{"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/12","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":12,"pages_in_order":275,"rows_per_page":100,"rows":[1101,1200],"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/11","next":"/method/dropout/papers/13","papers":[{"paper":null,"slug":"transformers-can-overcome-the-curse-of","title":"Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective","date":"2025-04-18","arxiv_id":"2504.13558","n_code_links":0,"syntology":null},{"paper":null,"slug":"word-embedding-techniques-for-classification","title":"Word Embedding Techniques for Classification of Star Ratings","date":"2025-04-18","arxiv_id":"2504.13653","n_code_links":0,"syntology":null},{"paper":null,"slug":"accuracy-is-not-agreement-expert-aligned","title":"Accuracy is Not Agreement: Expert-Aligned Evaluation of Crash Narrative Classification Models","date":"2025-04-17","arxiv_id":"2504.13068","n_code_links":0,"syntology":null},{"paper":"/paper/cdf-rag-causal-dynamic-feedback-for-adaptive","slug":"cdf-rag-causal-dynamic-feedback-for-adaptive","title":"CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation","date":"2025-04-17","arxiv_id":"2504.12560","n_code_links":1,"syntology":null},{"paper":"/paper/estimating-optimal-context-length-for-hybrid","slug":"estimating-optimal-context-length-for-hybrid","title":"Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization","date":"2025-04-17","arxiv_id":"2504.12972","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-expert-failures-improves-llm-agent","title":"Exploring Expert Failures Improves LLM Agent Tuning","date":"2025-04-17","arxiv_id":"2504.13145","n_code_links":0,"syntology":null},{"paper":null,"slug":"freshstack-building-realistic-benchmarks-for","title":"FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents","date":"2025-04-17","arxiv_id":"2504.13128","n_code_links":0,"syntology":null},{"paper":null,"slug":"instructrag-leveraging-retrieval-augmented","title":"InstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning","date":"2025-04-17","arxiv_id":"2504.13032","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-augmented-generation-with-3","slug":"retrieval-augmented-generation-with-3","title":"Retrieval-Augmented Generation with Conflicting Evidence","date":"2025-04-17","arxiv_id":"2504.13079","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hannight/ramdocs"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"simplifying-graph-transformers","title":"Plain Transformers Can be Powerful Graph Learners","date":"2025-04-17","arxiv_id":"2504.12588","n_code_links":0,"syntology":null},{"paper":null,"slug":"sstaf-spatial-spectral-temporal-attention","title":"SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classification","date":"2025-04-17","arxiv_id":"2504.13220","n_code_links":0,"syntology":null},{"paper":null,"slug":"validating-llm-generated-relevance-labels-for","title":"Validating LLM-Generated Relevance Labels for Educational Resource Search","date":"2025-04-17","arxiv_id":"2504.12732","n_code_links":0,"syntology":null},{"paper":"/paper/zerosumeval-scaling-llm-evaluation-with-inter","slug":"zerosumeval-scaling-llm-evaluation-with-inter","title":"ZeroSumEval: Scaling LLM Evaluation with Inter-Model Competition","date":"2025-04-17","arxiv_id":"2504.12562","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-visual-rag-pipeline-for-few-shot-fine","title":"A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification","date":"2025-04-16","arxiv_id":"2504.11838","n_code_links":0,"syntology":null},{"paper":null,"slug":"adat-time-series-aware-adaptive-transformer","title":"ADAT: Time-Series-Aware Adaptive Transformer Architecture for Sign Language Translation","date":"2025-04-16","arxiv_id":"2504.11942","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximation-bounds-for-transformer-networks","title":"Approximation Bounds for Transformer Networks with Application to Regression","date":"2025-04-16","arxiv_id":"2504.12175","n_code_links":0,"syntology":null},{"paper":null,"slug":"arcer-an-agentic-rag-for-the-automated","title":"ARCeR: an Agentic RAG for the Automated Definition of Cyber Ranges","date":"2025-04-16","arxiv_id":"2504.12143","n_code_links":0,"syntology":null},{"paper":"/paper/dense-backpropagation-improves-training-for","slug":"dense-backpropagation-improves-training-for","title":"Dense Backpropagation Improves Training for Sparse Mixture-of-Experts","date":"2025-04-16","arxiv_id":"2504.12463","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":4,"n_instrument":0,"unverified":2,"pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["vatsal0/default-moe"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/geometric-generality-of-transformer-based","slug":"geometric-generality-of-transformer-based","title":"Geometric Generality of Transformer-Based Gröbner Basis Computation","date":"2025-04-16","arxiv_id":"2504.12465","n_code_links":1,"syntology":null},{"paper":"/paper/gt-svq-a-linear-time-graph-transformer-for","slug":"gt-svq-a-linear-time-graph-transformer-for","title":"GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization","date":"2025-04-16","arxiv_id":"2504.11840","n_code_links":1,"syntology":null},{"paper":"/paper/human-aligned-compression-for-robust-models","slug":"human-aligned-compression-for-robust-models","title":"Human Aligned Compression for Robust Models","date":"2025-04-16","arxiv_id":"2504.12255","n_code_links":1,"syntology":null},{"paper":null,"slug":"mapping-controversies-using-artificial","title":"Mapping Controversies Using Artificial Intelligence: An Analysis of the Hamas-Israel Conflict on YouTube","date":"2025-04-16","arxiv_id":"2504.12177","n_code_links":0,"syntology":null},{"paper":null,"slug":"mitigating-llm-hallucinations-with-knowledge","title":"Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study","date":"2025-04-16","arxiv_id":"2504.12422","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-feasibility-of-using-multimodal-llms","title":"On the Feasibility of Using MultiModal LLMs to Execute AR Social Engineering Attacks","date":"2025-04-16","arxiv_id":"2504.13209","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-customized-gpt-to-develop-prompting","title":"Using customized GPT to develop prompting proficiency in architectural AI-generated images","date":"2025-04-16","arxiv_id":"2504.13948","n_code_links":0,"syntology":null},{"paper":"/paper/zooming-in-on-fakes-a-novel-dataset-for","slug":"zooming-in-on-fakes-a-novel-dataset-for","title":"Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach","date":"2025-04-16","arxiv_id":"2504.11922","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-decade-of-wheat-mapping-for-lebanon","title":"A Decade of Wheat Mapping for Lebanon","date":"2025-04-15","arxiv_id":"2504.11366","n_code_links":0,"syntology":null},{"paper":"/paper/afire-anatomy-driven-self-supervised-learning","slug":"afire-anatomy-driven-self-supervised-learning","title":"AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images","date":"2025-04-15","arxiv_id":"2504.10972","n_code_links":1,"syntology":null},{"paper":"/paper/an-adaptive-dropout-approach-for-high","slug":"an-adaptive-dropout-approach-for-high","title":"An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization","date":"2025-04-15","arxiv_id":"2504.11353","n_code_links":1,"syntology":null},{"paper":null,"slug":"bridging-distribution-gaps-in-time-series","title":"Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization","date":"2025-04-15","arxiv_id":"2504.10900","n_code_links":0,"syntology":null},{"paper":null,"slug":"csplade-learned-sparse-retrieval-with-causal","title":"CSPLADE: Learned Sparse Retrieval with Causal Language Models","date":"2025-04-15","arxiv_id":"2504.10816","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-based-bathymetry-retrieval","slug":"deep-learning-based-bathymetry-retrieval","title":"Deep Learning-based Bathymetry Retrieval without In-situ Depths using Remote Sensing Imagery and SfM-MVS DSMs with Data Gaps","date":"2025-04-15","arxiv_id":"2504.11416","n_code_links":1,"syntology":null},{"paper":null,"slug":"discovering-phoneme-specific-critical","title":"Discovering phoneme-specific critical articulators through a data-driven approach","date":"2025-04-15","arxiv_id":"2505.00007","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-distributed-retrieval-augmented","title":"Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance","date":"2025-04-15","arxiv_id":"2504.11197","n_code_links":0,"syntology":null},{"paper":null,"slug":"embedding-radiomics-into-vision-transformers","title":"Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification","date":"2025-04-15","arxiv_id":"2504.10916","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-the-role-of-kg-based-rag-in","title":"Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs","date":"2025-04-15","arxiv_id":"2504.10982","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-powerformer-a-memory-efficient","title":"Fast-Powerformer: A Memory-Efficient Transformer for Accurate Mid-Term Wind Power Forecasting","date":"2025-04-15","arxiv_id":"2504.10923","n_code_links":0,"syntology":null},{"paper":null,"slug":"hallucination-aware-generative-pretrained","title":"Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control","date":"2025-04-15","arxiv_id":"2504.10831","n_code_links":0,"syntology":null},{"paper":null,"slug":"intraoperative-perfusion-assessment-by","title":"Intraoperative perfusion assessment by continuous, low-latency hyperspectral light-field imaging: development, methodology, and clinical application","date":"2025-04-15","arxiv_id":"2504.10953","n_code_links":0,"syntology":null},{"paper":null,"slug":"layoutcot-unleashing-the-deep-reasoning","title":"LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation","date":"2025-04-15","arxiv_id":"2504.10829","n_code_links":0,"syntology":null},{"paper":null,"slug":"leveraging-point-transformers-for-detecting","title":"Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry","date":"2025-04-15","arxiv_id":"2504.11418","n_code_links":0,"syntology":null},{"paper":null,"slug":"moving-beyond-next-token-prediction","title":"Moving Beyond Next-Token Prediction: Transformers are Context-Sensitive Language Generators","date":"2025-04-15","arxiv_id":"2504.10845","n_code_links":0,"syntology":null},{"paper":"/paper/multi-scale-convolutional-transformer-network","slug":"multi-scale-convolutional-transformer-network","title":"Multi-scale convolutional transformer network for motor imagery brain-computer interface","date":"2025-04-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"progressive-rock-music-classification","title":"Progressive Rock Music Classification","date":"2025-04-15","arxiv_id":"2504.10821","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-universal-graph-structural-encoder","title":"Towards A Universal Graph Structural Encoder","date":"2025-04-15","arxiv_id":"2504.10917","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-automated-safety-requirements","title":"Towards Automated Safety Requirements Derivation Using Agent-based RAG","date":"2025-04-15","arxiv_id":"2504.11243","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-model-for-cold-start","title":"Transformer-Based Model for Cold Start Mitigation in FaaS Architecture","date":"2025-04-15","arxiv_id":"2504.11338","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-estimation-for-trust-attribution","title":"Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networks","date":"2025-04-15","arxiv_id":"2504.11307","n_code_links":0,"syntology":null},{"paper":null,"slug":"vexp-a-low-cost-risc-v-isa-extension-for","title":"VEXP: A Low-Cost RISC-V ISA Extension for Accelerated Softmax Computation in Transformers","date":"2025-04-15","arxiv_id":"2504.11227","n_code_links":0,"syntology":null},{"paper":"/paper/a-survey-of-personalization-from-rag-to-agent","slug":"a-survey-of-personalization-from-rag-to-agent","title":"A Survey of Personalization: From RAG to Agent","date":"2025-04-14","arxiv_id":"2504.10147","n_code_links":1,"syntology":null},{"paper":null,"slug":"beyond-chains-of-thought-benchmarking-latent","title":"Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models","date":"2025-04-14","arxiv_id":"2504.10615","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-llms-handle-webshell-detection-overcoming","title":"Can LLMs handle WebShell detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework","date":"2025-04-14","arxiv_id":"2504.13811","n_code_links":0,"syntology":null},{"paper":null,"slug":"dior-adaptive-cognitive-detection-and","title":"DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation","date":"2025-04-14","arxiv_id":"2504.10198","n_code_links":0,"syntology":null},{"paper":null,"slug":"emafusion-a-self-optimizing-system-for","title":"EMAFusion: A Self-Optimizing System for Seamless LLM Selection and Integration","date":"2025-04-14","arxiv_id":"2504.10681","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-and-local-mamba-network-for-multi","title":"Global and Local Mamba Network for Multi-Modality Medical Image Super-Resolution","date":"2025-04-14","arxiv_id":"2504.10105","n_code_links":0,"syntology":null},{"paper":null,"slug":"hallucination-detection-in-llms-via","title":"Hallucination Detection in LLMs via Topological Divergence on Attention Graphs","date":"2025-04-14","arxiv_id":"2504.10063","n_code_links":0,"syntology":null},{"paper":null,"slug":"hdc-hierarchical-distillation-for-multi-level","title":"HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation","date":"2025-04-14","arxiv_id":"2504.09876","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrating-vision-and-location-with","title":"Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis","date":"2025-04-14","arxiv_id":"2504.10452","n_code_links":0,"syntology":null},{"paper":null,"slug":"keyword-extraction-and-aspect-classification","title":"Keyword Extraction, and Aspect Classification in Sinhala, English, and Code-Mixed Content","date":"2025-04-14","arxiv_id":"2504.10679","n_code_links":0,"syntology":null},{"paper":null,"slug":"mmkb-rag-a-multi-modal-knowledge-based","title":"MMKB-RAG: A Multi-Modal Knowledge-Based Retrieval-Augmented Generation Framework","date":"2025-04-14","arxiv_id":"2504.10074","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-object-grounding-via-hierarchical","title":"Multi-Object Grounding via Hierarchical Contrastive Siamese Transformers","date":"2025-04-14","arxiv_id":"2504.10048","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-long-video-modeling-based-on","slug":"multimodal-long-video-modeling-based-on","title":"Multimodal Long Video Modeling Based on Temporal Dynamic Context","date":"2025-04-14","arxiv_id":"2504.10443","n_code_links":1,"syntology":null},{"paper":null,"slug":"paging-dr-gpt-extracting-information-from","title":"Paging Dr. GPT: Extracting Information from Clinical Notes to Enhance Patient Predictions","date":"2025-04-14","arxiv_id":"2504.12338","n_code_links":0,"syntology":null},{"paper":"/paper/rakg-document-level-retrieval-augmented","slug":"rakg-document-level-retrieval-augmented","title":"RAKG:Document-level Retrieval Augmented Knowledge Graph Construction","date":"2025-04-14","arxiv_id":"2504.09823","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-controlled-dynamic-expansion-model-for","title":"Self-Controlled Dynamic Expansion Model for Continual Learning","date":"2025-04-14","arxiv_id":"2504.10561","n_code_links":0,"syntology":null},{"paper":"/paper/toward-aligning-human-and-robot-actions-via","slug":"toward-aligning-human-and-robot-actions-via","title":"Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning","date":"2025-04-14","arxiv_id":"2504.11493","n_code_links":1,"syntology":null},{"paper":null,"slug":"understanding-and-optimizing-multi-stage-ai","title":"Understanding and Optimizing Multi-Stage AI Inference Pipelines","date":"2025-04-14","arxiv_id":"2504.09775","n_code_links":0,"syntology":null},{"paper":null,"slug":"vdocrag-retrieval-augmented-generation-over","title":"VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents","date":"2025-04-14","arxiv_id":"2504.09795","n_code_links":0,"syntology":null},{"paper":"/paper/xy-cut-advanced-layout-ordering-via","slug":"xy-cut-advanced-layout-ordering-via","title":"XY-Cut++: Advanced Layout Ordering via Hierarchical Mask Mechanism on a Novel Benchmark","date":"2025-04-14","arxiv_id":"2504.10258","n_code_links":1,"syntology":null},{"paper":"/paper/clinicalgpt-r1-pushing-reasoning-capability","slug":"clinicalgpt-r1-pushing-reasoning-capability","title":"ClinicalGPT-R1: Pushing reasoning capability of generalist disease diagnosis with large language model","date":"2025-04-13","arxiv_id":"2504.09421","n_code_links":1,"syntology":null},{"paper":null,"slug":"controlnet-a-firewall-for-rag-based-llm","title":"ControlNET: A Firewall for RAG-based LLM System","date":"2025-04-13","arxiv_id":"2504.09593","n_code_links":0,"syntology":null},{"paper":null,"slug":"ditse-high-fidelity-generative-speech","title":"DiTSE: High-Fidelity Generative Speech Enhancement via Latent Diffusion Transformers","date":"2025-04-13","arxiv_id":"2504.09381","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhanced-filterless-multi-color-vlc-via-qct","title":"Enhanced Filterless Multi-Color VLC via QCT","date":"2025-04-13","arxiv_id":"2504.09743","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-enhanced-graph-autoencoder-with-gat","title":"Ensemble-Enhanced Graph Autoencoder with GAT and Transformer-Based Encoders for Robust Fault Diagnosis","date":"2025-04-13","arxiv_id":"2504.09427","n_code_links":0,"syntology":null},{"paper":null,"slug":"hd-rag-retrieval-augmented-generation-for","title":"HD-RAG: Retrieval-Augmented Generation for Hybrid Documents Containing Text and Hierarchical Tables","date":"2025-04-13","arxiv_id":"2504.09554","n_code_links":0,"syntology":null},{"paper":"/paper/hm-rag-hierarchical-multi-agent-multimodal","slug":"hm-rag-hierarchical-multi-agent-multimodal","title":"HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation","date":"2025-04-13","arxiv_id":"2504.12330","n_code_links":1,"syntology":null},{"paper":null,"slug":"integrating-large-language-models-for-1","title":"Integrating Large Language Models for Automated Structural Analysis","date":"2025-04-13","arxiv_id":"2504.09754","n_code_links":0,"syntology":null},{"paper":null,"slug":"iterative-self-training-for-code-generation","title":"Iterative Self-Training for Code Generation via Reinforced Re-Ranking","date":"2025-04-13","arxiv_id":"2504.09643","n_code_links":0,"syntology":null},{"paper":"/paper/trajectory-guided-motion-perception-for","slug":"trajectory-guided-motion-perception-for","title":"Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological Disorders","date":"2025-04-13","arxiv_id":"2504.09530","n_code_links":1,"syntology":null},{"paper":null,"slug":"accurate-diagnosis-of-respiratory-viruses","title":"Accurate Diagnosis of Respiratory Viruses Using an Explainable Machine Learning with Mid-Infrared Biomolecular Fingerprinting of Nasopharyngeal Secretions","date":"2025-04-12","arxiv_id":"2504.09211","n_code_links":0,"syntology":null},{"paper":null,"slug":"amnet-an-acoustic-model-network-for-enhanced","title":"AMNet: An Acoustic Model Network for Enhanced Mandarin Speech Synthesis","date":"2025-04-12","arxiv_id":"2504.09225","n_code_links":0,"syntology":null},{"paper":null,"slug":"heterag-a-heterogeneous-retrieval-augmented","title":"HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations","date":"2025-04-12","arxiv_id":"2504.10529","n_code_links":0,"syntology":null},{"paper":"/paper/learning-occlusion-robust-vision-transformers-1","slug":"learning-occlusion-robust-vision-transformers-1","title":"Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking","date":"2025-04-12","arxiv_id":"2504.09228","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","official":{"repos":["wuyou3474/ortrack"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"lumos-efficient-performance-modeling-and","title":"Lumos: Efficient Performance Modeling and Estimation for Large-scale LLM Training","date":"2025-04-12","arxiv_id":"2504.09307","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-brain-tumor-segmentation-via-3d","title":"Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention","date":"2025-04-12","arxiv_id":"2504.09088","n_code_links":0,"syntology":null},{"paper":"/paper/multi-scale-activation-refinement-and-1","slug":"multi-scale-activation-refinement-and-1","title":"Multi-scale Activation, Refinement, and Aggregation: Exploring Diverse Cues for Fine-Grained Bird Recognition","date":"2025-04-12","arxiv_id":"2504.09215","n_code_links":0,"syntology":null},{"paper":"/paper/nettag-a-multimodal-rtl-and-layout-aligned","slug":"nettag-a-multimodal-rtl-and-layout-aligned","title":"NetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graph","date":"2025-04-12","arxiv_id":"2504.09260","n_code_links":1,"syntology":null},{"paper":"/paper/pneuma-leveraging-llms-for-tabular-data","slug":"pneuma-leveraging-llms-for-tabular-data","title":"Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System","date":"2025-04-12","arxiv_id":"2504.09207","n_code_links":1,"syntology":null},{"paper":null,"slug":"semantic-commit-helping-users-update-intent","title":"Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale","date":"2025-04-12","arxiv_id":"2504.09283","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-additive-parameter-updates-of-vision","title":"Adaptive Additive Parameter Updates of Vision Transformers for Few-Shot Continual Learning","date":"2025-04-11","arxiv_id":"2504.08982","n_code_links":0,"syntology":null},{"paper":null,"slug":"adopting-large-language-models-to-automated","title":"Adopting Large Language Models to Automated System Integration","date":"2025-04-11","arxiv_id":"2504.08490","n_code_links":0,"syntology":null},{"paper":null,"slug":"dreamfuse-adaptive-image-fusion-with","title":"DreamFuse: Adaptive Image Fusion with Diffusion Transformer","date":"2025-04-11","arxiv_id":"2504.08291","n_code_links":0,"syntology":null},{"paper":null,"slug":"drivaer-transformer-a-high-precision-and-fast","title":"DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset","date":"2025-04-11","arxiv_id":"2504.08217","n_code_links":0,"syntology":null},{"paper":null,"slug":"examining-gpt-s-capability-to-generate-and","title":"Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship","date":"2025-04-11","arxiv_id":"2504.08856","n_code_links":0,"syntology":null},{"paper":"/paper/hypercore-the-core-framework-for-building","slug":"hypercore-the-core-framework-for-building","title":"HyperCore: The Core Framework for Building Hyperbolic Foundation Models with Comprehensive Modules","date":"2025-04-11","arxiv_id":"2504.08912","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":["graph-and-geometric-learning/hypercore"],"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":"hypergraph-vision-transformers-images-are","title":"Hypergraph Vision Transformers: Images are More than Nodes, More than Edges","date":"2025-04-11","arxiv_id":"2504.08710","n_code_links":0,"syntology":null},{"paper":null,"slug":"integrated-ensemble-of-bert-and-features","title":"Integrated ensemble of BERT- and features-based models for authorship attribution in Japanese literary works","date":"2025-04-11","arxiv_id":"2504.08527","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-from-elders-making-an-llm-powered","title":"Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design","date":"2025-04-11","arxiv_id":"2504.08985","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-for-comparative-narrative-analysis","title":"LLM for Comparative Narrative Analysis","date":"2025-04-11","arxiv_id":"2504.08211","n_code_links":0,"syntology":null},{"paper":null,"slug":"llmtaxo-leveraging-large-language-models-for","title":"LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media","date":"2025-04-11","arxiv_id":"2504.12325","n_code_links":0,"syntology":null}],"record_sha256":"c3037d49e25c11b07a946785ed9d2226aeaababe92ffee9ac6b62a5726e4d4c0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}