{"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/16","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":16,"pages_in_order":88,"rows_per_page":100,"rows":[1501,1600],"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/15","next":"/task/prediction/papers/17","papers":[{"url":"/paper/explainablefold-understanding-alphafold","slug":"explainablefold-understanding-alphafold","title":"ExplainableFold: Understanding AlphaFold Prediction with Explainable AI","date":"2023-01-27","arxiv_id":"2301.11765","repositories_listed":1,"syntology":null},{"url":"/paper/dse-stock-price-prediction-using-hidden","slug":"dse-stock-price-prediction-using-hidden","title":"DSE Stock Price Prediction using Hidden Markov Model","date":"2023-01-26","arxiv_id":"2302.08911","repositories_listed":1,"syntology":null},{"url":"/paper/efficiently-predicting-high-resolution-mass","slug":"efficiently-predicting-high-resolution-mass","title":"Efficiently predicting high resolution mass spectra with graph neural networks","date":"2023-01-26","arxiv_id":"2301.11419","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-feature-set-for-click-through-rate","slug":"optimizing-feature-set-for-click-through-rate","title":"Optimizing Feature Set for Click-Through Rate Prediction","date":"2023-01-26","arxiv_id":"2301.10909","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/optimizing-feature-set-for-click-through-rate#ran","syntology_url":"https://syntology.ai/paper/2301.10909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10909"}},"official":{"repos":["fuyuanlyu/optfs"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-protein-ligand-binding-energy","slug":"unsupervised-protein-ligand-binding-energy","title":"Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation","date":"2023-01-25","arxiv_id":"2301.10814","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unsupervised-protein-ligand-binding-energy#ran","syntology_url":"https://syntology.ai/paper/2301.10814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10814"}},"official":{"repos":["wengong-jin/dsmbind"],"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/energy-prediction-using-federated-learning","slug":"energy-prediction-using-federated-learning","title":"Energy Prediction using Federated Learning","date":"2023-01-22","arxiv_id":"2301.09165","repositories_listed":1,"syntology":null},{"url":"/paper/explainable-multilayer-graph-neural-network","slug":"explainable-multilayer-graph-neural-network","title":"Explainable Multilayer Graph Neural Network for Cancer Gene Prediction","date":"2023-01-20","arxiv_id":"2301.08831","repositories_listed":1,"syntology":null},{"url":"/paper/causal-conditional-hidden-markov-model-for","slug":"causal-conditional-hidden-markov-model-for","title":"Causal conditional hidden Markov model for multimodal traffic prediction","date":"2023-01-19","arxiv_id":"2301.08249","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":8,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"11 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/causal-conditional-hidden-markov-model-for#ran","syntology_url":"https://syntology.ai/paper/2301.08249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.08249"}},"official":{"repos":["eternityzy/cchmm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":8,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pdformer-propagation-delay-aware-dynamic-long","slug":"pdformer-propagation-delay-aware-dynamic-long","title":"PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction","date":"2023-01-19","arxiv_id":"2301.07945","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":6,"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 6 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; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/pdformer-propagation-delay-aware-dynamic-long#ran","syntology_url":"https://syntology.ai/paper/2301.07945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.07945"}},"official":{"repos":["BUAABIGSCity/PDFormer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/spatio-temporal-neural-structural-causal","slug":"spatio-temporal-neural-structural-causal","title":"Spatio-temporal neural structural causal models for bike flow prediction","date":"2023-01-19","arxiv_id":"2301.07843","repositories_listed":1,"syntology":null},{"url":"/paper/behind-the-scenes-density-fields-for-single","slug":"behind-the-scenes-density-fields-for-single","title":"Behind the Scenes: Density Fields for Single View Reconstruction","date":"2023-01-18","arxiv_id":"2301.07668","repositories_listed":1,"syntology":null},{"url":"/paper/mortality-prediction-with-adaptive-feature","slug":"mortality-prediction-with-adaptive-feature","title":"Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset","date":"2023-01-17","arxiv_id":"2301.07107","repositories_listed":1,"syntology":null},{"url":"/paper/outlier-based-domain-of-applicability","slug":"outlier-based-domain-of-applicability","title":"Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models","date":"2023-01-17","arxiv_id":"2302.06454","repositories_listed":1,"syntology":null},{"url":"/paper/predictive-world-models-from-real-world","slug":"predictive-world-models-from-real-world","title":"Predictive World Models from Real-World Partial Observations","date":"2023-01-12","arxiv_id":"2301.04783","repositories_listed":1,"syntology":null},{"url":"/paper/tinyhd-efficient-video-saliency-prediction","slug":"tinyhd-efficient-video-saliency-prediction","title":"TinyHD: Efficient Video Saliency Prediction with Heterogeneous Decoders using Hierarchical Maps Distillation","date":"2023-01-11","arxiv_id":"2301.04619","repositories_listed":1,"syntology":null},{"url":"/paper/excelformer-a-neural-network-surpassing-gbdts","slug":"excelformer-a-neural-network-surpassing-gbdts","title":"ExcelFormer: A neural network surpassing GBDTs on tabular data","date":"2023-01-07","arxiv_id":"2301.02819","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/excelformer-a-neural-network-surpassing-gbdts#ran","syntology_url":"https://syntology.ai/paper/2301.02819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.02819"}},"official":{"repos":["whatashot/excelformer"],"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/imkga-sm-interpretable-multimodal-knowledge","slug":"imkga-sm-interpretable-multimodal-knowledge","title":"IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling","date":"2023-01-06","arxiv_id":"2301.02445","repositories_listed":1,"syntology":null},{"url":"/paper/multi-vehicle-trajectory-prediction-at","slug":"multi-vehicle-trajectory-prediction-at","title":"Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information","date":"2023-01-06","arxiv_id":"2301.02561","repositories_listed":1,"syntology":null},{"url":"/paper/tempsal-uncovering-temporal-information-for","slug":"tempsal-uncovering-temporal-information-for","title":"TempSAL -- Uncovering Temporal Information for Deep Saliency Prediction","date":"2023-01-05","arxiv_id":"2301.02315","repositories_listed":1,"syntology":null},{"url":"/paper/multi-aspect-explainable-inductive-relation","slug":"multi-aspect-explainable-inductive-relation","title":"Multi-Aspect Explainable Inductive Relation Prediction by Sentence Transformer","date":"2023-01-04","arxiv_id":"2301.01664","repositories_listed":1,"syntology":null},{"url":"/paper/efficientvit-lightweight-multi-scale","slug":"efficientvit-lightweight-multi-scale","title":"EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/int2-interactive-trajectory-prediction-at","slug":"int2-interactive-trajectory-prediction-at","title":"INT2: Interactive Trajectory Prediction at Intersections","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/modular-memorability-tiered-representations","slug":"modular-memorability-tiered-representations","title":"Modular Memorability: Tiered Representations for Video Memorability Prediction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-logit-distillation","slug":"multi-level-logit-distillation","title":"Multi-Level Logit Distillation","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/query-centric-trajectory-prediction","slug":"query-centric-trajectory-prediction","title":"Query-Centric Trajectory Prediction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/scandmm-a-deep-markov-model-of-scanpath","slug":"scandmm-a-deep-markov-model-of-scanpath","title":"ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-semantics-guided-adversarial-1","slug":"semi-supervised-semantics-guided-adversarial-1","title":"Semi-supervised Semantics-guided Adversarial Training for Robust Trajectory Prediction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/swinlstm-improving-spatiotemporal-prediction-1","slug":"swinlstm-improving-spatiotemporal-prediction-1","title":"SwinLSTM: Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/tempsal-uncovering-temporal-information-for-1","slug":"tempsal-uncovering-temporal-information-for-1","title":"TempSAL - Uncovering Temporal Information for Deep Saliency Prediction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/trajectory-unified-transformer-for-pedestrian","slug":"trajectory-unified-transformer-for-pedestrian","title":"Trajectory Unified Transformer for Pedestrian Trajectory Prediction","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generative-graph-neural-networks-for-link","slug":"generative-graph-neural-networks-for-link","title":"Generative Graph Neural Networks for Link Prediction","date":"2022-12-31","arxiv_id":"2301.00169","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-intervals-for-remaining","slug":"conformal-prediction-intervals-for-remaining","title":"Conformal Prediction Intervals for Remaining Useful Lifetime Estimation","date":"2022-12-30","arxiv_id":"2212.14612","repositories_listed":1,"syntology":null},{"url":"/paper/from-single-visit-to-multi-visit-image-based","slug":"from-single-visit-to-multi-visit-image-based","title":"From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis","date":"2022-12-27","arxiv_id":"2212.13535","repositories_listed":1,"syntology":null},{"url":"/paper/multi-step-ahead-stock-price-prediction-using","slug":"multi-step-ahead-stock-price-prediction-using","title":"Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition","date":"2022-12-24","arxiv_id":"2212.14687","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-human-motion-prediction-for","slug":"few-shot-human-motion-prediction-for","title":"Few-shot human motion prediction for heterogeneous sensors","date":"2022-12-22","arxiv_id":"2212.11771","repositories_listed":1,"syntology":null},{"url":"/paper/on-calibrating-semantic-segmentation-models","slug":"on-calibrating-semantic-segmentation-models","title":"On Calibrating Semantic Segmentation Models: Analyses and An Algorithm","date":"2022-12-22","arxiv_id":"2212.12053","repositories_listed":1,"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/on-calibrating-semantic-segmentation-models#ran","syntology_url":"https://syntology.ai/paper/2212.12053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.12053"}},"official":{"repos":["dwang181/selectivecal"],"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/a-retrieve-and-read-framework-for-knowledge","slug":"a-retrieve-and-read-framework-for-knowledge","title":"A Retrieve-and-Read Framework for Knowledge Graph Link Prediction","date":"2022-12-19","arxiv_id":"2212.09724","repositories_listed":1,"syntology":null},{"url":"/paper/cognitive-accident-prediction-in-driving","slug":"cognitive-accident-prediction-in-driving","title":"Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark","date":"2022-12-19","arxiv_id":"2212.09381","repositories_listed":1,"syntology":null},{"url":"/paper/rich-event-modeling-for-script-event","slug":"rich-event-modeling-for-script-event","title":"Rich Event Modeling for Script Event Prediction","date":"2022-12-16","arxiv_id":"2212.08287","repositories_listed":1,"syntology":null},{"url":"/paper/attention-based-multiple-instance-learning-2","slug":"attention-based-multiple-instance-learning-2","title":"Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays","date":"2022-12-15","arxiv_id":"2212.07724","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-labelling-and-dnabert-for-splice","slug":"sequential-labelling-and-dnabert-for-splice","title":"Sequential Labelling and DNABERT For Splice Site Prediction in Homo Sapiens DNA","date":"2022-12-15","arxiv_id":"2212.07638","repositories_listed":1,"syntology":null},{"url":"/paper/interactive-concept-bottleneck-models","slug":"interactive-concept-bottleneck-models","title":"Interactive Concept Bottleneck Models","date":"2022-12-14","arxiv_id":"2212.07430","repositories_listed":1,"syntology":null},{"url":"/paper/video-prediction-by-efficient-transformers","slug":"video-prediction-by-efficient-transformers","title":"Video Prediction by Efficient Transformers","date":"2022-12-12","arxiv_id":"2212.06026","repositories_listed":1,"syntology":{"n":7,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 7 unverified","sample_list":"/paper/video-prediction-by-efficient-transformers#ran","syntology_url":"https://syntology.ai/paper/2212.06026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06026"}},"official":{"repos":["xiye20/vptr"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":[]}}},{"url":"/paper/mimo-is-all-you-need-a-strong-multi-in-multi","slug":"mimo-is-all-you-need-a-strong-multi-in-multi","title":"MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction","date":"2022-12-09","arxiv_id":"2212.04655","repositories_listed":1,"syntology":null},{"url":"/paper/a-generative-approach-for-script-event-1","slug":"a-generative-approach-for-script-event-1","title":"A Generative Approach for Script Event Prediction via Contrastive Fine-tuning","date":"2022-12-07","arxiv_id":"2212.03496","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-predictive-conformal-inference-for","slug":"sequential-predictive-conformal-inference-for","title":"Sequential Predictive Conformal Inference for Time Series","date":"2022-12-07","arxiv_id":"2212.03463","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-self-supervised-learning-for","slug":"spatio-temporal-self-supervised-learning-for","title":"Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction","date":"2022-12-07","arxiv_id":"2212.04475","repositories_listed":1,"syntology":null},{"url":"/paper/towards-explainable-motion-prediction-using","slug":"towards-explainable-motion-prediction-using","title":"Towards Explainable Motion Prediction using Heterogeneous Graph Representations","date":"2022-12-07","arxiv_id":"2212.03806","repositories_listed":1,"syntology":null},{"url":"/paper/copula-conformal-prediction-for-multi-step","slug":"copula-conformal-prediction-for-multi-step","title":"Copula Conformal Prediction for Multi-step Time Series Forecasting","date":"2022-12-06","arxiv_id":"2212.03281","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/copula-conformal-prediction-for-multi-step#ran","syntology_url":"https://syntology.ai/paper/2212.03281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.03281"}},"official":{"repos":["rose-stl-lab/copulacpts"],"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/domain-generalization-strategy-to-train","slug":"domain-generalization-strategy-to-train","title":"Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift","date":"2022-12-06","arxiv_id":"2212.02968","repositories_listed":1,"syntology":null},{"url":"/paper/super-resolution-probabilistic-rain","slug":"super-resolution-probabilistic-rain","title":"Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormers","date":"2022-12-06","arxiv_id":"2212.02998","repositories_listed":1,"syntology":{"n":20,"n_ran":18,"n_constructed":0,"n_ran_checked":15,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/super-resolution-probabilistic-rain#ran","syntology_url":"https://syntology.ai/paper/2212.02998","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.02998"}},"official":{"repos":["bugsuse/weather4cast-2022-stage2"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/lightweight-facial-attractiveness-prediction","slug":"lightweight-facial-attractiveness-prediction","title":"Lightweight Facial Attractiveness Prediction Using Dual Label Distribution","date":"2022-12-04","arxiv_id":"2212.01742","repositories_listed":1,"syntology":null},{"url":"/paper/rfold-towards-simple-yet-effective-rna","slug":"rfold-towards-simple-yet-effective-rna","title":"Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective","date":"2022-12-02","arxiv_id":"2212.14041","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":5,"n_pointer_only":16,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 1 violated, 5 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/rfold-towards-simple-yet-effective-rna#ran","syntology_url":"https://syntology.ai/paper/2212.14041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.14041"}},"official":{"repos":["a4bio/rfold"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/cl4ctr-a-contrastive-learning-framework-for","slug":"cl4ctr-a-contrastive-learning-framework-for","title":"CL4CTR: A Contrastive Learning Framework for CTR Prediction","date":"2022-12-01","arxiv_id":"2212.00522","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-selective-similarity-integration","slug":"fine-grained-selective-similarity-integration","title":"Fine-Grained Selective Similarity Integration for Drug-Target Interaction Prediction","date":"2022-12-01","arxiv_id":"2212.00543","repositories_listed":1,"syntology":null},{"url":"/paper/boosted-dynamic-neural-networks","slug":"boosted-dynamic-neural-networks","title":"Boosted Dynamic Neural Networks","date":"2022-11-30","arxiv_id":"2211.16726","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/boosted-dynamic-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2211.16726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.16726"}},"official":{"repos":["SHI-Labs/Boosted-Dynamic-Networks"],"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/convolutional-proteinunetlm-competitive-with","slug":"convolutional-proteinunetlm-competitive-with","title":"Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors","date":"2022-11-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/protein-language-models-and-structure","slug":"protein-language-models-and-structure","title":"Protein Language Models and Structure Prediction: Connection and Progression","date":"2022-11-30","arxiv_id":"2211.16742","repositories_listed":1,"syntology":null},{"url":"/paper/an-extreme-adaptive-time-series-prediction","slug":"an-extreme-adaptive-time-series-prediction","title":"An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks","date":"2022-11-29","arxiv_id":"2211.15891","repositories_listed":1,"syntology":null},{"url":"/paper/cross-project-defect-prediction-with-an","slug":"cross-project-defect-prediction-with-an","title":"Cross-project Defect Prediction with An Enhanced Transfer Boosting Algorithm","date":"2022-11-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fakeedge-alleviate-dataset-shift-in-link","slug":"fakeedge-alleviate-dataset-shift-in-link","title":"FakeEdge: Alleviate Dataset Shift in Link Prediction","date":"2022-11-29","arxiv_id":"2211.15899","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-free-prediction-sets-for-node","slug":"distribution-free-prediction-sets-for-node","title":"Distribution Free Prediction Sets for Node Classification","date":"2022-11-26","arxiv_id":"2211.14555","repositories_listed":1,"syntology":null},{"url":"/paper/link-prediction-with-non-contrastive-learning","slug":"link-prediction-with-non-contrastive-learning","title":"Link Prediction with Non-Contrastive Learning","date":"2022-11-25","arxiv_id":"2211.14394","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/link-prediction-with-non-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2211.14394","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.14394"}},"official":{"repos":["snap-research/non-contrastive-link-prediction"],"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/analysis-on-english-vocabulary-appearance","slug":"analysis-on-english-vocabulary-appearance","title":"AI Knows Which Words Will Appear in Next Year's Korean CSAT","date":"2022-11-24","arxiv_id":"2211.15426","repositories_listed":1,"syntology":null},{"url":"/paper/lifting-weak-supervision-to-structured","slug":"lifting-weak-supervision-to-structured","title":"Lifting Weak Supervision To Structured Prediction","date":"2022-11-24","arxiv_id":"2211.13375","repositories_listed":1,"syntology":null},{"url":"/paper/search-behavior-prediction-a-hypergraph","slug":"search-behavior-prediction-a-hypergraph","title":"Search Behavior Prediction: A Hypergraph Perspective","date":"2022-11-23","arxiv_id":"2211.13328","repositories_listed":1,"syntology":null},{"url":"/paper/heterogenous-ensemble-of-models-for-molecular","slug":"heterogenous-ensemble-of-models-for-molecular","title":"Heterogenous Ensemble of Models for Molecular Property Prediction","date":"2022-11-20","arxiv_id":"2211.11035","repositories_listed":1,"syntology":null},{"url":"/paper/patch-level-gaze-distribution-prediction-for","slug":"patch-level-gaze-distribution-prediction-for","title":"Patch-level Gaze Distribution Prediction for Gaze Following","date":"2022-11-20","arxiv_id":"2211.11062","repositories_listed":1,"syntology":null},{"url":"/paper/reinform-selecting-paths-with-reinforcement","slug":"reinform-selecting-paths-with-reinforcement","title":"ReInform: Selecting paths with reinforcement learning for contextualized link prediction","date":"2022-11-19","arxiv_id":"2211.10688","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-multi-stream-information-fusion","slug":"leveraging-multi-stream-information-fusion","title":"Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach","date":"2022-11-18","arxiv_id":"2211.10226","repositories_listed":1,"syntology":null},{"url":"/paper/caspr-customer-activity-sequence-based","slug":"caspr-customer-activity-sequence-based","title":"CASPR: Customer Activity Sequence-based Prediction and Representation","date":"2022-11-16","arxiv_id":"2211.09174","repositories_listed":1,"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":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/caspr-customer-activity-sequence-based#ran","syntology_url":"https://syntology.ai/paper/2211.09174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09174"}},"official":{"repos":["microsoft/caspr"],"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/graph-sequential-neural-ode-process-for-link","slug":"graph-sequential-neural-ode-process-for-link","title":"Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs","date":"2022-11-15","arxiv_id":"2211.08568","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/graph-sequential-neural-ode-process-for-link#ran","syntology_url":"https://syntology.ai/paper/2211.08568","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.08568"}},"official":{"repos":["rmanluo/gsnop"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hgv4risk-hierarchical-global-view-guided","slug":"hgv4risk-hierarchical-global-view-guided","title":"HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction","date":"2022-11-15","arxiv_id":"2211.07956","repositories_listed":1,"syntology":null},{"url":"/paper/influencer-detection-with-dynamic-graph","slug":"influencer-detection-with-dynamic-graph","title":"Influencer Detection with Dynamic Graph Neural Networks","date":"2022-11-15","arxiv_id":"2211.09664","repositories_listed":1,"syntology":null},{"url":"/paper/advancing-learned-video-compression-with-in","slug":"advancing-learned-video-compression-with-in","title":"Advancing Learned Video Compression with In-loop Frame Prediction","date":"2022-11-13","arxiv_id":"2211.07004","repositories_listed":1,"syntology":null},{"url":"/paper/accounting-for-temporal-variability-in","slug":"accounting-for-temporal-variability-in","title":"Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence","date":"2022-11-11","arxiv_id":"2211.07429","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-visual-commonsense-immorality","slug":"zero-shot-visual-commonsense-immorality","title":"Zero-shot Visual Commonsense Immorality Prediction","date":"2022-11-10","arxiv_id":"2211.05521","repositories_listed":1,"syntology":null},{"url":"/paper/a-note-on-task-aware-loss-via-reweighing","slug":"a-note-on-task-aware-loss-via-reweighing","title":"A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret","date":"2022-11-09","arxiv_id":"2211.05116","repositories_listed":1,"syntology":null},{"url":"/paper/algorithmic-bias-in-machine-learning-based","slug":"algorithmic-bias-in-machine-learning-based","title":"Algorithmic Bias in Machine Learning Based Delirium Prediction","date":"2022-11-08","arxiv_id":"2211.04442","repositories_listed":1,"syntology":null},{"url":"/paper/hyperparameter-optimization-in-deep-multi","slug":"hyperparameter-optimization-in-deep-multi","title":"Hyperparameter optimization in deep multi-target prediction","date":"2022-11-08","arxiv_id":"2211.04362","repositories_listed":1,"syntology":null},{"url":"/paper/materials-property-prediction-with","slug":"materials-property-prediction-with","title":"Materials Property Prediction with Uncertainty Quantification: A Benchmark Study","date":"2022-11-04","arxiv_id":"2211.02235","repositories_listed":1,"syntology":null},{"url":"/paper/rcdpt-radar-camera-fusion-dense-prediction","slug":"rcdpt-radar-camera-fusion-dense-prediction","title":"RCDPT: Radar-Camera fusion Dense Prediction Transformer","date":"2022-11-04","arxiv_id":"2211.02432","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-the-positive-role-of-cluster","slug":"rethinking-the-positive-role-of-cluster","title":"Rethinking the positive role of cluster structure in complex networks for link prediction tasks","date":"2022-11-04","arxiv_id":"2211.02396","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-bayes-analysis-of-vehicle","slug":"an-empirical-bayes-analysis-of-vehicle","title":"An Empirical Bayes Analysis of Object Trajectory Representation Models","date":"2022-11-03","arxiv_id":"2211.01696","repositories_listed":1,"syntology":null},{"url":"/paper/open-vocabulary-argument-role-prediction-for","slug":"open-vocabulary-argument-role-prediction-for","title":"Open-Vocabulary Argument Role Prediction for Event Extraction","date":"2022-11-03","arxiv_id":"2211.01577","repositories_listed":1,"syntology":null},{"url":"/paper/pixel-wise-contrastive-distillation","slug":"pixel-wise-contrastive-distillation","title":"Pixel-Wise Contrastive Distillation","date":"2022-11-01","arxiv_id":"2211.00218","repositories_listed":1,"syntology":null},{"url":"/paper/do-charge-prediction-models-learn-legal","slug":"do-charge-prediction-models-learn-legal","title":"Do Charge Prediction Models Learn Legal Theory?","date":"2022-10-31","arxiv_id":"2210.17108","repositories_listed":1,"syntology":null},{"url":"/paper/exemplar-guided-deep-neural-network-for","slug":"exemplar-guided-deep-neural-network-for","title":"Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction","date":"2022-10-30","arxiv_id":"2210.16721","repositories_listed":1,"syntology":null},{"url":"/paper/towards-trustworthy-multi-modal-motion","slug":"towards-trustworthy-multi-modal-motion","title":"Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs","date":"2022-10-28","arxiv_id":"2210.16144","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-adverse-biological-effect","slug":"understanding-adverse-biological-effect","title":"Understanding Adverse Biological Effect Predictions Using Knowledge Graphs","date":"2022-10-28","arxiv_id":"2210.15985","repositories_listed":1,"syntology":null},{"url":"/paper/feature-necessity-relevancy-in-ml-classifier","slug":"feature-necessity-relevancy-in-ml-classifier","title":"Feature Necessity & Relevancy in ML Classifier Explanations","date":"2022-10-27","arxiv_id":"2210.15675","repositories_listed":1,"syntology":null},{"url":"/paper/autoregressive-structured-prediction-with","slug":"autoregressive-structured-prediction-with","title":"Autoregressive Structured Prediction with Language Models","date":"2022-10-26","arxiv_id":"2210.14698","repositories_listed":1,"syntology":null},{"url":"/paper/tamformer-multi-modal-transformer-with","slug":"tamformer-multi-modal-transformer-with","title":"TAMFormer: Multi-Modal Transformer with Learned Attention Mask for Early Intent Prediction","date":"2022-10-26","arxiv_id":"2210.14714","repositories_listed":1,"syntology":null},{"url":"/paper/fusing-modalities-by-multiplexed-graph-neural","slug":"fusing-modalities-by-multiplexed-graph-neural","title":"Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis","date":"2022-10-25","arxiv_id":"2210.14377","repositories_listed":1,"syntology":null},{"url":"/paper/line-graph-contrastive-learning-for-link","slug":"line-graph-contrastive-learning-for-link","title":"Line Graph Contrastive Learning for Link Prediction","date":"2022-10-25","arxiv_id":"2210.13795","repositories_listed":1,"syntology":null},{"url":"/paper/prediction-intervals-for-economic-fixed-event","slug":"prediction-intervals-for-economic-fixed-event","title":"Prediction intervals for economic fixed-event forecasts","date":"2022-10-24","arxiv_id":"2210.13562","repositories_listed":1,"syntology":null},{"url":"/paper/what-cleaves-is-proteasomal-cleavage","slug":"what-cleaves-is-proteasomal-cleavage","title":"What cleaves? Is proteasomal cleavage prediction reaching a ceiling?","date":"2022-10-24","arxiv_id":"2210.12991","repositories_listed":1,"syntology":null},{"url":"/paper/code4struct-code-generation-for-few-shot","slug":"code4struct-code-generation-for-few-shot","title":"Code4Struct: Code Generation for Few-Shot Event Structure Prediction","date":"2022-10-23","arxiv_id":"2210.12810","repositories_listed":1,"syntology":null},{"url":"/paper/algorithms-with-prediction-portfolios","slug":"algorithms-with-prediction-portfolios","title":"Algorithms with Prediction Portfolios","date":"2022-10-22","arxiv_id":"2210.12438","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/algorithms-with-prediction-portfolios#ran","syntology_url":"https://syntology.ai/paper/2210.12438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12438"}},"official":{"repos":["tlavastida/predictionportfolios"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/bayesian-optimization-with-conformal-coverage","slug":"bayesian-optimization-with-conformal-coverage","title":"Bayesian Optimization with Conformal Prediction Sets","date":"2022-10-22","arxiv_id":"2210.12496","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bayesian-optimization-with-conformal-coverage#ran","syntology_url":"https://syntology.ai/paper/2210.12496","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12496"}},"official":{"repos":["samuelstanton/conformal-bayesopt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"e02f1643a67ce221add54963a0eb9730384cc34a70d9e671d5b180e7809d0bae","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}