{"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/33","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":33,"pages_in_order":88,"rows_per_page":100,"rows":[3201,3300],"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/32","next":"/task/prediction/papers/34","papers":[{"url":null,"slug":"on-multi-token-prediction-for-efficient-llm","title":"On multi-token prediction for efficient LLM inference","date":"2025-02-13","arxiv_id":"2502.09419","repositories_listed":0,"syntology":null},{"url":null,"slug":"relational-conformal-prediction-for","title":"Relational Conformal Prediction for Correlated Time Series","date":"2025-02-13","arxiv_id":"2502.09443","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-enhanced-variational-autoencoder","title":"Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction","date":"2025-02-13","arxiv_id":"2502.09423","repositories_listed":0,"syntology":null},{"url":null,"slug":"auction-design-using-value-prediction-with","title":"Auction Design using Value Prediction with Hallucinations","date":"2025-02-12","arxiv_id":"2502.08792","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-cardiac-arrest-prediction-in-icu","title":"Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model","date":"2025-02-12","arxiv_id":"2502.08612","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-pretraining-with-continuous-concepts","title":"LLM Pretraining with Continuous Concepts","date":"2025-02-12","arxiv_id":"2502.08524","repositories_listed":0,"syntology":null},{"url":null,"slug":"poly-autoregressive-prediction-for-modeling","title":"Poly-Autoregressive Prediction for Modeling Interactions","date":"2025-02-12","arxiv_id":"2502.08646","repositories_listed":0,"syntology":null},{"url":null,"slug":"trend-encoded-probabilistic-multi-order-model","title":"Trend-encoded Probabilistic Multi-order Model: A Non-Machine Learning Approach for Enhanced Stock Market Forecasts","date":"2025-02-12","arxiv_id":"2502.08144","repositories_listed":0,"syntology":null},{"url":null,"slug":"crime-forecasting-a-spatio-temporal-analysis","title":"Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models","date":"2025-02-11","arxiv_id":"2502.07465","repositories_listed":0,"syntology":null},{"url":null,"slug":"making-language-models-robust-against","title":"Making Language Models Robust Against Negation","date":"2025-02-11","arxiv_id":"2502.07717","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-weight-averaging-for-stable-prediction","title":"Sample Weight Averaging for Stable Prediction","date":"2025-02-11","arxiv_id":"2502.07414","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-regions-are-imprecise","title":"Conformal Prediction Regions are Imprecise Highest Density Regions","date":"2025-02-10","arxiv_id":"2502.06331","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-distribution-prediction-a-unified","title":"Generative Distribution Prediction: A Unified Approach to Multimodal Learning","date":"2025-02-10","arxiv_id":"2502.07090","repositories_listed":0,"syntology":null},{"url":null,"slug":"hygen-regularizing-negative-hyperedge","title":"HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction","date":"2025-02-09","arxiv_id":"2502.05827","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-for-drug-drug-interaction-prediction-a","title":"LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison","date":"2025-02-09","arxiv_id":"2502.06890","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-based-conformal-prediction-for-multi","title":"Flow-based Conformal Prediction for Multi-dimensional Time Series","date":"2025-02-08","arxiv_id":"2502.05709","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-venn-and-venn-abers-calibration","title":"Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction","date":"2025-02-08","arxiv_id":"2502.05676","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-for-electricity-price","title":"Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market","date":"2025-02-07","arxiv_id":"2502.04935","repositories_listed":0,"syntology":null},{"url":null,"slug":"g2pdiffusion-genotype-to-phenotype-prediction","title":"G2PDiffusion: Genotype-to-Phenotype Prediction with Diffusion Models","date":"2025-02-07","arxiv_id":"2502.04684","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-conformal-prediction-for","title":"Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems","date":"2025-02-07","arxiv_id":"2502.05127","repositories_listed":0,"syntology":null},{"url":null,"slug":"cast-cross-attention-based-multimodal-fusion","title":"CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction","date":"2025-02-06","arxiv_id":"2502.06836","repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-powered-e-values","title":"Prediction-Powered E-Values","date":"2025-02-06","arxiv_id":"2502.04294","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-prediction-by-simulation-for","title":"Conditional Prediction by Simulation for Automated Driving","date":"2025-02-05","arxiv_id":"2502.03286","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-driven-student-performance","title":"Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction","date":"2025-02-05","arxiv_id":"2502.03143","repositories_listed":0,"syntology":null},{"url":"/paper/mol-llm-generalist-molecular-llm-with","slug":"mol-llm-generalist-molecular-llm-with","title":"Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization","date":"2025-02-05","arxiv_id":"2502.02810","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-conformal-prediction-using","title":"Multivariate Conformal Prediction using Optimal Transport","date":"2025-02-05","arxiv_id":"2502.03609","repositories_listed":0,"syntology":null},{"url":null,"slug":"orderfusion-encoding-orderbook-for","title":"OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Prediction","date":"2025-02-05","arxiv_id":"2502.06830","repositories_listed":0,"syntology":null},{"url":null,"slug":"causally-informed-deep-learning-towards","title":"Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care","date":"2025-02-04","arxiv_id":"2502.02109","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-belief-updates-explain-geometric","title":"Constrained belief updates explain geometric structures in transformer representations","date":"2025-02-04","arxiv_id":"2502.01954","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-theoretic-foundations-for-conformal","title":"Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents","date":"2025-02-04","arxiv_id":"2502.02561","repositories_listed":0,"syntology":null},{"url":null,"slug":"fab-ppi-frequentist-assisted-by-bayes","title":"FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference","date":"2025-02-04","arxiv_id":"2502.02363","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairness-in-survival-analysis-a-novel","title":"Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach","date":"2025-02-04","arxiv_id":"2502.02567","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-bias-of-next-token-prediction","title":"Reasoning Bias of Next Token Prediction Training","date":"2025-02-04","arxiv_id":"2502.02007","repositories_listed":0,"syntology":null},{"url":null,"slug":"regnet-reciprocal-space-aware-long-range","title":"ReGNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction","date":"2025-02-04","arxiv_id":"2502.02748","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-timesteps-samplers-and-prediction","title":"Rethinking Timesteps Samplers and Prediction Types","date":"2025-02-04","arxiv_id":"2502.01990","repositories_listed":0,"syntology":null},{"url":null,"slug":"docking-aware-attention-dynamic-protein","title":"Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration","date":"2025-02-03","arxiv_id":"2502.01461","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-partially-defer-for-sequences","title":"Learning to Partially Defer for Sequences","date":"2025-02-03","arxiv_id":"2502.01459","repositories_listed":0,"syntology":null},{"url":null,"slug":"vista-vision-text-alignment-model-with","title":"VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosis","date":"2025-02-03","arxiv_id":"2502.01535","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-informed-neural-networks-with-large","title":"Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization","date":"2025-02-02","arxiv_id":"2502.00828","repositories_listed":0,"syntology":null},{"url":null,"slug":"bcat-a-block-causal-transformer-for-pde","title":"BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics","date":"2025-01-31","arxiv_id":"2501.18972","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-in-hierarchical","title":"Conformal Prediction in Hierarchical Classification","date":"2025-01-31","arxiv_id":"2501.19038","repositories_listed":0,"syntology":null},{"url":null,"slug":"employee-turnover-prediction-a-cross","title":"Employee Turnover Prediction: A Cross-component Attention Transformer with Consideration of Competitor Influence and Contagious Effect","date":"2025-01-31","arxiv_id":"2502.01660","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hamiltonian-dynamics-with-bayesian","title":"Learning Hamiltonian Dynamics with Bayesian Data Assimilation","date":"2025-01-31","arxiv_id":"2501.18808","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-transport-based-conformal-prediction","title":"Optimal Transport-based Conformal Prediction","date":"2025-01-31","arxiv_id":"2501.18991","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-charge-location-prediction","title":"Privacy Preserving Charge Location Prediction for Electric Vehicles","date":"2025-01-31","arxiv_id":"2502.00068","repositories_listed":0,"syntology":null},{"url":"/paper/redefining-machine-unlearning-a-conformal","slug":"redefining-machine-unlearning-a-conformal","title":"Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach","date":"2025-01-31","arxiv_id":"2501.19403","repositories_listed":0,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/redefining-machine-unlearning-a-conformal#ran","syntology_url":"https://syntology.ai/paper/2501.19403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.19403"}},"official":null}},{"url":null,"slug":"the-value-of-prediction-in-identifying-the","title":"The Value of Prediction in Identifying the Worst-Off","date":"2025-01-31","arxiv_id":"2501.19334","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-large-protein-language-models-in","title":"Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark","date":"2025-01-30","arxiv_id":"2501.18223","repositories_listed":0,"syntology":null},{"url":null,"slug":"guaranteed-confidence-band-enclosures-for-pde","title":"Guaranteed confidence-band enclosures for PDE surrogates","date":"2025-01-30","arxiv_id":"2501.18426","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-concentration-levels-of-air","title":"Predicting concentration levels of air pollutants by transfer learning and recurrent neural network","date":"2025-01-30","arxiv_id":"2502.01654","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-online-conformal-prediction-under","title":"Robust Online Conformal Prediction under Uniform Label Noise","date":"2025-01-30","arxiv_id":"2501.18363","repositories_listed":0,"syntology":null},{"url":null,"slug":"stream-based-monitoring-of-algorithmic","title":"Stream-Based Monitoring of Algorithmic Fairness","date":"2025-01-30","arxiv_id":"2501.18331","repositories_listed":0,"syntology":null},{"url":null,"slug":"exact-characterization-of-e-safe-decision","title":"Exact characterization of ε-Safe Decision Regions for exponential family distributions and Multi Cost SVM approximation","date":"2025-01-29","arxiv_id":"2501.17731","repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-inter-protein-interactions-via","title":"Extracting Inter-Protein Interactions Via Multitasking Graph Structure Learning","date":"2025-01-29","arxiv_id":"2501.17589","repositories_listed":0,"syntology":null},{"url":null,"slug":"heuristic-informed-mixture-of-experts-for","title":"Heuristic-Informed Mixture of Experts for Link Prediction in Multilayer Networks","date":"2025-01-29","arxiv_id":"2501.17557","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-stage-dual-task-learning-strategy-for","title":"A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images","date":"2025-01-28","arxiv_id":"2502.00051","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-mining-in-transportation-networks-with","title":"Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook","date":"2025-01-28","arxiv_id":"2501.16656","repositories_listed":0,"syntology":null},{"url":null,"slug":"memorize-and-rank-elevating-large-language","title":"Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction","date":"2025-01-28","arxiv_id":"2501.17326","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-semantic-dilution-in-transformer","title":"Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction","date":"2025-01-28","arxiv_id":"2501.16753","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-process-based-prediction-and-control","title":"Gaussian Process-Based Prediction and Control of Hammerstein-Wiener Systems","date":"2025-01-27","arxiv_id":"2501.15849","repositories_listed":0,"syntology":null},{"url":null,"slug":"multipdenet-pde-embedded-learning-with-multi","title":"MultiPDENet: PDE-embedded Learning with Multi-time-stepping for Accelerated Flow Simulation","date":"2025-01-27","arxiv_id":"2501.15987","repositories_listed":0,"syntology":null},{"url":null,"slug":"star-stepwise-task-augmentation-and-relation","title":"STAR: Stepwise Task Augmentation and Relation Learning for Aspect Sentiment Quad Prediction","date":"2025-01-27","arxiv_id":"2501.16093","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-components-of-collaborative-joint","title":"The Components of Collaborative Joint Perception and Prediction -- A Conceptual Framework","date":"2025-01-27","arxiv_id":"2501.15860","repositories_listed":0,"syntology":null},{"url":null,"slug":"deterministic-reservoir-computing-for-chaotic","title":"Deterministic Reservoir Computing for Chaotic Time Series Prediction","date":"2025-01-26","arxiv_id":"2501.15615","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-aided-channel-prediction-for-vehicular","title":"Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images","date":"2025-01-25","arxiv_id":"2501.18618","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-inference-of-individual-treatment","title":"Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates","date":"2025-01-24","arxiv_id":"2501.14933","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-2d-ct-foundation-model-for-contrast","title":"Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost","date":"2025-01-23","arxiv_id":"2501.14066","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-informed-multi-agent-trajectory","title":"Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything","date":"2025-01-23","arxiv_id":"2501.13461","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-stock-price-prediction","title":"Multimodal Stock Price Prediction","date":"2025-01-23","arxiv_id":"2502.05186","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-learning-in-energy-based-models","title":"Predictive Learning in Energy-based Models with Attractor Structures","date":"2025-01-23","arxiv_id":"2501.13997","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-first-indoor-pathloss-radio-map","title":"The First Indoor Pathloss Radio Map Prediction Challenge","date":"2025-01-23","arxiv_id":"2501.13698","repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-regularized-conformal-prediction","title":"Wasserstein-regularized Conformal Prediction under General Distribution Shift","date":"2025-01-23","arxiv_id":"2501.13430","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-do-you-go-pedestrian-trajectory","title":"Where Do You Go? Pedestrian Trajectory Prediction using Scene Features","date":"2025-01-23","arxiv_id":"2501.13848","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spatio-temporal-graph-network-allowing","title":"A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction","date":"2025-01-22","arxiv_id":"2501.13973","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-supervised-and-self-supervised-graph","title":"A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications","date":"2025-01-21","arxiv_id":"2501.12309","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-multi-head-learning-systems-for","title":"Distributed Multi-Head Learning Systems for Power Consumption Prediction","date":"2025-01-21","arxiv_id":"2501.12133","repositories_listed":0,"syntology":null},{"url":null,"slug":"entire-learning-based-volume-rendering-time","title":"ENTIRE: Learning-based Volume Rendering Time Prediction","date":"2025-01-21","arxiv_id":"2501.12119","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-federated-learning-system-for","title":"Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction","date":"2025-01-21","arxiv_id":"2501.12125","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-like-conceptual-representations-emerge","title":"Human-like conceptual representations emerge from language prediction","date":"2025-01-21","arxiv_id":"2501.12547","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlinear-based-prediction-of-remaining-useful","title":"DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence","date":"2025-01-20","arxiv_id":"2501.11542","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-and-informativeness-of-1","title":"Generalization and Informativeness of Weighted Conformal Risk Control Under Covariate Shift","date":"2025-01-20","arxiv_id":"2501.11413","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-enabled-blockage-prediction-for","title":"Generative AI-enabled Blockage Prediction for Robust Dual-Band mmWave Communication","date":"2025-01-20","arxiv_id":"2501.11763","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-wireless-link-quality-prediction","title":"Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models","date":"2025-01-20","arxiv_id":"2501.11247","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomness-exchangeability-and-conformal","title":"Randomness, exchangeability, and conformal prediction","date":"2025-01-20","arxiv_id":"2501.11689","repositories_listed":0,"syntology":null},{"url":null,"slug":"transductive-conformal-inference-for-ranking","title":"Transductive Conformal Inference for Full Ranking","date":"2025-01-20","arxiv_id":"2501.11384","repositories_listed":0,"syntology":null},{"url":null,"slug":"ultra-high-reliability-by-predictive","title":"Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theory","date":"2025-01-20","arxiv_id":"2501.11704","repositories_listed":0,"syntology":null},{"url":null,"slug":"grid-protecting-training-graph-from-link","title":"GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models","date":"2025-01-19","arxiv_id":"2501.10985","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-sets-with-improved","title":"Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores","date":"2025-01-17","arxiv_id":"2501.10139","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-benefits-of-instance-decomposition-in","title":"On the Benefits of Instance Decomposition in Video Prediction Models","date":"2025-01-17","arxiv_id":"2501.10562","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-prediction-of-secondary","title":"Spatiotemporal Prediction of Secondary Crashes by Rebalancing Dynamic and Static Data with Generative Adversarial Networks","date":"2025-01-17","arxiv_id":"2501.10041","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-relevance-of-aws-chronos-an-evaluation-of","title":"The Relevance of AWS Chronos: An Evaluation of Standard Methods for Time Series Forecasting with Limited Tuning","date":"2025-01-17","arxiv_id":"2501.10216","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-nodes-improve-long-term-traffic","title":"Virtual Nodes Improve Long-term Traffic Prediction","date":"2025-01-17","arxiv_id":"2501.10048","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-monocular-scene-flow-estimation-in","title":"Zero-Shot Monocular Scene Flow Estimation in the Wild","date":"2025-01-17","arxiv_id":"2501.10357","repositories_listed":0,"syntology":null},{"url":null,"slug":"tessellated-linear-model-for-age-prediction","title":"Tessellated Linear Model for Age Prediction from Voice","date":"2025-01-16","arxiv_id":"2501.09229","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-multiple-interval-prediction-method","title":"Scenarios Generation-based Multiple Interval Prediction Method for Electricity Prices","date":"2025-01-15","arxiv_id":"2501.08532","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-multiple-interval-prediction-method-1","title":"A Novel Multiple Interval Prediction Method for Electricity Prices based on Scenarios Generation: Definition and Method","date":"2025-01-15","arxiv_id":"2501.08531","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-spatio-temporal-event-prediction","title":"Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph","date":"2025-01-15","arxiv_id":"2501.08653","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-quantized-relative-difference","title":"Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction","date":"2025-01-15","arxiv_id":"2501.09103","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-guide-dog-egocentric-path-prediction-on","title":"AI Guide Dog: Egocentric Path Prediction on Smartphone","date":"2025-01-14","arxiv_id":"2501.07957","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidepth-multimodal-neural-network","title":"BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Architecture for Spatial-Temporal Prediction","date":"2025-01-14","arxiv_id":"2501.08411","repositories_listed":0,"syntology":null}],"record_sha256":"72bf314a72f494c986ade8bf3f37f5f99a0fa1545ee4350f77ed45cb67faa924","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}