{"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/transformer/papers/46","list_of":"/method/transformer","method":"Transformer","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":46,"pages_in_order":140,"rows_per_page":100,"rows":[4501,4600],"of":13999,"counts":{"archive_papers_tagged":13999,"with_a_code_link":6572,"where_syntology_ran_a_sample":2248,"not_listed_spam_title":0,"listed":13999,"listed_where_code_ran":2248,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1919,"every_run_a_failure_of_syntologys_instrument":329,"listed_with_a_run_with_no_instrument_failure":1919,"listed_every_run_a_failure_of_syntologys_instrument":329,"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/transformer","prev":"/method/transformer/papers/45","next":"/method/transformer/papers/47","papers":[{"paper":"/paper/leveraging-speech-for-gesture-detection-in","slug":"leveraging-speech-for-gesture-detection-in","title":"Leveraging Speech for Gesture Detection in Multimodal Communication","date":"2024-04-23","arxiv_id":"2404.14952","n_code_links":1,"syntology":null},{"paper":"/paper/mamba3d-enhancing-local-features-for-3d-point","slug":"mamba3d-enhancing-local-features-for-3d-point","title":"Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model","date":"2024-04-23","arxiv_id":"2404.14966","n_code_links":1,"syntology":{"ran":7,"of":7,"n_ran_checked":6,"n_instrument":1,"unverified":0,"pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["xhanxu/Mamba3D"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"other-tokens-matter-exploring-global-and","title":"Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification","date":"2024-04-23","arxiv_id":"2404.14985","n_code_links":0,"syntology":null},{"paper":null,"slug":"prism-patient-records-interpretation-for","title":"PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models","date":"2024-04-23","arxiv_id":"2404.15549","n_code_links":0,"syntology":null},{"paper":"/paper/pyramid-hierarchical-transformer-for","slug":"pyramid-hierarchical-transformer-for","title":"Pyramid Hierarchical Transformer for Hyperspectral Image Classification","date":"2024-04-23","arxiv_id":"2404.14945","n_code_links":3,"syntology":null},{"paper":null,"slug":"sc-hvppnet-spatial-and-channel-hybrid","title":"SC-HVPPNet: Spatial and Channel Hybrid-Attention Video Post-Processing Network with CNN and Transformer","date":"2024-04-23","arxiv_id":"2404.14709","n_code_links":0,"syntology":null},{"paper":null,"slug":"science-written-by-generative-ai-is-perceived","title":"From Complexity to Clarity: How AI Enhances Perceptions of Scientists and the Public's Understanding of Science","date":"2024-04-23","arxiv_id":"2405.00706","n_code_links":0,"syntology":null},{"paper":"/paper/smpler-taming-transformers-for-monocular-3d","slug":"smpler-taming-transformers-for-monocular-3d","title":"SMPLer: Taming Transformers for Monocular 3D Human Shape and Pose Estimation","date":"2024-04-23","arxiv_id":"2404.15276","n_code_links":1,"syntology":null},{"paper":null,"slug":"thermopore-predicting-part-porosity-based-on","title":"ThermoPore: Predicting Part Porosity Based on Thermal Images Using Deep Learning","date":"2024-04-23","arxiv_id":"2404.16882","n_code_links":0,"syntology":null},{"paper":"/paper/towards-systematic-evaluation-of-logical","slug":"towards-systematic-evaluation-of-logical","title":"LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models","date":"2024-04-23","arxiv_id":"2404.15522","n_code_links":1,"syntology":null},{"paper":"/paper/traditional-to-transformers-a-survey-on","slug":"traditional-to-transformers-a-survey-on","title":"A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models","date":"2024-04-23","arxiv_id":"2404.14955","n_code_links":1,"syntology":null},{"paper":"/paper/a-multimodal-feature-distillation-with-cnn","slug":"a-multimodal-feature-distillation-with-cnn","title":"A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities","date":"2024-04-22","arxiv_id":"2404.14019","n_code_links":1,"syntology":null},{"paper":"/paper/how-well-can-llms-echo-us-evaluating-ai","slug":"how-well-can-llms-echo-us-evaluating-ai","title":"How Well Can LLMs Echo Us? Evaluating AI Chatbots' Role-Play Ability with ECHO","date":"2024-04-22","arxiv_id":"2404.13957","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":6,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cuhk-arise/echo"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"information-re-organization-improves","title":"Information Re-Organization Improves Reasoning in Large Language Models","date":"2024-04-22","arxiv_id":"2404.13985","n_code_links":0,"syntology":null},{"paper":null,"slug":"mambauie-sr-unraveling-the-ocean-s-secrets","title":"MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs","date":"2024-04-22","arxiv_id":"2404.13884","n_code_links":0,"syntology":null},{"paper":"/paper/mixlora-enhancing-large-language-models-fine","slug":"mixlora-enhancing-large-language-models-fine","title":"MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts","date":"2024-04-22","arxiv_id":"2404.15159","n_code_links":2,"syntology":{"ran":6,"of":11,"n_ran_checked":6,"n_instrument":0,"unverified":5,"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) · 5 unverified","official":{"repos":["TUDB-Labs/MixLoRA","mikecovlee/mLoRA"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"navigating-the-path-of-writing-outline-guided","title":"Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models","date":"2024-04-22","arxiv_id":"2404.13919","n_code_links":0,"syntology":null},{"paper":"/paper/spacebyte-towards-deleting-tokenization-from","slug":"spacebyte-towards-deleting-tokenization-from","title":"SpaceByte: Towards Deleting Tokenization from Large Language Modeling","date":"2024-04-22","arxiv_id":"2404.14408","n_code_links":1,"syntology":null},{"paper":"/paper/surgical-desam-decoupling-sam-for-instrument","slug":"surgical-desam-decoupling-sam-for-instrument","title":"Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery","date":"2024-04-22","arxiv_id":"2404.14040","n_code_links":1,"syntology":null},{"paper":null,"slug":"texture-aware-and-shape-guided-transformer","title":"Texture, Shape and Order Matter: A New Transformer Design for Sequential DeepFake Detection","date":"2024-04-22","arxiv_id":"2404.13873","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-nasal-cytology-dataset-for-object-detection","title":"A Nasal Cytology Dataset for Object Detection and Deep Learning","date":"2024-04-21","arxiv_id":"2404.13745","n_code_links":0,"syntology":null},{"paper":"/paper/llms-in-web-development-evaluating-llm","slug":"llms-in-web-development-evaluating-llm","title":"LLMs in Web Development: Evaluating LLM-Generated PHP Code Unveiling Vulnerabilities and Limitations","date":"2024-04-21","arxiv_id":"2404.14459","n_code_links":1,"syntology":null},{"paper":"/paper/masked-latent-transformer-with-the-random","slug":"masked-latent-transformer-with-the-random","title":"Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis of Dental Fluorosis","date":"2024-04-21","arxiv_id":"2404.13564","n_code_links":1,"syntology":null},{"paper":null,"slug":"smartmem-layout-transformation-elimination","title":"SmartMem: Layout Transformation Elimination and Adaptation for Efficient DNN Execution on Mobile","date":"2024-04-21","arxiv_id":"2404.13528","n_code_links":0,"syntology":null},{"paper":"/paper/svgeditbench-a-benchmark-dataset-for","slug":"svgeditbench-a-benchmark-dataset-for","title":"SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilities","date":"2024-04-21","arxiv_id":"2404.13710","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":["mti-lab/svgeditbench"],"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":"/paper/3d-convolution-guided-spectral-spatial","slug":"3d-convolution-guided-spectral-spatial","title":"3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification","date":"2024-04-20","arxiv_id":"2404.13252","n_code_links":1,"syntology":null},{"paper":"/paper/beyond-accuracy-investigating-error-types-in","slug":"beyond-accuracy-investigating-error-types-in","title":"Beyond Accuracy: Investigating Error Types in GPT-4 Responses to USMLE Questions","date":"2024-04-20","arxiv_id":"2404.13307","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparative-analysis-on-snowmelt-driven","title":"Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques","date":"2024-04-20","arxiv_id":"2404.13327","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-as-test-case-generators","slug":"large-language-models-as-test-case-generators","title":"Large Language Models as Test Case Generators: Performance Evaluation and Enhancement","date":"2024-04-20","arxiv_id":"2404.13340","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":null}},{"paper":null,"slug":"nested-tnt-hierarchical-vision-transformers","title":"Nested-TNT: Hierarchical Vision Transformers with Multi-Scale Feature Processing","date":"2024-04-20","arxiv_id":"2404.13434","n_code_links":0,"syntology":null},{"paper":null,"slug":"stridenet-swin-transformer-for-terrain","title":"StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction","date":"2024-04-20","arxiv_id":"2404.13270","n_code_links":0,"syntology":null},{"paper":"/paper/cross-cultural-inspiration-detection-and","slug":"cross-cultural-inspiration-detection-and","title":"Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data","date":"2024-04-19","arxiv_id":"2404.12933","n_code_links":1,"syntology":null},{"paper":"/paper/cyberseceval-2-a-wide-ranging-cybersecurity","slug":"cyberseceval-2-a-wide-ranging-cybersecurity","title":"CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models","date":"2024-04-19","arxiv_id":"2404.13161","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["facebookresearch/purplellama"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"dlora-trocr-mixed-text-mode-optical-character","title":"Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer","date":"2024-04-19","arxiv_id":"2404.12734","n_code_links":0,"syntology":null},{"paper":"/paper/dubo-sql-diverse-retrieval-augmented","slug":"dubo-sql-diverse-retrieval-augmented","title":"Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL","date":"2024-04-19","arxiv_id":"2404.12560","n_code_links":1,"syntology":null},{"paper":"/paper/heterogeneous-subgraph-transformer-for-fake","slug":"heterogeneous-subgraph-transformer-for-fake","title":"Heterogeneous Subgraph Transformer for Fake News Detection","date":"2024-04-19","arxiv_id":"2404.13192","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-robust-ferrous-scrap-material","title":"Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction","date":"2024-04-19","arxiv_id":"2404.13002","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-classification-outcome","title":"Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment","date":"2024-04-19","arxiv_id":"2404.12634","n_code_links":0,"syntology":null},{"paper":null,"slug":"accidentblip2-accident-detection-with-multi","title":"AccidentBlip: Agent of Accident Warning based on MA-former","date":"2024-04-18","arxiv_id":"2404.12149","n_code_links":0,"syntology":null},{"paper":"/paper/advisorqa-towards-helpful-and-harmless-advice","slug":"advisorqa-towards-helpful-and-harmless-advice","title":"AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence","date":"2024-04-18","arxiv_id":"2404.11826","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"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":["minbeomkim/advisorqa"],"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":"bird-a-trustworthy-bayesian-inference","title":"BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models","date":"2024-04-18","arxiv_id":"2404.12494","n_code_links":0,"syntology":null},{"paper":"/paper/caus-a-dataset-for-question-generation-based","slug":"caus-a-dataset-for-question-generation-based","title":"CAUS: A Dataset for Question Generation based on Human Cognition Leveraging Large Language Models","date":"2024-04-18","arxiv_id":"2404.11835","n_code_links":1,"syntology":null},{"paper":null,"slug":"concept-induction-using-llms-a-user","title":"Concept Induction using LLMs: a user experiment for assessment","date":"2024-04-18","arxiv_id":"2404.11875","n_code_links":0,"syntology":null},{"paper":null,"slug":"dst-gtn-dynamic-spatio-temporal-graph","title":"DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting","date":"2024-04-18","arxiv_id":"2404.11996","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-length-extrapolation-in-sequential","title":"Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory","date":"2024-04-18","arxiv_id":"2404.11870","n_code_links":0,"syntology":null},{"paper":"/paper/harnessing-joint-rain-detail-aware","slug":"harnessing-joint-rain-detail-aware","title":"Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains","date":"2024-04-18","arxiv_id":"2404.12091","n_code_links":1,"syntology":null},{"paper":"/paper/openbezoar-small-cost-effective-and-open","slug":"openbezoar-small-cost-effective-and-open","title":"OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data","date":"2024-04-18","arxiv_id":"2404.12195","n_code_links":1,"syntology":null},{"paper":"/paper/shadowrefiner-towards-mask-free-shadow","slug":"shadowrefiner-towards-mask-free-shadow","title":"ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer","date":"2024-04-18","arxiv_id":"2406.02559","n_code_links":1,"syntology":null},{"paper":"/paper/spidepth-strengthened-pose-information-for","slug":"spidepth-strengthened-pose-information-for","title":"SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation","date":"2024-04-18","arxiv_id":"2404.12501","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":11,"n_instrument":2,"unverified":1,"pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Lavreniuk/SPIdepth"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/uncovering-safety-risks-in-open-source-llms","slug":"uncovering-safety-risks-in-open-source-llms","title":"Uncovering Safety Risks of Large Language Models through Concept Activation Vector","date":"2024-04-18","arxiv_id":"2404.12038","n_code_links":1,"syntology":{"ran":4,"of":8,"n_ran_checked":3,"n_instrument":1,"unverified":4,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["sproutnan/ai-safety_scav"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["found_in_text","official"]}}},{"paper":"/paper/x-light-cross-city-traffic-signal-control","slug":"x-light-cross-city-traffic-signal-control","title":"X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner","date":"2024-04-18","arxiv_id":"2404.12090","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["jianghaoyuan1994/x-light"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/ai-enhanced-cognitive-behavioral-therapy-deep","slug":"ai-enhanced-cognitive-behavioral-therapy-deep","title":"AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts","date":"2024-04-17","arxiv_id":"2404.11449","n_code_links":1,"syntology":null},{"paper":"/paper/cross-problem-learning-for-solving-vehicle","slug":"cross-problem-learning-for-solving-vehicle","title":"Cross-Problem Learning for Solving Vehicle Routing Problems","date":"2024-04-17","arxiv_id":"2404.11677","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":5,"n_instrument":2,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 5 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["zhuoyi-lin/cross_problem_learning"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"function-approximation-for-reinforcement","title":"Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves","date":"2024-04-17","arxiv_id":"2404.10991","n_code_links":0,"syntology":null},{"paper":null,"slug":"genfighter-a-generative-and-evolutive-textual","title":"GenFighter: A Generative and Evolutive Textual Attack Removal","date":"2024-04-17","arxiv_id":"2404.11538","n_code_links":0,"syntology":null},{"paper":"/paper/jointvit-modeling-oxygen-saturation-levels","slug":"jointvit-modeling-oxygen-saturation-levels","title":"JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA","date":"2024-04-17","arxiv_id":"2404.11525","n_code_links":1,"syntology":null},{"paper":null,"slug":"multilateral-temporal-view-pyramid","title":"Mumpy: Multilateral Temporal-view Pyramid Transformer for Video Inpainting Detection","date":"2024-04-17","arxiv_id":"2404.11054","n_code_links":0,"syntology":null},{"paper":null,"slug":"octopus-v3-technical-report-for-on-device-sub","title":"Octopus v3: Technical Report for On-device Sub-billion Multimodal AI Agent","date":"2024-04-17","arxiv_id":"2404.11459","n_code_links":0,"syntology":null},{"paper":null,"slug":"pretraining-billion-scale-geospatial","title":"Pretraining Billion-scale Geospatial Foundational Models on Frontier","date":"2024-04-17","arxiv_id":"2404.11706","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-optimizer-of-text-to-image-diffusion","title":"Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding","date":"2024-04-17","arxiv_id":"2404.11589","n_code_links":0,"syntology":null},{"paper":"/paper/rd2bench-toward-data-centric-automatic-r-d","slug":"rd2bench-toward-data-centric-automatic-r-d","title":"Towards Data-Centric Automatic R&D","date":"2024-04-17","arxiv_id":"2404.11276","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-noise-resilience-strategies-in","title":"Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis","date":"2024-04-17","arxiv_id":"2404.11213","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-adaptive-psro-towards-an-automatic","title":"Self-adaptive PSRO: Towards an Automatic Population-based Game Solver","date":"2024-04-17","arxiv_id":"2404.11144","n_code_links":0,"syntology":null},{"paper":null,"slug":"supervised-contrastive-vision-transformer-for","title":"Supervised Contrastive Vision Transformer for Breast Histopathological Image Classification","date":"2024-04-17","arxiv_id":"2404.11052","n_code_links":0,"syntology":null},{"paper":"/paper/towards-coarse-to-fine-evaluation-of","slug":"towards-coarse-to-fine-evaluation-of","title":"Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models","date":"2024-04-17","arxiv_id":"2404.11502","n_code_links":1,"syntology":null},{"paper":"/paper/training-transformer-models-by-wavelet-losses","slug":"training-transformer-models-by-wavelet-losses","title":"Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution","date":"2024-04-17","arxiv_id":"2404.11273","n_code_links":1,"syntology":{"ran":14,"of":17,"n_ran_checked":7,"n_instrument":7,"unverified":3,"pointer_only":7,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 7 where Syntology's instrument failed) · 3 unverified","official":{"repos":["mandalinadagi/wavelettention"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"aghint-attribute-guided-representation","title":"AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer","date":"2024-04-16","arxiv_id":"2404.10443","n_code_links":0,"syntology":null},{"paper":null,"slug":"anomaly-correction-of-business-processes","title":"Anomaly Correction of Business Processes Using Transformer Autoencoder","date":"2024-04-16","arxiv_id":"2404.10211","n_code_links":0,"syntology":null},{"paper":"/paper/can-language-models-solve-olympiad","slug":"can-language-models-solve-olympiad","title":"Can Language Models Solve Olympiad Programming?","date":"2024-04-16","arxiv_id":"2404.10952","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":["princeton-nlp/USACO"],"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":"cotar-chain-of-thought-attribution-reasoning","title":"CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity","date":"2024-04-16","arxiv_id":"2404.10513","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-and-llm-based-methods-applied","slug":"deep-learning-and-llm-based-methods-applied","title":"Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification","date":"2024-04-16","arxiv_id":"2404.10757","n_code_links":1,"syntology":null},{"paper":"/paper/gasformer-a-transformer-based-architecture","slug":"gasformer-a-transformer-based-architecture","title":"Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging","date":"2024-04-16","arxiv_id":"2404.10841","n_code_links":1,"syntology":null},{"paper":"/paper/how-faithful-are-rag-models-quantifying-the","slug":"how-faithful-are-rag-models-quantifying-the","title":"ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence","date":"2024-04-16","arxiv_id":"2404.10198","n_code_links":1,"syntology":null},{"paper":"/paper/incubating-text-classifiers-following-user","slug":"incubating-text-classifiers-following-user","title":"Incubating Text Classifiers Following User Instruction with Nothing but LLM","date":"2024-04-16","arxiv_id":"2404.10877","n_code_links":1,"syntology":null},{"paper":null,"slug":"mathwriting-a-dataset-for-handwritten","title":"MathWriting: A Dataset For Handwritten Mathematical Expression Recognition","date":"2024-04-16","arxiv_id":"2404.10690","n_code_links":0,"syntology":null},{"paper":"/paper/minicheck-efficient-fact-checking-of-llms-on","slug":"minicheck-efficient-fact-checking-of-llms-on","title":"MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents","date":"2024-04-16","arxiv_id":"2404.10774","n_code_links":2,"syntology":{"ran":2,"of":8,"n_ran_checked":2,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"2 ran (of which 1 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) · 6 unverified","official":{"repos":["liyan06/minicheck"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"neuromorphic-vision-based-motion-segmentation","title":"Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network","date":"2024-04-16","arxiv_id":"2404.10940","n_code_links":0,"syntology":null},{"paper":"/paper/search-beyond-queries-training-smaller","slug":"search-beyond-queries-training-smaller","title":"Grounded Language Agent for Product Search via Intelligent Web Interactions","date":"2024-04-16","arxiv_id":"2404.10887","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["MultifacetedNLP/Web-Agents-Unsupervised"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/self-supervised-visual-preference-alignment","slug":"self-supervised-visual-preference-alignment","title":"Self-Supervised Visual Preference Alignment","date":"2024-04-16","arxiv_id":"2404.10501","n_code_links":1,"syntology":{"ran":10,"of":11,"n_ran_checked":6,"n_instrument":4,"unverified":1,"pointer_only":11,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 1 unverified","official":{"repos":["Kevinz-code/SeVa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"social-choice-for-ai-alignment-dealing-with","title":"Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback","date":"2024-04-16","arxiv_id":"2404.10271","n_code_links":0,"syntology":null},{"paper":null,"slug":"tc-ocr-tablecraft-ocr-for-efficient-detection","title":"TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content","date":"2024-04-16","arxiv_id":"2404.10305","n_code_links":0,"syntology":null},{"paper":"/paper/threat-behavior-textual-search-by-attention","slug":"threat-behavior-textual-search-by-attention","title":"Threat Behavior Textual Search by Attention Graph Isomorphism","date":"2024-04-16","arxiv_id":"2404.10944","n_code_links":1,"syntology":null},{"paper":null,"slug":"aigen-an-adversarial-approach-for-instruction","title":"AIGeN: An Adversarial Approach for Instruction Generation in VLN","date":"2024-04-15","arxiv_id":"2404.10054","n_code_links":0,"syntology":null},{"paper":null,"slug":"eyeformer-predicting-personalized-scanpaths","title":"EyeFormer: Predicting Personalized Scanpaths with Transformer-Guided Reinforcement Learning","date":"2024-04-15","arxiv_id":"2404.10163","n_code_links":0,"syntology":null},{"paper":null,"slug":"learn-your-reference-model-for-real-good","title":"Learn Your Reference Model for Real Good Alignment","date":"2024-04-15","arxiv_id":"2404.09656","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-evaluators-recognize-and-favor-their-own","title":"LLM Evaluators Recognize and Favor Their Own Generations","date":"2024-04-15","arxiv_id":"2404.13076","n_code_links":0,"syntology":null},{"paper":null,"slug":"lorap-transformer-sub-layers-deserve","title":"LoRAP: Transformer Sub-Layers Deserve Differentiated Structured Compression for Large Language Models","date":"2024-04-15","arxiv_id":"2404.09695","n_code_links":0,"syntology":null},{"paper":null,"slug":"numerical-attributes-learning-for-cardiac","title":"Are Medium-Sized Transformers Models still Relevant for Medical Records Processing?","date":"2024-04-15","arxiv_id":"2404.10171","n_code_links":0,"syntology":null},{"paper":null,"slug":"odformer-semantic-fundus-image-segmentation","title":"ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection","date":"2024-04-15","arxiv_id":"2405.09552","n_code_links":0,"syntology":null},{"paper":"/paper/segformer3d-an-efficient-transformer-for-3d","slug":"segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","arxiv_id":"2404.10156","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["osupcvlab/segformer3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/state-space-model-for-new-generation-network","slug":"state-space-model-for-new-generation-network","title":"State Space Model for New-Generation Network Alternative to Transformers: A Survey","date":"2024-04-15","arxiv_id":"2404.09516","n_code_links":1,"syntology":null},{"paper":null,"slug":"unveiling-imitation-learning-exploring-the","title":"Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model","date":"2024-04-15","arxiv_id":"2404.09717","n_code_links":0,"syntology":null},{"paper":"/paper/witunet-a-u-shaped-architecture-integrating","slug":"witunet-a-u-shaped-architecture-integrating","title":"WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion","date":"2024-04-15","arxiv_id":"2404.09533","n_code_links":1,"syntology":null},{"paper":"/paper/zero-shot-building-age-classification-from","slug":"zero-shot-building-age-classification-from","title":"Zero-shot Building Age Classification from Facade Image Using GPT-4","date":"2024-04-15","arxiv_id":"2404.09921","n_code_links":1,"syntology":null},{"paper":null,"slug":"arena-a-patch-of-interest-vit-inference","title":"Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics","date":"2024-04-14","arxiv_id":"2404.09245","n_code_links":0,"syntology":null},{"paper":"/paper/max-ast-combining-convolution-local-and","slug":"max-ast-combining-convolution-local-and","title":"MAX-AST: COMBINING CONVOLUTION, LOCAL AND GLOBAL SELF-ATTENTIONS FOR AUDIO EVENT CLASSIFICATION","date":"2024-04-14","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/rf-diffusion-radio-signal-generation-via-time","slug":"rf-diffusion-radio-signal-generation-via-time","title":"RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion","date":"2024-04-14","arxiv_id":"2404.09140","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":3,"phrase":"0 ran · 3 unverified","official":{"repos":["mobicom24/rf-diffusion"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":null,"slug":"transformerfam-feedback-attention-is-working","title":"TransformerFAM: Feedback attention is working memory","date":"2024-04-14","arxiv_id":"2404.09173","n_code_links":0,"syntology":null},{"paper":null,"slug":"heat-head-level-parameter-efficient","title":"Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks","date":"2024-04-13","arxiv_id":"2404.08894","n_code_links":0,"syntology":null},{"paper":"/paper/neurit-pushing-the-limit-of-neural-inertial","slug":"neurit-pushing-the-limit-of-neural-inertial","title":"NeurIT: Pushing the Limit of Neural Inertial Tracking for Indoor Robotic IoT","date":"2024-04-13","arxiv_id":"2404.08939","n_code_links":1,"syntology":null}],"record_sha256":"71a78bdefd9c4c66fcb6ffcf71f3b1f95fbfdf24051c0b3bb8b3ea5a6ad0021a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}