{"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/softmax/papers/56","list_of":"/method/softmax","method":"Softmax","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":56,"pages_in_order":375,"rows_per_page":100,"rows":[5501,5600],"of":37443,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"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/softmax","prev":"/method/softmax/papers/55","next":"/method/softmax/papers/57","papers":[{"paper":null,"slug":"rdg-gs-relative-depth-guidance-with-gaussian","title":"RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time Sparse-View 3D Rendering","date":"2025-01-19","arxiv_id":"2501.11102","n_code_links":0,"syntology":null},{"paper":"/paper/reproducibility-review-of-why-not-other","slug":"reproducibility-review-of-why-not-other","title":"Reproducibility review of \"Why Not Other Classes\": Towards Class-Contrastive Back-Propagation Explanations","date":"2025-01-19","arxiv_id":"2501.11096","n_code_links":1,"syntology":null},{"paper":"/paper/a-cnn-transformer-for-classification-of","slug":"a-cnn-transformer-for-classification-of","title":"A CNN-Transformer for Classification of Longitudinal 3D MRI Images -- A Case Study on Hepatocellular Carcinoma Prediction","date":"2025-01-18","arxiv_id":"2501.10733","n_code_links":1,"syntology":null},{"paper":null,"slug":"building-short-value-chains-for-animal","title":"Building Short Value Chains for Animal Welfare-Friendly Products Adoption: Insights from a Restaurant-Based Study in Japan","date":"2025-01-18","arxiv_id":"2501.10680","n_code_links":0,"syntology":null},{"paper":null,"slug":"cerebro-compact-encoder-for-representations","title":"CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention","date":"2025-01-18","arxiv_id":"2501.10885","n_code_links":0,"syntology":null},{"paper":null,"slug":"cs-net-contribution-based-sampling-network","title":"CS-Net:Contribution-based Sampling Network for Point Cloud Simplification","date":"2025-01-18","arxiv_id":"2501.10789","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-trend-fusion-module-for-traffic-flow","slug":"dynamic-trend-fusion-module-for-traffic-flow","title":"Dynamic Trend Fusion Module for Traffic Flow Prediction","date":"2025-01-18","arxiv_id":"2501.10796","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-auto-labeling-of-large-scale","title":"Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach","date":"2025-01-18","arxiv_id":"2501.10809","n_code_links":0,"syntology":null},{"paper":null,"slug":"fsmoe-a-flexible-and-scalable-training-system","title":"FSMoE: A Flexible and Scalable Training System for Sparse Mixture-of-Experts Models","date":"2025-01-18","arxiv_id":"2501.10714","n_code_links":0,"syntology":null},{"paper":null,"slug":"gec-rag-improving-generative-error-correction","title":"GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems","date":"2025-01-18","arxiv_id":"2501.10734","n_code_links":0,"syntology":null},{"paper":null,"slug":"hops-high-order-polynomials-with-self","title":"HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting","date":"2025-01-18","arxiv_id":"2501.10637","n_code_links":0,"syntology":null},{"paper":null,"slug":"in-the-picture-medical-imaging-datasets","title":"In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review","date":"2025-01-18","arxiv_id":"2501.10727","n_code_links":0,"syntology":null},{"paper":"/paper/ld-detr-loop-decoder-detection-transformer","slug":"ld-detr-loop-decoder-detection-transformer","title":"LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection","date":"2025-01-18","arxiv_id":"2501.10787","n_code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-semantic-segmentation-for","slug":"semi-supervised-semantic-segmentation-for","title":"Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention","date":"2025-01-18","arxiv_id":"2501.10736","n_code_links":1,"syntology":null},{"paper":null,"slug":"simulation-of-hypergraph-algorithms-with","title":"Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers","date":"2025-01-18","arxiv_id":"2501.10688","n_code_links":0,"syntology":null},{"paper":null,"slug":"uav-assisted-multi-task-federated-learning","title":"UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing","date":"2025-01-18","arxiv_id":"2501.10644","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-rag-expanding-mllm-visual-knowledge","title":"Visual RAG: Expanding MLLM visual knowledge without fine-tuning","date":"2025-01-18","arxiv_id":"2501.10834","n_code_links":0,"syntology":null},{"paper":"/paper/4bit-quantization-in-vector-embedding-for-rag","slug":"4bit-quantization-in-vector-embedding-for-rag","title":"4bit-Quantization in Vector-Embedding for RAG","date":"2025-01-17","arxiv_id":"2501.10534","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-multi-scale-feature-extraction-and-fusion","title":"A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases","date":"2025-01-17","arxiv_id":"2501.09938","n_code_links":0,"syntology":null},{"paper":null,"slug":"accept-diagnostic-forecasting-of-battery","title":"ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning","date":"2025-01-17","arxiv_id":"2501.10492","n_code_links":0,"syntology":null},{"paper":null,"slug":"airrag-activating-intrinsic-reasoning-for","title":"AirRAG: Activating Intrinsic Reasoning for Retrieval Augmented Generation via Tree-based Search","date":"2025-01-17","arxiv_id":"2501.10053","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-guided-self-reflection-for-zero","title":"Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models","date":"2025-01-17","arxiv_id":"2501.09997","n_code_links":0,"syntology":null},{"paper":null,"slug":"bbpos-bert-based-part-of-speech-tagging-for","title":"BBPOS: BERT-based Part-of-Speech Tagging for Uzbek","date":"2025-01-17","arxiv_id":"2501.10107","n_code_links":0,"syntology":null},{"paper":"/paper/bias-in-decision-making-for-ai-s-ethical","slug":"bias-in-decision-making-for-ai-s-ethical","title":"Bias in Decision-Making for AI's Ethical Dilemmas: A Comparative Study of ChatGPT and Claude","date":"2025-01-17","arxiv_id":"2501.10484","n_code_links":1,"syntology":null},{"paper":null,"slug":"challenges-and-recommendations-for-electronic","title":"Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide","date":"2025-01-17","arxiv_id":"2501.10240","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffvsr-enhancing-real-world-video-super","title":"DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency","date":"2025-01-17","arxiv_id":"2501.10110","n_code_links":0,"syntology":null},{"paper":null,"slug":"dperc-direct-parameter-estimation-for-mixed","title":"DPERC: Direct Parameter Estimation for Mixed Data","date":"2025-01-17","arxiv_id":"2501.10540","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-the-reliability-in-machine-learning","title":"Enhancing the Reliability in Machine Learning for Gravitational Wave Parameter Estimation with Attention-Based Models","date":"2025-01-17","arxiv_id":"2501.10486","n_code_links":0,"syntology":null},{"paper":"/paper/filo-zero-few-shot-anomaly-detection-by-fused","slug":"filo-zero-few-shot-anomaly-detection-by-fused","title":"FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization","date":"2025-01-17","arxiv_id":"2501.10067","n_code_links":1,"syntology":null},{"paper":null,"slug":"himix-reducing-computational-complexity-in","title":"HiMix: Reducing Computational Complexity in Large Vision-Language Models","date":"2025-01-17","arxiv_id":"2501.10318","n_code_links":0,"syntology":null},{"paper":"/paper/lwganet-a-lightweight-group-attention","slug":"lwganet-a-lightweight-group-attention","title":"LWGANet: A Lightweight Group Attention Backbone for Remote Sensing Visual Tasks","date":"2025-01-17","arxiv_id":"2501.10040","n_code_links":1,"syntology":null},{"paper":"/paper/multi-modal-attention-networks-for-enhanced","slug":"multi-modal-attention-networks-for-enhanced","title":"Multi-Modal Attention Networks for Enhanced Segmentation and Depth Estimation of Subsurface Defects in Pulse Thermography","date":"2025-01-17","arxiv_id":"2501.09994","n_code_links":1,"syntology":null},{"paper":"/paper/multipruner-balanced-structure-removal-in","slug":"multipruner-balanced-structure-removal-in","title":"MultiPruner: Balanced Structure Removal in Foundation Models","date":"2025-01-17","arxiv_id":"2501.09949","n_code_links":1,"syntology":null},{"paper":"/paper/pasa-an-llm-agent-for-comprehensive-academic","slug":"pasa-an-llm-agent-for-comprehensive-academic","title":"PaSa: An LLM Agent for Comprehensive Academic Paper Search","date":"2025-01-17","arxiv_id":"2501.10120","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["bytedance/pasa"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"passage-segmentation-of-documents-for","title":"Passage Segmentation of Documents for Extractive Question Answering","date":"2025-01-17","arxiv_id":"2501.09940","n_code_links":0,"syntology":null},{"paper":null,"slug":"provably-safeguarding-a-classifier-from-ood","title":"Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach","date":"2025-01-17","arxiv_id":"2501.10202","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-change-captioning-in-remote-sensing","title":"Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and MModalCC Framework","date":"2025-01-17","arxiv_id":"2501.10075","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-clustering-graph-transformer-approach-to","title":"Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity","date":"2025-01-17","arxiv_id":"2501.16345","n_code_links":0,"syntology":null},{"paper":"/paper/the-r-vessel-x-project","slug":"the-r-vessel-x-project","title":"The R-Vessel-X Project","date":"2025-01-17","arxiv_id":"2501.10068","n_code_links":2,"syntology":null},{"paper":"/paper/a-simple-aerial-detection-baseline-of","slug":"a-simple-aerial-detection-baseline-of","title":"A Simple Aerial Detection Baseline of Multimodal Language Models","date":"2025-01-16","arxiv_id":"2501.09720","n_code_links":1,"syntology":null},{"paper":"/paper/a-simple-graph-contrastive-learning-framework","slug":"a-simple-graph-contrastive-learning-framework","title":"A Simple Graph Contrastive Learning Framework for Short Text Classification","date":"2025-01-16","arxiv_id":"2501.09219","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["keaml-jlu/simstc"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"adafv-accelerating-vlms-with-self-adaptive","title":"AdaFV: Rethinking of Visual-Language alignment for VLM acceleration","date":"2025-01-16","arxiv_id":"2501.09532","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-based-bidirectional-gru-hybrid","title":"Attention based Bidirectional GRU hybrid model for inappropriate content detection in Urdu language","date":"2025-01-16","arxiv_id":"2501.09722","n_code_links":0,"syntology":null},{"paper":"/paper/capa-carve-n-paint-synthesis-for-efficient-4k","slug":"capa-carve-n-paint-synthesis-for-efficient-4k","title":"CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation","date":"2025-01-16","arxiv_id":"2501.09433","n_code_links":1,"syntology":null},{"paper":"/paper/confidence-estimation-for-error-detection-in","slug":"confidence-estimation-for-error-detection-in","title":"Confidence Estimation for Error Detection in Text-to-SQL Systems","date":"2025-01-16","arxiv_id":"2501.09527","n_code_links":1,"syntology":null},{"paper":null,"slug":"cooperative-decentralized-backdoor-attacks-on","title":"Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning","date":"2025-01-16","arxiv_id":"2501.09320","n_code_links":0,"syntology":null},{"paper":"/paper/dstigcn-deformable-spatial-temporal","slug":"dstigcn-deformable-spatial-temporal","title":"DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction","date":"2025-01-16","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"enhancing-lexicon-based-text-embeddings-with","title":"Enhancing Lexicon-Based Text Embeddings with Large Language Models","date":"2025-01-16","arxiv_id":"2501.09749","n_code_links":0,"syntology":null},{"paper":null,"slug":"erasebench-understanding-the-ripple-effects","title":"EraseBench: Understanding The Ripple Effects of Concept Erasure Techniques","date":"2025-01-16","arxiv_id":"2501.09833","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-the-inquiry-diagnosis-relationship","slug":"exploring-the-inquiry-diagnosis-relationship","title":"Exploring the Inquiry-Diagnosis Relationship with Advanced Patient Simulators","date":"2025-01-16","arxiv_id":"2501.09484","n_code_links":1,"syntology":null},{"paper":"/paper/fine-grained-image-text-correspondence-with","slug":"fine-grained-image-text-correspondence-with","title":"Fine-Grained Image-Text Correspondence with Cost Aggregation for Open-Vocabulary Part Segmentation","date":"2025-01-16","arxiv_id":"2501.09688","n_code_links":1,"syntology":{"ran":9,"of":11,"n_ran_checked":8,"n_instrument":1,"unverified":2,"pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["kaist-cvml/part-catseg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/free-knots-kolmogorov-arnold-network-on-the","slug":"free-knots-kolmogorov-arnold-network-on-the","title":"Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots and Advancing Stability","date":"2025-01-16","arxiv_id":"2501.09283","n_code_links":1,"syntology":null},{"paper":null,"slug":"generalized-single-image-based-morphing","title":"Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer","date":"2025-01-16","arxiv_id":"2501.09817","n_code_links":0,"syntology":null},{"paper":"/paper/hspformer-hierarchical-spatial-perception","slug":"hspformer-hierarchical-spatial-perception","title":"HSPFormer: Hierarchical Spatial Perception Transformer for Semantic Segmentation","date":"2025-01-16","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/lavcap-llm-based-audio-visual-captioning","slug":"lavcap-llm-based-audio-visual-captioning","title":"LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport","date":"2025-01-16","arxiv_id":"2501.09291","n_code_links":2,"syntology":null},{"paper":null,"slug":"learnings-from-scaling-visual-tokenizers-for","title":"Learnings from Scaling Visual Tokenizers for Reconstruction and Generation","date":"2025-01-16","arxiv_id":"2501.09755","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-scale-aware-representations-for","title":"Leveraging Scale-aware Representations for improved Concept-Representation Alignment in ViTs","date":"2025-01-16","arxiv_id":"2501.09221","n_code_links":0,"syntology":null},{"paper":"/paper/mitigating-hallucinations-in-large-vision-3","slug":"mitigating-hallucinations-in-large-vision-3","title":"Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key","date":"2025-01-16","arxiv_id":"2501.09695","n_code_links":1,"syntology":{"ran":11,"of":16,"n_ran_checked":9,"n_instrument":2,"unverified":5,"pointer_only":16,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","official":{"repos":["zhyang2226/opa-dpo"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"near-field-xl-mimo-systems-with-sparse-upas","title":"Exploring the Advantages of Sparse Arrays in XL-MIMO Systems: Do Half-Wavelength Arrays Still Offer an Edge in the Near Field?","date":"2025-01-16","arxiv_id":"2501.09234","n_code_links":0,"syntology":null},{"paper":"/paper/on-learning-informative-trajectory-embeddings","slug":"on-learning-informative-trajectory-embeddings","title":"On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression","date":"2025-01-16","arxiv_id":"2501.09327","n_code_links":1,"syntology":null},{"paper":null,"slug":"perspective-transition-of-large-language","title":"Perspective Transition of Large Language Models for Solving Subjective Tasks","date":"2025-01-16","arxiv_id":"2501.09265","n_code_links":0,"syntology":null},{"paper":"/paper/practical-continual-forgetting-for-pre","slug":"practical-continual-forgetting-for-pre","title":"Practical Continual Forgetting for Pre-trained Vision Models","date":"2025-01-16","arxiv_id":"2501.09705","n_code_links":1,"syntology":null},{"paper":"/paper/prompt-cam-a-simpler-interpretable","slug":"prompt-cam-a-simpler-interpretable","title":"Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis","date":"2025-01-16","arxiv_id":"2501.09333","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"reward-guided-controlled-generation-for","title":"Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review","date":"2025-01-16","arxiv_id":"2501.09685","n_code_links":0,"syntology":null},{"paper":null,"slug":"se-bsfv-online-subspace-learning-based-shadow","title":"SE-BSFV: Online Subspace Learning based Shadow Enhancement and Background Suppression for ViSAR under Complex Background","date":"2025-01-16","arxiv_id":"2501.09341","n_code_links":0,"syntology":null},{"paper":null,"slug":"sentiment-analysis-in-twitter-social-network","title":"Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning","date":"2025-01-16","arxiv_id":"2501.09777","n_code_links":0,"syntology":null},{"paper":null,"slug":"soft-knowledge-distillation-with-multi","title":"Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression","date":"2025-01-16","arxiv_id":"2501.09321","n_code_links":0,"syntology":null},{"paper":"/paper/towards-robust-and-realistic-human-pose","slug":"towards-robust-and-realistic-human-pose","title":"Towards Robust and Realistic Human Pose Estimation via WiFi Signals","date":"2025-01-16","arxiv_id":"2501.09411","n_code_links":1,"syntology":null},{"paper":null,"slug":"unified-face-matching-and-physical-digital","title":"Unified Face Matching and Physical-Digital Spoofing Attack Detection","date":"2025-01-16","arxiv_id":"2501.09635","n_code_links":0,"syntology":null},{"paper":null,"slug":"achieving-stability-and-optimality-control","title":"Achieving Stability and Optimality: Control Strategy for a Wind Turbine Supplying an Electrolyzer in the Islanded Storage-less Microgrid","date":"2025-01-15","arxiv_id":"2501.08853","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-sampled-softmax-with-inverted-multi","slug":"adaptive-sampled-softmax-with-inverted-multi","title":"Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and Applications","date":"2025-01-15","arxiv_id":"2501.08563","n_code_links":1,"syntology":null},{"paper":"/paper/agentic-retrieval-augmented-generation-a","slug":"agentic-retrieval-augmented-generation-a","title":"Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG","date":"2025-01-15","arxiv_id":"2501.09136","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-is-all-you-need-until-you-need","title":"Attention is All You Need Until You Need Retention","date":"2025-01-15","arxiv_id":"2501.09166","n_code_links":0,"syntology":null},{"paper":null,"slug":"augmenting-human-annotated-training-data-with","title":"Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment","date":"2025-01-15","arxiv_id":"2501.09126","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-speaker-identity-text-guided-target","slug":"beyond-speaker-identity-text-guided-target","title":"Beyond Speaker Identity: Text Guided Target Speech Extraction","date":"2025-01-15","arxiv_id":"2501.09169","n_code_links":1,"syntology":null},{"paper":"/paper/bright-vo-brightness-guided-hybrid","slug":"bright-vo-brightness-guided-hybrid","title":"BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement Module","date":"2025-01-15","arxiv_id":"2501.08659","n_code_links":1,"syntology":null},{"paper":null,"slug":"cancer-net-pca-seg-benchmarking-deep-learning","title":"Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging","date":"2025-01-15","arxiv_id":"2501.09185","n_code_links":0,"syntology":null},{"paper":"/paper/citydreamer4d-compositional-generative-model","slug":"citydreamer4d-compositional-generative-model","title":"CityDreamer4D: Compositional Generative Model of Unbounded 4D Cities","date":"2025-01-15","arxiv_id":"2501.08983","n_code_links":1,"syntology":null},{"paper":null,"slug":"computationally-intensive-research-advancing","title":"Computationally Intensive Research: Advancing a Role for Secondary Analysis of Qualitative Data","date":"2025-01-15","arxiv_id":"2506.04230","n_code_links":0,"syntology":null},{"paper":null,"slug":"ct-patchtst-channel-time-patch-time-series","title":"CT-PatchTST: Channel-Time Patch Time-Series Transformer for Long-Term Renewable Energy Forecasting","date":"2025-01-15","arxiv_id":"2501.08620","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-distance-map-regression-network-with","title":"Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation","date":"2025-01-15","arxiv_id":"2501.09116","n_code_links":0,"syntology":null},{"paper":"/paper/deep-self-supervised-disturbance-mapping-with","slug":"deep-self-supervised-disturbance-mapping-with","title":"Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product","date":"2025-01-15","arxiv_id":"2501.09129","n_code_links":1,"syntology":null},{"paper":null,"slug":"dynamic-portfolio-optimization-via-augmented","title":"Dynamic Portfolio Optimization via Augmented DDPG with Quantum Price Levels-Based Trading Strategy","date":"2025-01-15","arxiv_id":"2501.08528","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamicface-high-quality-and-consistent-video","title":"DynamicFace: High-Quality and Consistent Video Face Swapping using Composable 3D Facial Priors","date":"2025-01-15","arxiv_id":"2501.08553","n_code_links":0,"syntology":null},{"paper":null,"slug":"easing-seasickness-through-attention","title":"Easing Seasickness through Attention Redirection with a Mindfulness-Based Brain--Computer Interface","date":"2025-01-15","arxiv_id":"2501.08518","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-traffic-prediction-through-spatio","slug":"efficient-traffic-prediction-through-spatio","title":"Efficient Traffic Prediction Through Spatio-Temporal Distillation","date":"2025-01-15","arxiv_id":"2501.10459","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["lizzyhku/TP"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"enhanced-large-language-models-for-effective","title":"Enhanced Large Language Models for Effective Screening of Depression and Anxiety","date":"2025-01-15","arxiv_id":"2501.08769","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-multi-scale-cross-attention-for","title":"Enhanced Multi-Scale Cross-Attention for Person Image Generation","date":"2025-01-15","arxiv_id":"2501.08900","n_code_links":0,"syntology":null},{"paper":null,"slug":"expanding-vietnamese-sentiwordnet-to-improve","title":"Expanding Vietnamese SentiWordNet to Improve Performance of Vietnamese Sentiment Analysis Models","date":"2025-01-15","arxiv_id":"2501.08758","n_code_links":0,"syntology":null},{"paper":"/paper/feature-based-one-for-all-a-universal","slug":"feature-based-one-for-all-a-universal","title":"Feature-based One-For-All: A Universal Framework for Heterogeneous Knowledge Distillation","date":"2025-01-15","arxiv_id":"2501.08885","n_code_links":0,"syntology":{"ran":13,"of":16,"n_ran_checked":9,"n_instrument":4,"unverified":3,"pointer_only":0,"phrase":"13 ran (of which 8 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 4 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"generative-ai-takes-a-statistics-exam-a","title":"Generative AI Takes a Statistics Exam: A Comparison of Performance between ChatGPT3.5, ChatGPT4, and ChatGPT4o-mini","date":"2025-01-15","arxiv_id":"2501.09171","n_code_links":0,"syntology":null},{"paper":"/paper/grappa-a-hybrid-graph-neural-network-for","slug":"grappa-a-hybrid-graph-neural-network-for","title":"GRAPPA - A Hybrid Graph Neural Network for Predicting Pure Component Vapor Pressures","date":"2025-01-15","arxiv_id":"2501.08729","n_code_links":1,"syntology":null},{"paper":null,"slug":"incrementally-learning-multiple-diverse-data","title":"Incrementally Learning Multiple Diverse Data Domains via Multi-Source Dynamic Expansion Model","date":"2025-01-15","arxiv_id":"2501.08878","n_code_links":0,"syntology":null},{"paper":"/paper/information-entropy-invariance-enhancing","slug":"information-entropy-invariance-enhancing","title":"Information Entropy Invariance: Enhancing Length Extrapolation in Attention Mechanisms","date":"2025-01-15","arxiv_id":"2501.08570","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ht-neko/information-entropy-invariance"],"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"]}}},{"paper":null,"slug":"magnet-augmenting-generative-decoders-with","title":"MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities","date":"2025-01-15","arxiv_id":"2501.08648","n_code_links":0,"syntology":null},{"paper":null,"slug":"miafex-an-attention-based-feature-extraction","title":"MIAFEx: An Attention-based Feature Extraction Method for Medical Image Classification","date":"2025-01-15","arxiv_id":"2501.08562","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-class-traffic-assignment-using-multi","title":"Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks","date":"2025-01-15","arxiv_id":"2501.09117","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-view-transformers-for-airway-to-lung","title":"Multi-View Transformers for Airway-To-Lung Ratio Inference on Cardiac CT Scans: The C4R Study","date":"2025-01-15","arxiv_id":"2501.08902","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-fake-news-video-explanation","title":"Multimodal Fake News Video Explanation: Dataset, Analysis and Evaluation","date":"2025-01-15","arxiv_id":"2501.08514","n_code_links":0,"syntology":null},{"paper":null,"slug":"ouroboros-diffusion-exploring-consistent","title":"Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion","date":"2025-01-15","arxiv_id":"2501.09019","n_code_links":0,"syntology":null}],"record_sha256":"561fe318143830ded27d84bd261a89836a4d756741999cee855785253ba7768b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}