug":"extracting-polygonal-footprints-in-off-nadir","title":"Extracting polygonal footprints in off-nadir images with Segment Anything Model","date":"2024-08-16","arxiv_id":"2408.08645","repositories_listed":1,"syntology":null},{"url":"/paper/opencity-open-spatio-temporal-foundation","slug":"opencity-open-spatio-temporal-foundation","title":"OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction","date":"2024-08-16","arxiv_id":"2408.10269","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/opencity-open-spatio-temporal-foundation#ran","syntology_url":"https://syntology.ai/paper/2408.10269","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.10269"}},"official":{"repos":["hkuds/opencity"],"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/predicting-the-genetic-component-of-gene","slug":"predicting-the-genetic-component-of-gene","title":"Predicting the genetic component of gene expression using gene regulatory networks","date":"2024-08-16","arxiv_id":"2408.08530","repositories_listed":1,"syntology":null},{"url":"/paper/se-sgformer-a-self-explainable-signed-graph","slug":"se-sgformer-a-self-explainable-signed-graph","title":"Self-Explainable Graph Transformer for Link Sign Prediction","date":"2024-08-16","arxiv_id":"2408.08754","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":9,"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) · 6 unverified","sample_list":"/paper/se-sgformer-a-self-explainable-signed-graph#ran","syntology_url":"https://syntology.ai/paper/2408.08754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.08754"}},"official":{"repos":["liule66/SE-SGformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/tamer-tree-aware-transformer-for-handwritten","slug":"tamer-tree-aware-transformer-for-handwritten","title":"TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition","date":"2024-08-16","arxiv_id":"2408.08578","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":7,"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/tamer-tree-aware-transformer-for-handwritten#ran","syntology_url":"https://syntology.ai/paper/2408.08578","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.08578"}},"official":{"repos":["qingzhenduyu/tamer"],"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/imgcn-interpretable-masked-graph-convolution","slug":"imgcn-interpretable-masked-graph-convolution","title":"IMGCN: Interpretable Masked Graph Convolution Network for Pedestrian Trajectory Prediction","date":"2024-08-13","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unified-iou-for-high-quality-object-detection","slug":"unified-iou-for-high-quality-object-detection","title":"Unified-IoU: For High-Quality Object Detection","date":"2024-08-13","arxiv_id":"2408.06636","repositories_listed":1,"syntology":null},{"url":"/paper/attention-please-what-transformer-models","slug":"attention-please-what-transformer-models","title":"Attention Please: What Transformer Models Really Learn for Process Prediction","date":"2024-08-12","arxiv_id":"2408.07097","repositories_listed":1,"syntology":null},{"url":"/paper/pattern-matching-dynamic-memory-network-for-1","slug":"pattern-matching-dynamic-memory-network-for-1","title":"Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction","date":"2024-08-12","arxiv_id":"2408.07100","repositories_listed":1,"syntology":null},{"url":"/paper/review-driven-personalized-preference","slug":"review-driven-personalized-preference","title":"Review-driven Personalized Preference Reasoning with Large Language Models for Recommendation","date":"2024-08-12","arxiv_id":"2408.06276","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/review-driven-personalized-preference#ran","syntology_url":"https://syntology.ai/paper/2408.06276","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.06276"}},"official":{"repos":["jieyong99/exp3rt"],"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/how-does-audio-influence-visual-attention-in","slug":"how-does-audio-influence-visual-attention-in","title":"How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model","date":"2024-08-10","arxiv_id":"2408.05411","repositories_listed":1,"syntology":null},{"url":"/paper/histokernel-whole-slide-image-level-maximum","slug":"histokernel-whole-slide-image-level-maximum","title":"HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling","date":"2024-08-09","arxiv_id":"2408.05195","repositories_listed":1,"syntology":null},{"url":"/paper/learning-rule-induced-subgraph-1","slug":"learning-rule-induced-subgraph-1","title":"Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction","date":"2024-08-09","arxiv_id":"2408.07088","repositories_listed":1,"syntology":null},{"url":"/paper/performative-prediction-on-games-and","slug":"performative-prediction-on-games-and","title":"Performative Prediction on Games and Mechanism Design","date":"2024-08-09","arxiv_id":"2408.05146","repositories_listed":1,"syntology":null},{"url":"/paper/crowd-intelligence-for-early-misinformation","slug":"crowd-intelligence-for-early-misinformation","title":"Crowd Intelligence for Early Misinformation Prediction on Social Media","date":"2024-08-08","arxiv_id":"2408.04463","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generative-models-for-subgraph","slug":"deep-generative-models-for-subgraph","title":"Deep Generative Models for Subgraph Prediction","date":"2024-08-07","arxiv_id":"2408.04053","repositories_listed":1,"syntology":null},{"url":"/paper/effect-of-kernel-size-on-cnn-vision","slug":"effect-of-kernel-size-on-cnn-vision","title":"Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data","date":"2024-08-06","arxiv_id":"2408.03478","repositories_listed":1,"syntology":null},{"url":"/paper/2408-02792","slug":"2408-02792","title":"Lesion Elevation Prediction from Skin Images Improves Diagnosis","date":"2024-08-05","arxiv_id":"2408.02792","repositories_listed":1,"syntology":null},{"url":"/paper/rica-2-rubric-informed-calibrated-assessment","slug":"rica-2-rubric-informed-calibrated-assessment","title":"RICA2: Rubric-Informed, Calibrated Assessment of Actions","date":"2024-08-04","arxiv_id":"2408.02138","repositories_listed":1,"syntology":null},{"url":"/paper/2408-01163","slug":"2408-01163","title":"Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery","date":"2024-08-02","arxiv_id":"2408.01163","repositories_listed":1,"syntology":null},{"url":"/paper/2408-00374","slug":"2408-00374","title":"Conformal Trajectory Prediction with Multi-View Data Integration in Cooperative Driving","date":"2024-08-01","arxiv_id":"2408.00374","repositories_listed":1,"syntology":null},{"url":"/paper/safety-critical-control-with-offline-online","slug":"safety-critical-control-with-offline-online","title":"Safety-Critical Control with Offline-Online Neural Network Inference","date":"2024-08-01","arxiv_id":"2408.00918","repositories_listed":1,"syntology":null},{"url":"/paper/2407-21310","slug":"2407-21310","title":"MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration","date":"2024-07-31","arxiv_id":"2407.21310","repositories_listed":1,"syntology":null},{"url":"/paper/mart-multiscale-relational-transformer","slug":"mart-multiscale-relational-transformer","title":"MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction","date":"2024-07-31","arxiv_id":"2407.21635","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-syntax-score-prediction-benchmark","slug":"end-to-end-syntax-score-prediction-benchmark","title":"CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method","date":"2024-07-29","arxiv_id":"2407.19894","repositories_listed":1,"syntology":null},{"url":"/paper/knowcomp-pokemon-team-at-dialam-2024-a-two","slug":"knowcomp-pokemon-team-at-dialam-2024-a-two","title":"KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining","date":"2024-07-29","arxiv_id":"2407.19740","repositories_listed":1,"syntology":null},{"url":"/paper/salnas-efficient-saliency-prediction-neural","slug":"salnas-efficient-saliency-prediction-neural","title":"SalNAS: Efficient Saliency-prediction Neural Architecture Search with self-knowledge distillation","date":"2024-07-29","arxiv_id":"2407.20062","repositories_listed":1,"syntology":null},{"url":"/paper/forecast-peft-parameter-efficient-fine-tuning","slug":"forecast-peft-parameter-efficient-fine-tuning","title":"Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models","date":"2024-07-28","arxiv_id":"2407.19564","repositories_listed":1,"syntology":null},{"url":"/paper/efficiently-improving-key-weather-variables","slug":"efficiently-improving-key-weather-variables","title":"Efficiently improving key weather variables forecasting by performing the guided iterative prediction in latent space","date":"2024-07-27","arxiv_id":"2407.19187","repositories_listed":1,"syntology":null},{"url":"/paper/multi-agent-trajectory-prediction-with-1","slug":"multi-agent-trajectory-prediction-with-1","title":"Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network","date":"2024-07-26","arxiv_id":"2407.18551","repositories_listed":1,"syntology":null},{"url":"/paper/asep-benchmarking-deep-learning-methods-for","slug":"asep-benchmarking-deep-learning-methods-for","title":"AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction","date":"2024-07-25","arxiv_id":"2407.18184","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-data-labeling-for-software","slug":"automatic-data-labeling-for-software","title":"Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?","date":"2024-07-25","arxiv_id":"2407.17803","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-multi-task-learning-for","slug":"context-aware-multi-task-learning-for","title":"Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction","date":"2024-07-24","arxiv_id":"2407.17162","repositories_listed":1,"syntology":null},{"url":"/paper/m4-multi-proxy-multi-gate-mixture-of-experts","slug":"m4-multi-proxy-multi-gate-mixture-of-experts","title":"M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis","date":"2024-07-24","arxiv_id":"2407.17267","repositories_listed":1,"syntology":null},{"url":"/paper/sepsislab-early-sepsis-prediction-with","slug":"sepsislab-early-sepsis-prediction-with","title":"SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing","date":"2024-07-24","arxiv_id":"2407.16999","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-thresholded-intervals-for-efficient","slug":"conformal-thresholded-intervals-for-efficient","title":"Conformal Thresholded Intervals for Efficient Regression","date":"2024-07-19","arxiv_id":"2407.14495","repositories_listed":1,"syntology":null}],"record_sha256":"7b73e8ea8dcaab001d979b662de1ee070f5e0360f209e5647fcc2692e3496711","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}