{"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/dropout/papers/55","list_of":"/method/dropout","method":"Dropout","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":55,"pages_in_order":275,"rows_per_page":100,"rows":[5401,5500],"of":27472,"counts":{"archive_papers_tagged":27472,"with_a_code_link":12129,"where_syntology_ran_a_sample":3620,"not_listed_spam_title":0,"listed":27472,"listed_where_code_ran":3620,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3044,"every_run_a_failure_of_syntologys_instrument":576,"listed_with_a_run_with_no_instrument_failure":3044,"listed_every_run_a_failure_of_syntologys_instrument":576,"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/dropout","prev":"/method/dropout/papers/54","next":"/method/dropout/papers/56","papers":[{"paper":"/paper/communitykg-rag-leveraging-community","slug":"communitykg-rag-leveraging-community","title":"CommunityKG-RAG: Leveraging Community Structures in Knowledge Graphs for Advanced Retrieval-Augmented Generation in Fact-Checking","date":"2024-08-16","arxiv_id":"2408.08535","n_code_links":1,"syntology":null},{"paper":"/paper/fine-tuning-llms-for-autonomous-spacecraft","slug":"fine-tuning-llms-for-autonomous-spacecraft","title":"Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space Program","date":"2024-08-16","arxiv_id":"2408.08676","n_code_links":1,"syntology":null},{"paper":null,"slug":"geotransformer-enhancing-urban-forecasting","title":"GeoTransformer: Enhancing Urban Forecasting with Dependency Retrieval and Geospatial Attention","date":"2024-08-16","arxiv_id":"2408.08852","n_code_links":0,"syntology":null},{"paper":null,"slug":"hycot-hyperspectral-compression-transformer","title":"HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression","date":"2024-08-16","arxiv_id":"2408.08700","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-vte-identification-through-language","title":"Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT","date":"2024-08-16","arxiv_id":"2408.09043","n_code_links":0,"syntology":null},{"paper":"/paper/mat-sed-amasked-audio-transformer-with-masked","slug":"mat-sed-amasked-audio-transformer-with-masked","title":"MAT-SED: A Masked Audio Transformer with Masked-Reconstruction Based Pre-training for Sound Event Detection","date":"2024-08-16","arxiv_id":"2408.08673","n_code_links":1,"syntology":null},{"paper":null,"slug":"meta-knowledge-for-retrieval-augmented-large","title":"Meta Knowledge for Retrieval Augmented Large Language Models","date":"2024-08-16","arxiv_id":"2408.09017","n_code_links":0,"syntology":null},{"paper":"/paper/multi-granularity-part-sampling-attention-for","slug":"multi-granularity-part-sampling-attention-for","title":"Multi-Granularity Part Sampling Attention for Fine-Grained Visual Classification","date":"2024-08-16","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/opencity-open-spatio-temporal-foundation","slug":"opencity-open-spatio-temporal-foundation","title":"OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction","date":"2024-08-16","arxiv_id":"2408.10269","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":["hkuds/opencity"],"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":"persona-is-a-double-edged-sword-enhancing-the","title":"Persona is a Double-edged Sword: Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks","date":"2024-08-16","arxiv_id":"2408.08631","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-the-effectiveness-of-student","title":"Quantifying the Effectiveness of Student Organization Activities using Natural Language Processing","date":"2024-08-16","arxiv_id":"2408.08694","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-personalized-compression","title":"Research on Personalized Compression Algorithm for Pre-trained Models Based on Homomorphic Entropy Increase","date":"2024-08-16","arxiv_id":"2408.08684","n_code_links":0,"syntology":null},{"paper":"/paper/se-sgformer-a-self-explainable-signed-graph","slug":"se-sgformer-a-self-explainable-signed-graph","title":"Self-Explainable Graph Transformer for Link Sign Prediction","date":"2024-08-16","arxiv_id":"2408.08754","n_code_links":1,"syntology":{"ran":3,"of":9,"n_ran_checked":3,"n_instrument":0,"unverified":6,"pointer_only":9,"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) · 6 unverified","official":{"repos":["liule66/SE-SGformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":"/paper/see-what-llms-cannot-answer-a-self-challenge","slug":"see-what-llms-cannot-answer-a-self-challenge","title":"See What LLMs Cannot Answer: A Self-Challenge Framework for Uncovering LLM Weaknesses","date":"2024-08-16","arxiv_id":"2408.08978","n_code_links":1,"syntology":null},{"paper":"/paper/tamer-tree-aware-transformer-for-handwritten","slug":"tamer-tree-aware-transformer-for-handwritten","title":"TAMER: Tree-Aware Transformer for Handwritten Mathematical Expression Recognition","date":"2024-08-16","arxiv_id":"2408.08578","n_code_links":1,"syntology":{"ran":5,"of":7,"n_ran_checked":5,"n_instrument":0,"unverified":2,"pointer_only":7,"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":["qingzhenduyu/tamer"],"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":"/paper/task-aware-dynamic-transformer-for-efficient","slug":"task-aware-dynamic-transformer-for-efficient","title":"Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution","date":"2024-08-16","arxiv_id":"2408.08736","n_code_links":1,"syntology":null},{"paper":"/paper/the-fellowship-of-the-llms-multi-agent","slug":"the-fellowship-of-the-llms-multi-agent","title":"The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation","date":"2024-08-16","arxiv_id":"2408.08688","n_code_links":1,"syntology":null},{"paper":null,"slug":"vera-validation-and-evaluation-of-retrieval","title":"VERA: Validation and Evaluation of Retrieval-Augmented Systems","date":"2024-08-16","arxiv_id":"2409.03759","n_code_links":0,"syntology":null},{"paper":"/paper/arablegaleval-a-multitask-benchmark-for","slug":"arablegaleval-a-multitask-benchmark-for","title":"ArabLegalEval: A Multitask Benchmark for Assessing Arabic Legal Knowledge in Large Language Models","date":"2024-08-15","arxiv_id":"2408.07983","n_code_links":1,"syntology":null},{"paper":null,"slug":"benchmarking-the-capabilities-of-large","title":"Benchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviors","date":"2024-08-15","arxiv_id":"2408.08302","n_code_links":0,"syntology":null},{"paper":"/paper/beyond-uniform-query-distribution-key-driven","slug":"beyond-uniform-query-distribution-key-driven","title":"Beyond Uniform Query Distribution: Key-Driven Grouped Query Attention","date":"2024-08-15","arxiv_id":"2408.08454","n_code_links":1,"syntology":null},{"paper":"/paper/computer-vision-model-compression-techniques","slug":"computer-vision-model-compression-techniques","title":"Computer Vision Model Compression Techniques for Embedded Systems: A Survey","date":"2024-08-15","arxiv_id":"2408.08250","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-data-sketches-and-fine-tuning-for","title":"Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer","date":"2024-08-15","arxiv_id":"2408.08456","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-the-validity-of-word-level","slug":"evaluating-the-validity-of-word-level","title":"Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models","date":"2024-08-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/fusechat-knowledge-fusion-of-chat-models-1","slug":"fusechat-knowledge-fusion-of-chat-models-1","title":"FuseChat: Knowledge Fusion of Chat Models","date":"2024-08-15","arxiv_id":"2408.07990","n_code_links":3,"syntology":null},{"paper":"/paper/graph-retrieval-augmented-generation-a-survey","slug":"graph-retrieval-augmented-generation-a-survey","title":"Graph Retrieval-Augmented Generation: A Survey","date":"2024-08-15","arxiv_id":"2408.08921","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-web-crawled-data-for-high-quality","slug":"leveraging-web-crawled-data-for-high-quality","title":"Leveraging Web-Crawled Data for High-Quality Fine-Tuning","date":"2024-08-15","arxiv_id":"2408.08003","n_code_links":1,"syntology":null},{"paper":"/paper/mag-sql-multi-agent-generative-approach-with","slug":"mag-sql-multi-agent-generative-approach-with","title":"MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL","date":"2024-08-15","arxiv_id":"2408.07930","n_code_links":1,"syntology":{"ran":21,"of":25,"n_ran_checked":19,"n_instrument":2,"unverified":4,"pointer_only":4,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 3 violated, 15 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["LancelotXWX/MAG-SQL"],"state":"official (archive's flag): 21 ran","n_ran":21,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/mambavt-spatio-temporal-contextual-modeling","slug":"mambavt-spatio-temporal-contextual-modeling","title":"MambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking","date":"2024-08-15","arxiv_id":"2408.07889","n_code_links":1,"syntology":null},{"paper":null,"slug":"plan-with-code-comparing-approaches-for","title":"Plan with Code: Comparing approaches for robust NL to DSL generation","date":"2024-08-15","arxiv_id":"2408.08335","n_code_links":0,"syntology":null},{"paper":null,"slug":"polaris-open-ended-interactive-robotic","title":"Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models","date":"2024-08-15","arxiv_id":"2408.07975","n_code_links":0,"syntology":null},{"paper":"/paper/pqv-mobile-a-combined-pruning-and","slug":"pqv-mobile-a-combined-pruning-and","title":"PQV-Mobile: A Combined Pruning and Quantization Toolkit to Optimize Vision Transformers for Mobile Applications","date":"2024-08-15","arxiv_id":"2408.08437","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-lung-cancer-patient-prognosis-with","title":"Predicting Lung Cancer Patient Prognosis with Large Language Models","date":"2024-08-15","arxiv_id":"2408.07971","n_code_links":0,"syntology":null},{"paper":"/paper/ragchecker-a-fine-grained-framework-for","slug":"ragchecker-a-fine-grained-framework-for","title":"RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation","date":"2024-08-15","arxiv_id":"2408.08067","n_code_links":1,"syntology":{"ran":3,"of":5,"n_ran_checked":3,"n_instrument":0,"unverified":2,"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) · 2 unverified","official":{"repos":["amazon-science/ragchecker"],"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/unsupervised-part-discovery-via-dual","slug":"unsupervised-part-discovery-via-dual","title":"Unsupervised Part Discovery via Dual Representation Alignment","date":"2024-08-15","arxiv_id":"2408.08108","n_code_links":1,"syntology":null},{"paper":null,"slug":"your-turn-real-world-turning-angle-estimation","title":"Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment","date":"2024-08-15","arxiv_id":"2408.08182","n_code_links":0,"syntology":null},{"paper":"/paper/a-spitting-image-modular-superpixel","slug":"a-spitting-image-modular-superpixel","title":"A Spitting Image: Modular Superpixel Tokenization in Vision Transformers","date":"2024-08-14","arxiv_id":"2408.07680","n_code_links":1,"syntology":{"ran":16,"of":18,"n_ran_checked":16,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["dsb-ifi/spit"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"codemirage-hallucinations-in-code-generated","title":"CodeMirage: Hallucinations in Code Generated by Large Language Models","date":"2024-08-14","arxiv_id":"2408.08333","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-aware-early-fusion-with-stage-divided","title":"Cross-aware Early Fusion with Stage-divided Vision and Language Transformer Encoders for Referring Image Segmentation","date":"2024-08-14","arxiv_id":"2408.07539","n_code_links":0,"syntology":null},{"paper":"/paper/datavist5-a-pre-trained-language-model-for","slug":"datavist5-a-pre-trained-language-model-for","title":"DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization","date":"2024-08-14","arxiv_id":"2408.07401","n_code_links":1,"syntology":null},{"paper":null,"slug":"end-to-end-semantic-centric-video-based","title":"End-to-end Semantic-centric Video-based Multimodal Affective Computing","date":"2024-08-14","arxiv_id":"2408.07694","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-visual-question-answering-through-1","title":"Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion","date":"2024-08-14","arxiv_id":"2408.07303","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-retrieval-augmented-generation-in","slug":"exploring-retrieval-augmented-generation-in","title":"Exploring Retrieval Augmented Generation in Arabic","date":"2024-08-14","arxiv_id":"2408.07425","n_code_links":1,"syntology":null},{"paper":null,"slug":"g-2-v-2-former-graph-guided-video-vision","title":"G$^2$V$^2$former: Graph Guided Video Vision Transformer for Face Anti-Spoofing","date":"2024-08-14","arxiv_id":"2408.07675","n_code_links":0,"syntology":null},{"paper":"/paper/improved-3d-whole-heart-geometry-from-sparse","slug":"improved-3d-whole-heart-geometry-from-sparse","title":"Improved 3D Whole Heart Geometry from Sparse CMR Slices","date":"2024-08-14","arxiv_id":"2408.07532","n_code_links":1,"syntology":null},{"paper":null,"slug":"kraken-inherently-parallel-transformers-for","title":"Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference","date":"2024-08-14","arxiv_id":"2408.07802","n_code_links":0,"syntology":null},{"paper":"/paper/lipcot-linear-predictive-coding-based","slug":"lipcot-linear-predictive-coding-based","title":"LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language Models","date":"2024-08-14","arxiv_id":"2408.07292","n_code_links":1,"syntology":null},{"paper":"/paper/metaseg-metaformer-based-global-contexts","slug":"metaseg-metaformer-based-global-contexts","title":"MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation","date":"2024-08-14","arxiv_id":"2408.07576","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-periodicity-dependency-transformer","title":"Multi-periodicity dependency Transformer based on spectrum offset for radio frequency fingerprint identification","date":"2024-08-14","arxiv_id":"2408.07592","n_code_links":0,"syntology":null},{"paper":null,"slug":"sage-rt-synthetic-alignment-data-generation","title":"SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming","date":"2024-08-14","arxiv_id":"2408.11851","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformers-and-large-language-models-for-1","title":"Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey","date":"2024-08-14","arxiv_id":"2408.07583","n_code_links":0,"syntology":null},{"paper":null,"slug":"uahoi-uncertainty-aware-robust-interaction","title":"UAHOI: Uncertainty-aware Robust Interaction Learning for HOI Detection","date":"2024-08-14","arxiv_id":"2408.07430","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-perspective-on-large-language-models","title":"A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition","date":"2024-08-13","arxiv_id":"2408.06598","n_code_links":0,"syntology":null},{"paper":null,"slug":"bert-s-conceptual-cartography-mapping-the","title":"BERT's Conceptual Cartography: Mapping the Landscapes of Meaning","date":"2024-08-13","arxiv_id":"2408.07190","n_code_links":0,"syntology":null},{"paper":"/paper/cross-view-geolocalization-and-disaster","slug":"cross-view-geolocalization-and-disaster","title":"Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN","date":"2024-08-13","arxiv_id":"2408.06761","n_code_links":1,"syntology":null},{"paper":null,"slug":"divide-and-conquer-improving-multi-camera-3d","title":"Divide and Conquer: Improving Multi-Camera 3D Perception with 2D Semantic-Depth Priors and Input-Dependent Queries","date":"2024-08-13","arxiv_id":"2408.06901","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-deep-model-based-optoacoustic-image","title":"Efficient Deep Model-Based Optoacoustic Image Reconstruction","date":"2024-08-13","arxiv_id":"2408.07109","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-cultural-adaptability-of-a-large","slug":"evaluating-cultural-adaptability-of-a-large","title":"Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas","date":"2024-08-13","arxiv_id":"2408.06929","n_code_links":1,"syntology":null},{"paper":null,"slug":"flatfusion-delving-into-details-of-sparse","title":"FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Driving","date":"2024-08-13","arxiv_id":"2408.06832","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-ai-for-automatic-topic-labelling","title":"Generative AI for automatic topic labelling","date":"2024-08-13","arxiv_id":"2408.07003","n_code_links":0,"syntology":null},{"paper":null,"slug":"harnessing-earnings-reports-for-stock","title":"Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach","date":"2024-08-13","arxiv_id":"2408.06634","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-language-models-for-emotion-and","title":"Leveraging Language Models for Emotion and Behavior Analysis in Education","date":"2024-08-13","arxiv_id":"2408.06874","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimal-preprocessing-for-joint-detection-and","title":"Optimal Preprocessing for Joint Detection and Classification of Wireless Communication Signals in Congested Spectrum Using Computer Vision Methods","date":"2024-08-13","arxiv_id":"2408.06545","n_code_links":0,"syntology":null},{"paper":null,"slug":"pragmatic-inference-of-scalar-implicature-by","title":"Pragmatic inference of scalar implicature by LLMs","date":"2024-08-13","arxiv_id":"2408.06673","n_code_links":0,"syntology":null},{"paper":"/paper/spectrum-prediction-with-deep-3d-pyramid","slug":"spectrum-prediction-with-deep-3d-pyramid","title":"Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning","date":"2024-08-13","arxiv_id":"2408.06870","n_code_links":1,"syntology":null},{"paper":"/paper/sumotosima-a-framework-and-dataset-for","slug":"sumotosima-a-framework-and-dataset-for","title":"Sumotosima: A Framework and Dataset for Classifying and Summarizing Otoscopic Images","date":"2024-08-13","arxiv_id":"2408.06755","n_code_links":1,"syntology":null},{"paper":null,"slug":"tableguard-securing-structured-unstructured","title":"TableGuard -- Securing Structured & Unstructured Data","date":"2024-08-13","arxiv_id":"2408.07045","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-quantification-in-alzheimer-s","title":"Uncertainty Quantification in Alzheimer's Disease Progression Modeling","date":"2024-08-13","arxiv_id":"2408.14478","n_code_links":0,"syntology":null},{"paper":"/paper/unlocking-efficiency-adaptive-masking-for","slug":"unlocking-efficiency-adaptive-masking-for","title":"Unlocking Efficiency: Adaptive Masking for Gene Transformer Models","date":"2024-08-13","arxiv_id":"2408.07180","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-advanced-llms-to-enhance-smaller-llms","title":"Using Advanced LLMs to Enhance Smaller LLMs: An Interpretable Knowledge Distillation Approach","date":"2024-08-13","arxiv_id":"2408.07238","n_code_links":0,"syntology":null},{"paper":null,"slug":"vulcatch-enhancing-binary-vulnerability","title":"VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction","date":"2024-08-13","arxiv_id":"2408.07181","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-vision-transformers-and-open-set","title":"Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies","date":"2024-08-12","arxiv_id":"2408.06457","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-inference-to-improve-quality-of","title":"Bayesian inference to improve quality of Retrieval Augmented Generation","date":"2024-08-12","arxiv_id":"2408.08901","n_code_links":0,"syntology":null},{"paper":"/paper/benchmarking-tree-species-classification-from","slug":"benchmarking-tree-species-classification-from","title":"Benchmarking tree species classification from proximally-sensed laser scanning data: introducing the FOR-species20K dataset","date":"2024-08-12","arxiv_id":"2408.06507","n_code_links":2,"syntology":null},{"paper":null,"slug":"body-transformer-leveraging-robot-embodiment","title":"Body Transformer: Leveraging Robot Embodiment for Policy Learning","date":"2024-08-12","arxiv_id":"2408.06316","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-lingual-conversational-speech","title":"Cross-Lingual Conversational Speech Summarization with Large Language Models","date":"2024-08-12","arxiv_id":"2408.06484","n_code_links":0,"syntology":null},{"paper":null,"slug":"dpdetr-decoupled-position-detection","title":"DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object Detection","date":"2024-08-12","arxiv_id":"2408.06123","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-3d-transformer-segmentation-model","slug":"enhancing-3d-transformer-segmentation-model","title":"Enhancing 3D Transformer Segmentation Model for Medical Image with Token-level Representation Learning","date":"2024-08-12","arxiv_id":"2408.05889","n_code_links":1,"syntology":null},{"paper":"/paper/generalization-enhancement-strategies-to","slug":"generalization-enhancement-strategies-to","title":"Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples","date":"2024-08-12","arxiv_id":"2408.06467","n_code_links":1,"syntology":null},{"paper":"/paper/hat-history-augmented-anchor-transformer-for","slug":"hat-history-augmented-anchor-transformer-for","title":"HAT: History-Augmented Anchor Transformer for Online Temporal Action Localization","date":"2024-08-12","arxiv_id":"2408.06437","n_code_links":1,"syntology":{"ran":13,"of":14,"n_ran_checked":12,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"13 ran (of which 3 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["sakibreza/eccv24-hat"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":3,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"improving-structural-diversity-of-blackbox","title":"Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting","date":"2024-08-12","arxiv_id":"2408.06186","n_code_links":0,"syntology":null},{"paper":null,"slug":"lolgorithm-integrating-semantic-syntactic-and","title":"LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification","date":"2024-08-12","arxiv_id":"2408.06335","n_code_links":0,"syntology":null},{"paper":null,"slug":"med42-v2-a-suite-of-clinical-llms","title":"Med42-v2: A Suite of Clinical LLMs","date":"2024-08-12","arxiv_id":"2408.06142","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimizing-rag-techniques-for-automotive","title":"Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models","date":"2024-08-12","arxiv_id":"2408.05933","n_code_links":0,"syntology":null},{"paper":"/paper/paformer-part-aware-transformer-for-person-re","slug":"paformer-part-aware-transformer-for-person-re","title":"PAFormer: Part Aware Transformer for Person Re-identification","date":"2024-08-12","arxiv_id":"2408.05918","n_code_links":0,"syntology":null},{"paper":null,"slug":"phago-protein-function-annotation-for","title":"PhaGO: Protein function annotation for bacteriophages by integrating the genomic context","date":"2024-08-12","arxiv_id":"2408.06402","n_code_links":0,"syntology":null},{"paper":"/paper/spacetime-e-n-transformer-equivariant","slug":"spacetime-e-n-transformer-equivariant","title":"Spacetime $E(n)$-Transformer: Equivariant Attention for Spatio-temporal Graphs","date":"2024-08-12","arxiv_id":"2408.06039","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-language-of-trauma-modeling-traumatic","title":"The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI","date":"2024-08-12","arxiv_id":"2408.05977","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-informed-volume-visualization","title":"Uncertainty-Informed Volume Visualization using Implicit Neural Representation","date":"2024-08-12","arxiv_id":"2408.06018","n_code_links":0,"syntology":null},{"paper":null,"slug":"utilize-transformers-for-translating","title":"Utilize Transformers for translating Wikipedia category names","date":"2024-08-12","arxiv_id":"2408.06124","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-4-emulates-average-human-emotional","title":"GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective","date":"2024-08-11","arxiv_id":"2408.13718","n_code_links":0,"syntology":null},{"paper":"/paper/hyspark-hybrid-sparse-masking-for-large-scale","slug":"hyspark-hybrid-sparse-masking-for-large-scale","title":"HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training","date":"2024-08-11","arxiv_id":"2408.05815","n_code_links":1,"syntology":null},{"paper":"/paper/kov-transferable-and-naturalistic-black-box","slug":"kov-transferable-and-naturalistic-black-box","title":"Kov: Transferable and Naturalistic Black-Box LLM Attacks using Markov Decision Processes and Tree Search","date":"2024-08-11","arxiv_id":"2408.08899","n_code_links":1,"syntology":null},{"paper":null,"slug":"sampling-foundational-transformer-a","title":"Sampling Foundational Transformer: A Theoretical Perspective","date":"2024-08-11","arxiv_id":"2408.05822","n_code_links":0,"syntology":null},{"paper":"/paper/u-decn-end-to-end-underwater-object-detection","slug":"u-decn-end-to-end-underwater-object-detection","title":"U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training","date":"2024-08-11","arxiv_id":"2408.05780","n_code_links":1,"syntology":null},{"paper":"/paper/utilizing-large-language-models-to-optimize","slug":"utilizing-large-language-models-to-optimize","title":"PhishLang: A Real-Time, Fully Client-Side Phishing Detection Framework Using MobileBERT","date":"2024-08-11","arxiv_id":"2408.05667","n_code_links":2,"syntology":null},{"paper":null,"slug":"beyondct-a-deep-learning-model-for-predicting","title":"BeyondCT: A deep learning model for predicting pulmonary function from chest CT scans","date":"2024-08-10","arxiv_id":"2408.05645","n_code_links":0,"syntology":null},{"paper":null,"slug":"chain-of-condition-construct-verify-and-solve","title":"Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering","date":"2024-08-10","arxiv_id":"2408.05442","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-whisper-s-recognition-performance","title":"Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text","date":"2024-08-10","arxiv_id":"2408.05554","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-multi-step-scientific-processes-with","title":"Modeling Multi-Step Scientific Processes with Graph Transformer Networks","date":"2024-08-10","arxiv_id":"2408.05425","n_code_links":0,"syntology":null}],"record_sha256":"917c94ab4d9d286d137df7729e20be20e69c2ba01757cb52d470ae53c1900077","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}