{"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":"/method/set/papers/66","list_of":"/method/set","method":"SET","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":66,"pages_in_order":135,"rows_per_page":100,"rows":[6501,6600],"of":13419,"counts":{"archive_papers_tagged":13419,"with_a_code_link":4158,"where_syntology_ran_a_sample":1186,"not_listed_spam_title":0,"listed":13419,"listed_where_code_ran":1186,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1038,"every_run_a_failure_of_syntologys_instrument":148,"listed_with_a_run_with_no_instrument_failure":1038,"listed_every_run_a_failure_of_syntologys_instrument":148,"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":"/method/set","prev":"/method/set/papers/65","next":"/method/set/papers/67","papers":[{"paper":null,"slug":"disconerf-class-agnostic-object-field-for-3d","title":"Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields","date":"2024-08-19","arxiv_id":"2408.09928","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-label-injection-for-imbalanced","slug":"dynamic-label-injection-for-imbalanced","title":"Dynamic Label Injection for Imbalanced Industrial Defect Segmentation","date":"2024-08-19","arxiv_id":"2408.10031","n_code_links":1,"syntology":null},{"paper":null,"slug":"federated-learning-of-large-asr-models-in-the","title":"Federated Learning of Large ASR Models in the Real World","date":"2024-08-19","arxiv_id":"2408.10443","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-frequency-trading-liquidity-analysis","title":"High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification","date":"2024-08-19","arxiv_id":"2408.10016","n_code_links":0,"syntology":null},{"paper":"/paper/importance-weighting-can-help-large-language","slug":"importance-weighting-can-help-large-language","title":"Importance Weighting Can Help Large Language Models Self-Improve","date":"2024-08-19","arxiv_id":"2408.09849","n_code_links":1,"syntology":{"ran":4,"of":7,"n_ran_checked":4,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["rubickkcibur/IWSI"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"learning-brave-assumption-based-argumentation","title":"Learning Brave Assumption-Based Argumentation Frameworks via ASP","date":"2024-08-19","arxiv_id":"2408.10126","n_code_links":0,"syntology":null},{"paper":"/paper/masala-model-agnostic-surrogate-explanations","slug":"masala-model-agnostic-surrogate-explanations","title":"MASALA: Model-Agnostic Surrogate Explanations by Locality Adaptation","date":"2024-08-19","arxiv_id":"2408.10085","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-foundations-of-conflict-driven-solving","title":"On the Foundations of Conflict-Driven Solving for Hybrid MKNF Knowledge Bases","date":"2024-08-19","arxiv_id":"2408.09626","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-insurance-design-with-lambda-value-at","title":"Optimal insurance design with Lambda-Value-at-Risk","date":"2024-08-19","arxiv_id":"2408.09799","n_code_links":0,"syntology":null},{"paper":null,"slug":"paired-completion-flexible-quantification-of","title":"Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs","date":"2024-08-19","arxiv_id":"2408.09742","n_code_links":0,"syntology":null},{"paper":null,"slug":"partial-multivariate-model-for-forecasting","title":"Partial-Multivariate Model for Forecasting","date":"2024-08-19","arxiv_id":"2408.09703","n_code_links":0,"syntology":null},{"paper":"/paper/preference-optimized-pareto-set-learning-for","slug":"preference-optimized-pareto-set-learning-for","title":"Preference-Optimized Pareto Set Learning for Blackbox Optimization","date":"2024-08-19","arxiv_id":"2408.09976","n_code_links":1,"syntology":null},{"paper":null,"slug":"principle-driven-parameterized-fiber-model","title":"Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network","date":"2024-08-19","arxiv_id":"2408.09951","n_code_links":0,"syntology":null},{"paper":"/paper/r2gencsr-retrieving-context-samples-for-large","slug":"r2gencsr-retrieving-context-samples-for-large","title":"R2GenCSR: Retrieving Context Samples for Large Language Model based X-ray Medical Report Generation","date":"2024-08-19","arxiv_id":"2408.09743","n_code_links":1,"syntology":null},{"paper":null,"slug":"shortcircuit-alphazero-driven-circuit-design","title":"ShortCircuit: AlphaZero-Driven Circuit Design","date":"2024-08-19","arxiv_id":"2408.09858","n_code_links":0,"syntology":null},{"paper":null,"slug":"strategic-demonstration-selection-for","title":"Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learning","date":"2024-08-19","arxiv_id":"2408.09757","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-practimum-optimum-algorithm-for","title":"The Practimum-Optimum Algorithm for Manufacturing Scheduling: A Paradigm Shift Leading to Breakthroughs in Scale and Performance","date":"2024-08-19","arxiv_id":"2408.10040","n_code_links":0,"syntology":null},{"paper":null,"slug":"uninext-cutie-the-1st-solution-for-lsvos","title":"UNINEXT-Cutie: The 1st Solution for LSVOS Challenge RVOS Track","date":"2024-08-19","arxiv_id":"2408.10129","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-object-segmentation-via-sam-2-the-4th","title":"Video Object Segmentation via SAM 2: The 4th Solution for LSVOS Challenge VOS Track","date":"2024-08-19","arxiv_id":"2408.10125","n_code_links":0,"syntology":null},{"paper":"/paper/circuit-design-in-biology-and-machine","slug":"circuit-design-in-biology-and-machine","title":"Circuit design in biology and machine learning. I. Random networks and dimensional reduction","date":"2024-08-18","arxiv_id":"2408.09604","n_code_links":1,"syntology":null},{"paper":null,"slug":"concept-distillation-from-strong-to-weak","title":"Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Prompting","date":"2024-08-18","arxiv_id":"2408.09365","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-quantum-memory-lifetime-with","title":"Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning","date":"2024-08-18","arxiv_id":"2408.09524","n_code_links":0,"syntology":null},{"paper":null,"slug":"grammatical-error-feedback-an-implicit","title":"Grammatical Error Feedback: An Implicit Evaluation Approach","date":"2024-08-18","arxiv_id":"2408.09565","n_code_links":0,"syntology":null},{"paper":null,"slug":"grlinq-an-intelligent-spectrum-sharing","title":"GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning","date":"2024-08-18","arxiv_id":"2408.09394","n_code_links":0,"syntology":null},{"paper":null,"slug":"hindi-beir-a-large-scale-retrieval-benchmark","title":"Hindi-BEIR : A Large Scale Retrieval Benchmark in Hindi","date":"2024-08-18","arxiv_id":"2408.09437","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-lung-cancer-diagnosis-and-survival","title":"Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging","date":"2024-08-18","arxiv_id":"2408.09367","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-stopping-and-divestment-timing-under","title":"Optimal stopping and divestment timing under scenario ambiguity and learning","date":"2024-08-18","arxiv_id":"2408.09349","n_code_links":0,"syntology":null},{"paper":null,"slug":"sample-optimal-large-scale-optimal-subset","title":"Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening","date":"2024-08-18","arxiv_id":"2408.09537","n_code_links":0,"syntology":null},{"paper":null,"slug":"undominated-monopoly-regulation","title":"Undominated monopoly regulation","date":"2024-08-18","arxiv_id":"2408.09473","n_code_links":0,"syntology":null},{"paper":"/paper/are-clip-features-all-you-need-for-universal","slug":"are-clip-features-all-you-need-for-universal","title":"Are CLIP features all you need for Universal Synthetic Image Origin Attribution?","date":"2024-08-17","arxiv_id":"2408.09153","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":10,"n_instrument":0,"unverified":3,"pointer_only":13,"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","official":{"repos":["ciodar/universalattribution"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cyberpal-ai-empowering-llms-with-expert","title":"CyberPal.AI: Empowering LLMs with Expert-Driven Cybersecurity Instructions","date":"2024-08-17","arxiv_id":"2408.09304","n_code_links":0,"syntology":null},{"paper":null,"slug":"dual-band-slant-polarized-mimo-antenna-set","title":"Dual-Band, Slant-Polarized MIMO Antenna Set for Vehicular Communication","date":"2024-08-17","arxiv_id":"2408.09133","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-community-detection-in-networks-a","title":"Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms","date":"2024-08-17","arxiv_id":"2408.09072","n_code_links":0,"syntology":null},{"paper":"/paper/gaussian-in-the-dark-real-time-view-synthesis","slug":"gaussian-in-the-dark-real-time-view-synthesis","title":"Gaussian in the Dark: Real-Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting","date":"2024-08-17","arxiv_id":"2408.09130","n_code_links":2,"syntology":null},{"paper":null,"slug":"how-to-make-an-action-better","title":"How to Make an Action Better","date":"2024-08-17","arxiv_id":"2408.09294","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-camera-multi-person-association-using","title":"Multi-Camera Multi-Person Association using Transformer-Based Dense Pixel Correspondence Estimation and Detection-Based Masking","date":"2024-08-17","arxiv_id":"2408.09295","n_code_links":0,"syntology":null},{"paper":null,"slug":"narrowing-the-focus-learned-optimizers-for","title":"Narrowing the Focus: Learned Optimizers for Pretrained Models","date":"2024-08-17","arxiv_id":"2408.09310","n_code_links":0,"syntology":null},{"paper":"/paper/premap-a-unifying-preimage-approximation","slug":"premap-a-unifying-preimage-approximation","title":"PREMAP: A Unifying PREiMage APproximation Framework for Neural Networks","date":"2024-08-17","arxiv_id":"2408.09262","n_code_links":1,"syntology":null},{"paper":null,"slug":"tablebench-a-comprehensive-and-complex","title":"TableBench: A Comprehensive and Complex Benchmark for Table Question Answering","date":"2024-08-17","arxiv_id":"2408.09174","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-training-regime-to-learn-unified","title":"A training regime to learn unified representations from complementary breast imaging modalities","date":"2024-08-16","arxiv_id":"2408.08560","n_code_links":0,"syntology":null},{"paper":null,"slug":"aden-adaptive-density-representations-for","title":"ADen: Adaptive Density Representations for Sparse-view Camera Pose Estimation","date":"2024-08-16","arxiv_id":"2408.09042","n_code_links":0,"syntology":null},{"paper":"/paper/backward-compatible-aligned-representations","slug":"backward-compatible-aligned-representations","title":"Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer","date":"2024-08-16","arxiv_id":"2408.08793","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-lazy-to-prolific-tackling-missing-labels","title":"From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning","date":"2024-08-16","arxiv_id":"2408.08981","n_code_links":0,"syntology":null},{"paper":null,"slug":"historical-printed-ornaments-dataset-and","title":"Historical Printed Ornaments: Dataset and Tasks","date":"2024-08-16","arxiv_id":"2408.08633","n_code_links":0,"syntology":null},{"paper":null,"slug":"hycot-hyperspectral-compression-transformer","title":"HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression","date":"2024-08-16","arxiv_id":"2408.08700","n_code_links":0,"syntology":null},{"paper":"/paper/language-driven-interactive-shadow-detection","slug":"language-driven-interactive-shadow-detection","title":"Language-Driven Interactive Shadow Detection","date":"2024-08-16","arxiv_id":"2408.08543","n_code_links":1,"syntology":null},{"paper":"/paper/math-puma-progressive-upward-multimodal","slug":"math-puma-progressive-upward-multimodal","title":"Math-PUMA: Progressive Upward Multimodal Alignment to Enhance Mathematical Reasoning","date":"2024-08-16","arxiv_id":"2408.08640","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":1,"n_instrument":4,"unverified":0,"pointer_only":5,"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) · 0 unverified","official":{"repos":["wwzhuang01/math-puma"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"meta-knowledge-for-retrieval-augmented-large","title":"Meta Knowledge for Retrieval Augmented Large Language Models","date":"2024-08-16","arxiv_id":"2408.09017","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-relational-triple-extraction-with","title":"Multimodal Relational Triple Extraction with Query-based Entity Object Transformer","date":"2024-08-16","arxiv_id":"2408.08709","n_code_links":0,"syntology":null},{"paper":"/paper/near-a-training-free-pre-estimator-of-machine","slug":"near-a-training-free-pre-estimator-of-machine","title":"NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance","date":"2024-08-16","arxiv_id":"2408.08776","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-undecidability-of-artificial","title":"On the Undecidability of Artificial Intelligence Alignment: Machines that Halt","date":"2024-08-16","arxiv_id":"2408.08995","n_code_links":0,"syntology":null},{"paper":null,"slug":"optdist-learning-optimal-distribution-for","title":"OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction","date":"2024-08-16","arxiv_id":"2408.08585","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-the-effectiveness-of-student","title":"Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing","date":"2024-08-16","arxiv_id":"2408.08694","n_code_links":0,"syntology":null},{"paper":null,"slug":"sc-rec-enhancing-generative-retrieval-with","title":"SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation","date":"2024-08-16","arxiv_id":"2408.08686","n_code_links":0,"syntology":null},{"paper":null,"slug":"selectllm-query-aware-efficient-selection","title":"SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models","date":"2024-08-16","arxiv_id":"2408.08545","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-the-quantum-many-body-hamiltonian","title":"Solving The Quantum Many-Body Hamiltonian Learning Problem with Neural Differential Equations","date":"2024-08-16","arxiv_id":"2408.08639","n_code_links":0,"syntology":null},{"paper":null,"slug":"speckle-noise-analysis-for-synthetic-aperture","title":"Speckle Noise Analysis for Synthetic Aperture Radar (SAR) Space Data","date":"2024-08-16","arxiv_id":"2408.08774","n_code_links":0,"syntology":null},{"paper":null,"slug":"symbolic-parameter-learning-in-probabilistic","title":"Symbolic Parameter Learning in Probabilistic Answer Set Programming","date":"2024-08-16","arxiv_id":"2408.08732","n_code_links":0,"syntology":null},{"paper":"/paper/sympol-symbolic-tree-based-on-policy","slug":"sympol-symbolic-tree-based-on-policy","title":"Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization","date":"2024-08-16","arxiv_id":"2408.08761","n_code_links":1,"syntology":null},{"paper":null,"slug":"vera-validation-and-evaluation-of-retrieval","title":"VERA: Validation and Evaluation of Retrieval-Augmented Systems","date":"2024-08-16","arxiv_id":"2409.03759","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-systematic-evaluation-of-generated-time","title":"A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining","date":"2024-08-15","arxiv_id":"2408.07869","n_code_links":0,"syntology":null},{"paper":null,"slug":"asteroid-resource-efficient-hybrid-pipeline","title":"Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices","date":"2024-08-15","arxiv_id":"2408.08015","n_code_links":0,"syntology":null},{"paper":"/paper/certifiable-deep-learning-for-reachability","slug":"certifiable-deep-learning-for-reachability","title":"Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function","date":"2024-08-15","arxiv_id":"2408.07866","n_code_links":1,"syntology":null},{"paper":"/paper/deepseek-prover-v1-5-harnessing-proof","slug":"deepseek-prover-v1-5-harnessing-proof","title":"DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search","date":"2024-08-15","arxiv_id":"2408.08152","n_code_links":2,"syntology":{"ran":10,"of":10,"n_ran_checked":10,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["deepseek-ai/deepseek-prover-v1.5"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"enhanced-equivalent-circuit-model-for-high","title":"Enhanced Equivalent Circuit Model for High Current Discharge of Lithium-Ion Batteries with Application to Electric Vertical Takeoff and Landing Aircraft","date":"2024-08-15","arxiv_id":"2408.07926","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-learning-environments-for-label","title":"Exploring learning environments for label\\-efficient cancer diagnosis","date":"2024-08-15","arxiv_id":"2408.07988","n_code_links":0,"syntology":null},{"paper":"/paper/graph-retrieval-augmented-generation-a-survey","slug":"graph-retrieval-augmented-generation-a-survey","title":"Graph Retrieval-Augmented Generation: A Survey","date":"2024-08-15","arxiv_id":"2408.08921","n_code_links":1,"syntology":null},{"paper":null,"slug":"heavy-labels-out-dataset-distillation-with","title":"Heavy Labels Out! Dataset Distillation with Label Space Lightening","date":"2024-08-15","arxiv_id":"2408.08201","n_code_links":0,"syntology":null},{"paper":null,"slug":"heightlane-bev-heightmap-guided-3d-lane","title":"HeightLane: BEV Heightmap guided 3D Lane Detection","date":"2024-08-15","arxiv_id":"2408.08270","n_code_links":0,"syntology":null},{"paper":"/paper/impact-of-comprehensive-data-preprocessing-on","slug":"impact-of-comprehensive-data-preprocessing-on","title":"Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 Mortality","date":"2024-08-15","arxiv_id":"2408.08142","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-web-crawled-data-for-high-quality","slug":"leveraging-web-crawled-data-for-high-quality","title":"Leveraging Web-Crawled Data for High-Quality Fine-Tuning","date":"2024-08-15","arxiv_id":"2408.08003","n_code_links":1,"syntology":null},{"paper":"/paper/maximally-permissive-reward-machines","slug":"maximally-permissive-reward-machines","title":"Maximally Permissive Reward Machines","date":"2024-08-15","arxiv_id":"2408.08059","n_code_links":1,"syntology":null},{"paper":"/paper/monte-carlo-path-tracing-and-statistical","slug":"monte-carlo-path-tracing-and-statistical","title":"Monte Carlo Path Tracing and Statistical Event Detection for Event Camera Simulation","date":"2024-08-15","arxiv_id":"2408.07996","n_code_links":1,"syntology":null},{"paper":"/paper/navigating-data-scarcity-using-foundation","slug":"navigating-data-scarcity-using-foundation","title":"Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging","date":"2024-08-15","arxiv_id":"2408.08058","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-accelerating-large-scale-robust-portfolio","title":"On Accelerating Large-Scale Robust Portfolio Optimization","date":"2024-08-15","arxiv_id":"2408.07879","n_code_links":0,"syntology":null},{"paper":null,"slug":"opdr-order-preserving-dimension-reduction-for","title":"OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data","date":"2024-08-15","arxiv_id":"2408.10264","n_code_links":0,"syntology":null},{"paper":"/paper/penny-wise-and-pound-foolish-in-deepfake","slug":"penny-wise-and-pound-foolish-in-deepfake","title":"Penny-Wise and Pound-Foolish in Deepfake Detection","date":"2024-08-15","arxiv_id":"2408.08412","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-sharelm-collection-and-plugin","title":"The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community","date":"2024-08-15","arxiv_id":"2408.08291","n_code_links":0,"syntology":null},{"paper":"/paper/treat-stillness-with-movement-remote-sensing","slug":"treat-stillness-with-movement-remote-sensing","title":"Treat Stillness with Movement: Remote Sensing Change Detection via Coarse-grained Temporal Foregrounds Mining","date":"2024-08-15","arxiv_id":"2408.08078","n_code_links":1,"syntology":null},{"paper":null,"slug":"winning-snake-design-choices-in-multi-shot","title":"Winning Snake: Design Choices in Multi-Shot ASP","date":"2024-08-15","arxiv_id":"2408.08150","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-unconstrained-face-recognition-with","title":"Boosting Unconstrained Face Recognition with Targeted Style Adversary","date":"2024-08-14","arxiv_id":"2408.07642","n_code_links":0,"syntology":null},{"paper":"/paper/con-fold-explainable-machine-learning-with","slug":"con-fold-explainable-machine-learning-with","title":"CON-FOLD -- Explainable Machine Learning with Confidence","date":"2024-08-14","arxiv_id":"2408.07854","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-a-heuristic-three-stage","title":"Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-based Clinical Data","date":"2024-08-14","arxiv_id":"2408.07673","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-near-duplicate-face-images","slug":"detecting-near-duplicate-face-images","title":"Detecting Near-Duplicate Face Images","date":"2024-08-14","arxiv_id":"2408.07689","n_code_links":1,"syntology":null},{"paper":null,"slug":"dome-registry-implementing-community-wide","title":"DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology","date":"2024-08-14","arxiv_id":"2408.07721","n_code_links":0,"syntology":null},{"paper":"/paper/dominating-set-reconfiguration-with-answer","slug":"dominating-set-reconfiguration-with-answer","title":"Dominating Set Reconfiguration with Answer Set Programming","date":"2024-08-14","arxiv_id":"2408.07510","n_code_links":1,"syntology":null},{"paper":null,"slug":"enhanced-optimization-strategies-to-design-an","title":"Enhanced Optimization Strategies to Design an Underactuated Hand Exoskeleton","date":"2024-08-14","arxiv_id":"2408.07384","n_code_links":0,"syntology":null},{"paper":null,"slug":"exact-trajectory-similarity-search-with-n","title":"Exact Trajectory Similarity Search With N-tree: An Efficient Metric Index for kNN and Range Queries","date":"2024-08-14","arxiv_id":"2408.07650","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-inference-for-probabilistic-answer-set","title":"Fast Inference for Probabilistic Answer Set Programs via the Residual Program","date":"2024-08-14","arxiv_id":"2408.07524","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-brazilian-portuguese-to-european","title":"From Brazilian Portuguese to European Portuguese","date":"2024-08-14","arxiv_id":"2408.07457","n_code_links":0,"syntology":null},{"paper":null,"slug":"gqe-generalized-query-expansion-for-enhanced","title":"Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach","date":"2024-08-14","arxiv_id":"2408.07249","n_code_links":0,"syntology":null},{"paper":"/paper/grif-dm-generation-of-rich-impression-fonts","slug":"grif-dm-generation-of-rich-impression-fonts","title":"GRIF-DM: Generation of Rich Impression Fonts using Diffusion Models","date":"2024-08-14","arxiv_id":"2408.07259","n_code_links":1,"syntology":null},{"paper":"/paper/how-big-is-big-enough-adjusting-model-size-in","slug":"how-big-is-big-enough-adjusting-model-size-in","title":"Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?","date":"2024-08-14","arxiv_id":"2408.07588","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-global-parameter-sharing-in","title":"Improving Global Parameter-sharing in Physically Heterogeneous Multi-agent Reinforcement Learning with Unified Action Space","date":"2024-08-14","arxiv_id":"2408.07395","n_code_links":0,"syntology":null},{"paper":"/paper/optimising-dynamic-traffic-distribution-for","slug":"optimising-dynamic-traffic-distribution-for","title":"Optimising Dynamic Traffic Distribution for Urban Networks with Answer Set Programming","date":"2024-08-14","arxiv_id":"2408.07521","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-the-distributions-of-stock-returns","title":"Predicting the distributions of stock returns around the globe in the era of big data and learning","date":"2024-08-14","arxiv_id":"2408.07497","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-over-optimum-answer-sets","title":"Quantifying over Optimum Answer Sets","date":"2024-08-14","arxiv_id":"2408.07697","n_code_links":0,"syntology":null},{"paper":null,"slug":"ranking-and-combining-latent-structured","title":"Ranking and Combining Latent Structured Predictive Scores without Labeled Data","date":"2024-08-14","arxiv_id":"2408.07796","n_code_links":0,"syntology":null},{"paper":null,"slug":"ranking-evaluation-metrics-from-a-group","title":"Ranking evaluation metrics from a group-theoretic perspective","date":"2024-08-14","arxiv_id":"2408.16009","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-open-vocabulary-segmentation-of","title":"Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space","date":"2024-08-14","arxiv_id":"2408.07416","n_code_links":0,"syntology":null}],"record_sha256":"561e96534270349a395fb17b1669161f86c99a7f59553dabd4be1d711725a298","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}