{"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/attention/papers/115","list_of":"/method/attention","method":"Attention","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":115,"pages_in_order":316,"rows_per_page":100,"rows":[11401,11500],"of":31583,"counts":{"archive_papers_tagged":31583,"with_a_code_link":13473,"where_syntology_ran_a_sample":3998,"not_listed_spam_title":0,"listed":31583,"listed_where_code_ran":3998,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3366,"every_run_a_failure_of_syntologys_instrument":632,"listed_with_a_run_with_no_instrument_failure":3366,"listed_every_run_a_failure_of_syntologys_instrument":632,"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/attention","prev":"/method/attention/papers/114","next":"/method/attention/papers/116","papers":[{"paper":"/paper/adversarial-robust-decision-transformer","slug":"adversarial-robust-decision-transformer","title":"Adversarially Robust Decision Transformer","date":"2024-07-25","arxiv_id":"2407.18414","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["xiaohangt/ardt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/attentionhand-text-driven-controllable-hand","slug":"attentionhand-text-driven-controllable-hand","title":"AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild","date":"2024-07-25","arxiv_id":"2407.18034","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["redorangeyellowy/AttentionHand"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"banyan-improved-representation-learning-with","title":"Banyan: Improved Representation Learning with Explicit Structure","date":"2024-07-25","arxiv_id":"2407.17771","n_code_links":0,"syntology":null},{"paper":null,"slug":"closing-the-gap-between-open-source-and","title":"Closing the gap between open-source and commercial large language models for medical evidence summarization","date":"2024-07-25","arxiv_id":"2408.00588","n_code_links":0,"syntology":null},{"paper":"/paper/cost-effective-instruction-learning-for","slug":"cost-effective-instruction-learning-for","title":"Cost-effective Instruction Learning for Pathology Vision and Language Analysis","date":"2024-07-25","arxiv_id":"2407.17734","n_code_links":1,"syntology":null},{"paper":"/paper/cswin-unet-transformer-unet-with-cross-shaped","slug":"cswin-unet-transformer-unet-with-cross-shaped","title":"CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation","date":"2024-07-25","arxiv_id":"2407.18070","n_code_links":1,"syntology":null},{"paper":"/paper/dac-2d-3d-retrieval-with-noisy-labels-via","slug":"dac-2d-3d-retrieval-with-noisy-labels-via","title":"DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction","date":"2024-07-25","arxiv_id":"2407.17779","n_code_links":1,"syntology":null},{"paper":"/paper/detection-of-manatee-vocalisations-using-the","slug":"detection-of-manatee-vocalisations-using-the","title":"Detection of manatee vocalisations using the Audio Spectrogram Transformer","date":"2024-07-25","arxiv_id":"2407.18083","n_code_links":1,"syntology":null},{"paper":null,"slug":"ecg-arrhythmia-detection-using-disease","title":"ECG Arrhythmia Detection Using Disease-specific Attention-based Deep Learning Model","date":"2024-07-25","arxiv_id":"2407.18033","n_code_links":0,"syntology":null},{"paper":null,"slug":"eeg-ssm-leveraging-state-space-model-for","title":"EEG-SSM: Leveraging State-Space Model for Dementia Detection","date":"2024-07-25","arxiv_id":"2407.17801","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-llm-training-and-serving-with","title":"S2-Attention: Hardware-Aware Context Sharding Among Attention Heads","date":"2024-07-25","arxiv_id":"2407.17678","n_code_links":0,"syntology":null},{"paper":null,"slug":"energy-efficient-aerial-ris-phase-shift","title":"Energy Efficient Aerial RIS: Phase Shift Optimization and Trajectory Design","date":"2024-07-25","arxiv_id":"2407.17989","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-power-enhancements-for-testing-many","title":"Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $\\infty$-norm","date":"2024-07-25","arxiv_id":"2407.17888","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-the-limitations-of-kolmogorov","slug":"exploring-the-limitations-of-kolmogorov","title":"Exploring the Limitations of Kolmogorov-Arnold Networks in Classification: Insights to Software Training and Hardware Implementation","date":"2024-07-25","arxiv_id":"2407.17790","n_code_links":1,"syntology":null},{"paper":null,"slug":"guided-latent-slot-diffusion-for-object","title":"Guided Latent Slot Diffusion for Object-Centric Learning","date":"2024-07-25","arxiv_id":"2407.17929","n_code_links":0,"syntology":null},{"paper":"/paper/harnessing-temporal-causality-for-advanced","slug":"harnessing-temporal-causality-for-advanced","title":"Harnessing Temporal Causality for Advanced Temporal Action Detection","date":"2024-07-25","arxiv_id":"2407.17792","n_code_links":1,"syntology":null},{"paper":null,"slug":"hg-pipe-vision-transformer-acceleration-with","title":"HG-PIPE: Vision Transformer Acceleration with Hybrid-Grained Pipeline","date":"2024-07-25","arxiv_id":"2407.17879","n_code_links":0,"syntology":null},{"paper":null,"slug":"how-lightweight-can-a-vision-transformer-be","title":"How Lightweight Can A Vision Transformer Be","date":"2024-07-25","arxiv_id":"2407.17783","n_code_links":0,"syntology":null},{"paper":null,"slug":"is-the-digital-forensics-and-incident","title":"Is the Digital Forensics and Incident Response Pipeline Ready for Text-Based Threats in LLM Era?","date":"2024-07-25","arxiv_id":"2407.17870","n_code_links":0,"syntology":null},{"paper":null,"slug":"keep-the-cost-down-a-review-on-methods-to","title":"Keep the Cost Down: A Review on Methods to Optimize LLM' s KV-Cache Consumption","date":"2024-07-25","arxiv_id":"2407.18003","n_code_links":0,"syntology":null},{"paper":"/paper/mew-multiplexed-immunofluorescence-image","slug":"mew-multiplexed-immunofluorescence-image","title":"Mew: Multiplexed Immunofluorescence Image Analysis through an Efficient Multiplex Network","date":"2024-07-25","arxiv_id":"2407.17857","n_code_links":1,"syntology":{"ran":11,"of":11,"n_ran_checked":11,"n_instrument":0,"unverified":0,"pointer_only":11,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["unites-lab/mew"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/peft-u-parameter-efficient-fine-tuning-for","slug":"peft-u-parameter-efficient-fine-tuning-for","title":"PEFT-U: Parameter-Efficient Fine-Tuning for User Personalization","date":"2024-07-25","arxiv_id":"2407.18078","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"pointer_only":5,"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) · 2 unverified","official":{"repos":["ChrisIsKing/Parameter-Efficient-Personalization"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/personagym-evaluating-persona-agents-and-llms","slug":"personagym-evaluating-persona-agents-and-llms","title":"PersonaGym: Evaluating Persona Agents and LLMs","date":"2024-07-25","arxiv_id":"2407.18416","n_code_links":1,"syntology":null},{"paper":"/paper/positive-text-reframing-under-multi-strategy","slug":"positive-text-reframing-under-multi-strategy","title":"Positive Text Reframing under Multi-strategy Optimization","date":"2024-07-25","arxiv_id":"2407.17940","n_code_links":1,"syntology":null},{"paper":null,"slug":"privacy-threats-and-countermeasures-in","title":"Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review","date":"2024-07-25","arxiv_id":"2407.18096","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-inspired-evolutionary-algorithms-for","title":"Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey","date":"2024-07-25","arxiv_id":"2407.17946","n_code_links":0,"syntology":null},{"paper":"/paper/refmask3d-language-guided-transformer-for-3d","slug":"refmask3d-language-guided-transformer-for-3d","title":"RefMask3D: Language-Guided Transformer for 3D Referring Segmentation","date":"2024-07-25","arxiv_id":"2407.18244","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["heshuting555/refmask3d"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"relating-the-seemingly-unrelated-principled","title":"Relating the Seemingly Unrelated: Principled Understanding of Generalization for Generative Models in Arithmetic Reasoning Tasks","date":"2024-07-25","arxiv_id":"2407.17963","n_code_links":0,"syntology":null},{"paper":null,"slug":"roberta-resnext-and-bilstm-with-self","title":"RoBERTa, ResNeXt and BiLSTM with self-attention: The ultimate trio for customer sentiment analysis","date":"2024-07-25","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/self-supervision-improves-diffusion-models","slug":"self-supervision-improves-diffusion-models","title":"Self-Supervision Improves Diffusion Models for Tabular Data Imputation","date":"2024-07-25","arxiv_id":"2407.18013","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yixinliu233/simpdm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/self-training-with-direct-preference","slug":"self-training-with-direct-preference","title":"Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning","date":"2024-07-25","arxiv_id":"2407.18248","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tianduowang/dpo-st"],"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"]}}},{"paper":null,"slug":"the-geometry-of-queries-query-based","title":"The Geometry of Queries: Query-Based Innovations in Retrieval-Augmented Generation","date":"2024-07-25","arxiv_id":"2407.18044","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-combining-data-and-knowledge-gpt","title":"The Power of Combining Data and Knowledge: GPT-4o is an Effective Interpreter of Machine Learning Models in Predicting Lymph Node Metastasis of Lung Cancer","date":"2024-07-25","arxiv_id":"2407.17900","n_code_links":0,"syntology":null},{"paper":null,"slug":"trajectory-aligned-space-time-tokens-for-few","title":"Trajectory-aligned Space-time Tokens for Few-shot Action Recognition","date":"2024-07-25","arxiv_id":"2407.18249","n_code_links":0,"syntology":null},{"paper":null,"slug":"trust-or-escalate-llm-judges-with-provable","title":"Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement","date":"2024-07-25","arxiv_id":"2407.18370","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-the-interplay-of-scale-data-and","title":"Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERT","date":"2024-07-25","arxiv_id":"2407.21058","n_code_links":0,"syntology":null},{"paper":"/paper/unified-lexical-representation-for","slug":"unified-lexical-representation-for","title":"Unified Lexical Representation for Interpretable Visual-Language Alignment","date":"2024-07-25","arxiv_id":"2407.17827","n_code_links":1,"syntology":null},{"paper":null,"slug":"your-graph-recommender-is-provably-a-single","title":"Your Graph Recommender is Provably a Single-view Graph Contrastive Learning","date":"2024-07-25","arxiv_id":"2407.17723","n_code_links":0,"syntology":null},{"paper":null,"slug":"2407-21056","title":"What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers","date":"2024-07-24","arxiv_id":"2407.21056","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-01454","title":"Amman City, Jordan: Toward a Sustainable City from the Ground Up","date":"2024-07-24","arxiv_id":"2408.01454","n_code_links":0,"syntology":null},{"paper":"/paper/3dattgan-a-3d-attention-based-generative","slug":"3dattgan-a-3d-attention-based-generative","title":"3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-Resolution","date":"2024-07-24","arxiv_id":"2407.16965","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-comprehensive-approach-to-misspelling","title":"A Comprehensive Approach to Misspelling Correction with BERT and Levenshtein Distance","date":"2024-07-24","arxiv_id":"2407.17383","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-two-step-fine-tuning-pipeline-for","title":"A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks","date":"2024-07-24","arxiv_id":"2407.17284","n_code_links":0,"syntology":null},{"paper":null,"slug":"ahmf-adaptive-hybrid-memory-fusion-model-for","title":"AHMF: Adaptive Hybrid-Memory-Fusion Model for Driver Attention Prediction","date":"2024-07-24","arxiv_id":"2407.17442","n_code_links":0,"syntology":null},{"paper":null,"slug":"bailicai-a-domain-optimized-retrieval","title":"Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications","date":"2024-07-24","arxiv_id":"2407.21055","n_code_links":0,"syntology":null},{"paper":"/paper/case-enhanced-vision-transformer-improving","slug":"case-enhanced-vision-transformer-improving","title":"Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric","date":"2024-07-24","arxiv_id":"2407.16981","n_code_links":1,"syntology":null},{"paper":null,"slug":"cheems-wonderful-matrices-more-efficient-and","title":"Wonderful Matrices: More Efficient and Effective Architecture for Language Modeling Tasks","date":"2024-07-24","arxiv_id":"2407.16958","n_code_links":0,"syntology":null},{"paper":null,"slug":"continual-learning-in-bio-plausible-spiking","title":"Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective","date":"2024-07-24","arxiv_id":"2407.17305","n_code_links":0,"syntology":null},{"paper":"/paper/curriculum-negative-mining-for-temporal","slug":"curriculum-negative-mining-for-temporal","title":"Curriculum Negative Mining For Temporal Networks","date":"2024-07-24","arxiv_id":"2407.17070","n_code_links":1,"syntology":null},{"paper":"/paper/dependency-transformer-grammars-integrating","slug":"dependency-transformer-grammars-integrating","title":"Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models","date":"2024-07-24","arxiv_id":"2407.17406","n_code_links":1,"syntology":null},{"paper":null,"slug":"domain-generalized-recaptured-screen-image","title":"Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer","date":"2024-07-24","arxiv_id":"2407.17170","n_code_links":0,"syntology":null},{"paper":"/paper/dvpe-divided-view-position-embedding-for","slug":"dvpe-divided-view-position-embedding-for","title":"DVPE: Divided View Position Embedding for Multi-View 3D Object Detection","date":"2024-07-24","arxiv_id":"2407.16955","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 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) · 1 unverified","official":{"repos":["dop0/dvpe"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/dynamic-graph-transformer-with-correlated","slug":"dynamic-graph-transformer-with-correlated","title":"Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding","date":"2024-07-24","arxiv_id":"2407.16959","n_code_links":1,"syntology":null},{"paper":null,"slug":"early-screening-of-potential-breakthrough","title":"Early screening of potential breakthrough technologies with enhanced interpretability: A patent-specific hierarchical attention network model","date":"2024-07-24","arxiv_id":"2407.16939","n_code_links":0,"syntology":null},{"paper":"/paper/embedding-free-transformer-with-inference","slug":"embedding-free-transformer-with-inference","title":"Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic Segmentation","date":"2024-07-24","arxiv_id":"2407.17261","n_code_links":1,"syntology":null},{"paper":null,"slug":"explainable-artificial-intelligence-7","title":"Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients","date":"2024-07-24","arxiv_id":"2407.17165","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-and-local-confidence-based-fraud","title":"Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection","date":"2024-07-24","arxiv_id":"2407.17333","n_code_links":0,"syntology":null},{"paper":"/paper/graph-neural-networks-a-suitable-alternative","slug":"graph-neural-networks-a-suitable-alternative","title":"Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?","date":"2024-07-24","arxiv_id":"2407.17219","n_code_links":1,"syntology":null},{"paper":"/paper/i-could-ve-asked-that-reformulating","slug":"i-could-ve-asked-that-reformulating","title":"I Could've Asked That: Reformulating Unanswerable Questions","date":"2024-07-24","arxiv_id":"2407.17469","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-icd-coding-using-chapter-based","title":"Improving ICD coding using Chapter based Named Entities and Attentional Models","date":"2024-07-24","arxiv_id":"2407.17230","n_code_links":0,"syntology":null},{"paper":null,"slug":"label-alignment-and-reassignment-with","title":"Label Alignment and Reassignment with Generalist Large Language Model for Enhanced Cross-Domain Named Entity Recognition","date":"2024-07-24","arxiv_id":"2407.17344","n_code_links":0,"syntology":null},{"paper":"/paper/loformer-local-frequency-transformer-for","slug":"loformer-local-frequency-transformer-for","title":"LoFormer: Local Frequency Transformer for Image Deblurring","date":"2024-07-24","arxiv_id":"2407.16993","n_code_links":2,"syntology":null},{"paper":"/paper/must-multi-scale-transformers-for-surgical","slug":"must-multi-scale-transformers-for-surgical","title":"MuST: Multi-Scale Transformers for Surgical Phase Recognition","date":"2024-07-24","arxiv_id":"2407.17361","n_code_links":1,"syntology":null},{"paper":null,"slug":"online-social-network-data-driven-early","title":"Online Social Network Data-Driven Early Detection on Short-Form Video Addiction","date":"2024-07-24","arxiv_id":"2407.18277","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-challenges-on-fairness-of-artificial","title":"Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications","date":"2024-07-24","arxiv_id":"2407.16953","n_code_links":0,"syntology":null},{"paper":"/paper/pretraining-a-neural-operator-in-lower","slug":"pretraining-a-neural-operator-in-lower","title":"Pretraining a Neural Operator in Lower Dimensions","date":"2024-07-24","arxiv_id":"2407.17616","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["baratilab/prelowd"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"quality-assured-rethinking-annotation","title":"Quality Assured: Rethinking Annotation Strategies in Imaging AI","date":"2024-07-24","arxiv_id":"2407.17596","n_code_links":0,"syntology":null},{"paper":null,"slug":"reporting-and-analysing-the-environmental","title":"Reporting and Analysing the Environmental Impact of Language Models on the Example of Commonsense Question Answering with External Knowledge","date":"2024-07-24","arxiv_id":"2408.01453","n_code_links":0,"syntology":null},{"paper":"/paper/rt-detrv2-improved-baseline-with-bag-of","slug":"rt-detrv2-improved-baseline-with-bag-of","title":"RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer","date":"2024-07-24","arxiv_id":"2407.17140","n_code_links":3,"syntology":null},{"paper":null,"slug":"sma-hyper-spatiotemporal-multi-view-fusion","title":"SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction","date":"2024-07-24","arxiv_id":"2407.17642","n_code_links":0,"syntology":null},{"paper":null,"slug":"speech-editing-a-summary","title":"Speech Editing -- a Summary","date":"2024-07-24","arxiv_id":"2407.17172","n_code_links":0,"syntology":null},{"paper":null,"slug":"testing-large-language-models-on-driving","title":"Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles","date":"2024-07-24","arxiv_id":"2407.17211","n_code_links":0,"syntology":null},{"paper":"/paper/towards-robust-knowledge-tracing-models-via-k","slug":"towards-robust-knowledge-tracing-models-via-k","title":"Towards Robust Knowledge Tracing Models via k-Sparse Attention","date":"2024-07-24","arxiv_id":"2407.17097","n_code_links":2,"syntology":null},{"paper":"/paper/trans2unet-neural-fusion-for-nuclei-semantic","slug":"trans2unet-neural-fusion-for-nuclei-semantic","title":"Trans2Unet: Neural fusion for Nuclei Semantic Segmentation","date":"2024-07-24","arxiv_id":"2407.17181","n_code_links":0,"syntology":null},{"paper":"/paper/uncertainty-based-ensemble-learning-for","slug":"uncertainty-based-ensemble-learning-for","title":"Uncertainty-Based Ensemble Learning For Speech Classification","date":"2024-07-24","arxiv_id":"2407.17009","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-multitask-deep-learning-model-for","title":"A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset","date":"2024-07-23","arxiv_id":"2407.16384","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-of-text-style-transfer-applications","title":"A Survey of Text Style Transfer: Applications and Ethical Implications","date":"2024-07-23","arxiv_id":"2407.16737","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyzing-the-polysemy-evolution-using","title":"Analyzing Polysemy Evolution Using Semantic Cells","date":"2024-07-23","arxiv_id":"2407.16110","n_code_links":0,"syntology":null},{"paper":null,"slug":"artificial-intelligence-in-extracting","title":"Artificial Intelligence in Extracting Diagnostic Data from Dental Records","date":"2024-07-23","arxiv_id":"2407.21050","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-large-language-models-automatically","title":"Can Large Language Models Automatically Jailbreak GPT-4V?","date":"2024-07-23","arxiv_id":"2407.16686","n_code_links":0,"syntology":null},{"paper":"/paper/channel-partitioned-windowed-attention-and","slug":"channel-partitioned-windowed-attention-and","title":"Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution","date":"2024-07-23","arxiv_id":"2407.16232","n_code_links":0,"syntology":null},{"paper":"/paper/data-mixture-inference-what-do-bpe-tokenizers","slug":"data-mixture-inference-what-do-bpe-tokenizers","title":"Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?","date":"2024-07-23","arxiv_id":"2407.16607","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 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","official":{"repos":["alisawuffles/tokenizer-attack"],"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"]}}},{"paper":null,"slug":"ddk-distilling-domain-knowledge-for-efficient","title":"DDK: Distilling Domain Knowledge for Efficient Large Language Models","date":"2024-07-23","arxiv_id":"2407.16154","n_code_links":0,"syntology":null},{"paper":"/paper/diff-shadow-global-guided-diffusion-model-for","slug":"diff-shadow-global-guided-diffusion-model-for","title":"Diff-Shadow: Global-guided Diffusion Model for Shadow Removal","date":"2024-07-23","arxiv_id":"2407.16214","n_code_links":1,"syntology":null},{"paper":null,"slug":"diffusion-transformer-captures-spatial","title":"Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data","date":"2024-07-23","arxiv_id":"2407.16134","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-llms-know-when-to-not-answer-investigating","title":"Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models","date":"2024-07-23","arxiv_id":"2407.16221","n_code_links":0,"syntology":null},{"paper":"/paper/eianet-a-novel-domain-adaptation-approach-to","slug":"eianet-a-novel-domain-adaptation-approach-to","title":"EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles","date":"2024-07-23","arxiv_id":"2407.16189","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-llm-s-cognition-via-structurization","slug":"enhancing-llm-s-cognition-via-structurization","title":"Enhancing LLM's Cognition via Structurization","date":"2024-07-23","arxiv_id":"2407.16434","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"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","official":{"repos":["alibaba/struxgpt"],"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"]}}},{"paper":null,"slug":"evaluating-long-range-dependency-handling-in","title":"Evaluating Long Range Dependency Handling in Code Generation Models using Multi-Step Key Retrieval","date":"2024-07-23","arxiv_id":"2407.21049","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-neural-burden-in-pruned-models","title":"Exploring The Neural Burden In Pruned Models: An Insight Inspired By Neuroscience","date":"2024-07-23","arxiv_id":"2407.16716","n_code_links":0,"syntology":null},{"paper":null,"slug":"forecasting-automotive-supply-chain","title":"Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series","date":"2024-07-23","arxiv_id":"2407.16739","n_code_links":0,"syntology":null},{"paper":null,"slug":"hsvlt-hierarchical-scale-aware-vision","title":"HSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification","date":"2024-07-23","arxiv_id":"2407.16244","n_code_links":0,"syntology":null},{"paper":"/paper/hytas-a-hyperspectral-image-transformer","slug":"hytas-a-hyperspectral-image-transformer","title":"HyTAS: A Hyperspectral Image Transformer Architecture Search Benchmark and Analysis","date":"2024-07-23","arxiv_id":"2407.16269","n_code_links":1,"syntology":null},{"paper":null,"slug":"lawluo-a-chinese-law-firm-co-run-by-llm","title":"LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation","date":"2024-07-23","arxiv_id":"2407.16252","n_code_links":0,"syntology":null},{"paper":"/paper/lawma-the-power-of-specialization-for-legal","slug":"lawma-the-power-of-specialization-for-legal","title":"Lawma: The Power of Specialization for Legal Tasks","date":"2024-07-23","arxiv_id":"2407.16615","n_code_links":0,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"masked-graph-learning-with-recurrent","title":"Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversation","date":"2024-07-23","arxiv_id":"2407.16714","n_code_links":0,"syntology":null},{"paper":null,"slug":"microemo-time-sensitive-multimodal-emotion","title":"MicroEmo: Time-Sensitive Multimodal Emotion Recognition with Micro-Expression Dynamics in Video Dialogues","date":"2024-07-23","arxiv_id":"2407.16552","n_code_links":0,"syntology":null},{"paper":"/paper/monowad-weather-adaptive-diffusion-model-for","slug":"monowad-weather-adaptive-diffusion-model-for","title":"MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection","date":"2024-07-23","arxiv_id":"2407.16448","n_code_links":1,"syntology":{"ran":15,"of":26,"n_ran_checked":14,"n_instrument":1,"unverified":11,"pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 11 unverified","official":{"repos":["visualaikhu/monowad"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":11,"ran_from_kinds":["official"]}}},{"paper":"/paper/on-the-benefits-of-rank-in-attention-layers","slug":"on-the-benefits-of-rank-in-attention-layers","title":"On the Benefits of Rank in Attention Layers","date":"2024-07-23","arxiv_id":"2407.16153","n_code_links":1,"syntology":null},{"paper":"/paper/origen-enhancing-rtl-code-generation-with","slug":"origen-enhancing-rtl-code-generation-with","title":"OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection","date":"2024-07-23","arxiv_id":"2407.16237","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":1,"n_instrument":0,"unverified":2,"pointer_only":3,"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) · 2 unverified","official":{"repos":["pku-liang/origen"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}}],"record_sha256":"e541fac1bf263f98387d4d374f586da8a533ef79c3aaccad392bc1a2f8944150","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}