{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/softmax/papers/80","list_of":"/method/softmax","method":"Softmax","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":80,"pages_in_order":375,"rows_per_page":100,"rows":[7901,8000],"of":37443,"counts":{"archive_papers_tagged":37443,"with_a_code_link":15869,"where_syntology_ran_a_sample":4578,"not_listed_spam_title":0,"listed":37443,"listed_where_code_ran":4578,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3835,"every_run_a_failure_of_syntologys_instrument":743,"listed_with_a_run_with_no_instrument_failure":3835,"listed_every_run_a_failure_of_syntologys_instrument":743,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/softmax","prev":"/method/softmax/papers/79","next":"/method/softmax/papers/81","papers":[{"paper":"/paper/squeezed-attention-accelerating-long-context","slug":"squeezed-attention-accelerating-long-context","title":"Squeezed Attention: Accelerating Long Context Length LLM Inference","date":"2024-11-14","arxiv_id":"2411.09688","n_code_links":1,"syntology":{"ran":12,"of":15,"n_ran_checked":9,"n_instrument":3,"unverified":3,"pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":{"repos":["SqueezeAILab/SqueezedAttention"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"stability-and-generalization-for-distributed","title":"Stability and Generalization for Distributed SGDA","date":"2024-11-14","arxiv_id":"2411.09365","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-future-of-skill-what-is-it-to-be-skilled","title":"The Future of Skill: What Is It to Be Skilled at Work?","date":"2024-11-14","arxiv_id":"2411.10488","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-classification-of-open-source-ml","title":"Towards a Classification of Open-Source ML Models and Datasets for Software Engineering","date":"2024-11-14","arxiv_id":"2411.09683","n_code_links":0,"syntology":null},{"paper":"/paper/a-large-scale-study-of-relevance-assessments","slug":"a-large-scale-study-of-relevance-assessments","title":"A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Look","date":"2024-11-13","arxiv_id":"2411.08275","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-transformer-based-visual-piano","title":"A Transformer-Based Visual Piano Transcription Algorithm","date":"2024-11-13","arxiv_id":"2411.09037","n_code_links":0,"syntology":null},{"paper":null,"slug":"ad-dino-attention-dynamic-dino-for-distance","title":"AD-DINO: Attention-Dynamic DINO for Distance-Aware Embodied Reference Understanding","date":"2024-11-13","arxiv_id":"2411.08451","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-nonlinear-scma-codebook-design-based","title":"Advanced Nonlinear SCMA Codebook Design Based on Lattice Constellations","date":"2024-11-13","arxiv_id":"2411.08493","n_code_links":0,"syntology":null},{"paper":null,"slug":"analyst-reports-and-stock-performance","title":"Analyst Reports and Stock Performance: Evidence from the Chinese Market","date":"2024-11-13","arxiv_id":"2411.08726","n_code_links":0,"syntology":null},{"paper":null,"slug":"camembert-2-0-a-smarter-french-language-model","title":"CamemBERT 2.0: A Smarter French Language Model Aged to Perfection","date":"2024-11-13","arxiv_id":"2411.08868","n_code_links":0,"syntology":null},{"paper":null,"slug":"continuous-gnn-based-anomaly-detection-on","title":"Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning","date":"2024-11-13","arxiv_id":"2411.09072","n_code_links":0,"syntology":null},{"paper":"/paper/cut-your-losses-in-large-vocabulary-language","slug":"cut-your-losses-in-large-vocabulary-language","title":"Cut Your Losses in Large-Vocabulary Language Models","date":"2024-11-13","arxiv_id":"2411.09009","n_code_links":2,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["apple/ml-cross-entropy"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/finrobot-ai-agent-for-equity-research-and","slug":"finrobot-ai-agent-for-equity-research-and","title":"FinRobot: AI Agent for Equity Research and Valuation with Large Language Models","date":"2024-11-13","arxiv_id":"2411.08804","n_code_links":1,"syntology":{"ran":10,"of":11,"n_ran_checked":10,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ai4finance-foundation/finrobot"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"flow-reconstruction-in-time-varying","title":"Flow reconstruction in time-varying geometries using graph neural networks","date":"2024-11-13","arxiv_id":"2411.08764","n_code_links":0,"syntology":null},{"paper":"/paper/fluoroformer-scaling-multiple-instance","slug":"fluoroformer-scaling-multiple-instance","title":"Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion","date":"2024-11-13","arxiv_id":"2411.08975","n_code_links":1,"syntology":null},{"paper":null,"slug":"llmstinger-jailbreaking-llms-using-rl-fine","title":"LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs","date":"2024-11-13","arxiv_id":"2411.08862","n_code_links":0,"syntology":null},{"paper":"/paper/logllm-log-based-anomaly-detection-using","slug":"logllm-log-based-anomaly-detection-using","title":"LogLLM: Log-based Anomaly Detection Using Large Language Models","date":"2024-11-13","arxiv_id":"2411.08561","n_code_links":1,"syntology":null},{"paper":null,"slug":"multimodal-object-detection-using-depth-and","title":"Multimodal Object Detection using Depth and Image Data for Manufacturing Parts","date":"2024-11-13","arxiv_id":"2411.09062","n_code_links":0,"syntology":null},{"paper":null,"slug":"oblique-bayesian-additive-regression-trees","title":"Oblique Bayesian additive regression trees","date":"2024-11-13","arxiv_id":"2411.08849","n_code_links":0,"syntology":null},{"paper":null,"slug":"perceivers-a-multi-scale-perceiver-with","title":"PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for Long-Term Expressive Symbolic Music Generation","date":"2024-11-13","arxiv_id":"2411.08307","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantity-versus-diversity-influence-of-data","title":"Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models","date":"2024-11-13","arxiv_id":"2411.17709","n_code_links":0,"syntology":null},{"paper":null,"slug":"remp-reusable-motion-prior-for-multi-domain","title":"ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose Estimation and Motion Inbetweening","date":"2024-11-13","arxiv_id":"2411.09435","n_code_links":0,"syntology":null},{"paper":"/paper/resolve-relational-reasoning-with-symbolic","slug":"resolve-relational-reasoning-with-symbolic","title":"RESOLVE: Relational Reasoning with Symbolic and Object-Level Features Using Vector Symbolic Processing","date":"2024-11-13","arxiv_id":"2411.08290","n_code_links":1,"syntology":null},{"paper":null,"slug":"responsible-ai-in-construction-safety","title":"Responsible AI in Construction Safety: Systematic Evaluation of Large Language Models and Prompt Engineering","date":"2024-11-13","arxiv_id":"2411.08320","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-recipe-generation","title":"Retrieval Augmented Recipe Generation","date":"2024-11-13","arxiv_id":"2411.08715","n_code_links":0,"syntology":null},{"paper":null,"slug":"sad-time-a-spatiotemporal-fused-network-for","title":"SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor","date":"2024-11-13","arxiv_id":"2411.08521","n_code_links":0,"syntology":null},{"paper":null,"slug":"sam-i2i-unleash-the-power-of-segment-anything","title":"SAM-I2I: Unleash the Power of Segment Anything Model for Medical Image Translation","date":"2024-11-13","arxiv_id":"2411.12755","n_code_links":0,"syntology":null},{"paper":null,"slug":"sase-a-searching-architecture-for-squeeze-and","title":"SASE: A Searching Architecture for Squeeze and Excitation Operations","date":"2024-11-13","arxiv_id":"2411.08333","n_code_links":0,"syntology":null},{"paper":null,"slug":"scale-contrastive-learning-with-selective","title":"Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment","date":"2024-11-13","arxiv_id":"2411.09007","n_code_links":0,"syntology":null},{"paper":"/paper/scalenet-scale-invariance-learning-in","slug":"scalenet-scale-invariance-learning-in","title":"ScaleNet: Scale Invariance Learning in Directed Graphs","date":"2024-11-13","arxiv_id":"2411.08758","n_code_links":1,"syntology":null},{"paper":"/paper/towards-objective-and-unbiased-decision","slug":"towards-objective-and-unbiased-decision","title":"Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks","date":"2024-11-13","arxiv_id":"2411.08504","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-optimizing-a-retrieval-augmented","title":"Towards Optimizing a Retrieval Augmented Generation using Large Language Model on Academic Data","date":"2024-11-13","arxiv_id":"2411.08438","n_code_links":0,"syntology":null},{"paper":"/paper/trace-transformer-based-risk-assessment-for","slug":"trace-transformer-based-risk-assessment-for","title":"TRACE: Transformer-based Risk Assessment for Clinical Evaluation","date":"2024-11-13","arxiv_id":"2411.08701","n_code_links":1,"syntology":null},{"paper":null,"slug":"uiformer-a-unified-transformer-based","title":"UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation","date":"2024-11-13","arxiv_id":"2411.08569","n_code_links":0,"syntology":null},{"paper":null,"slug":"valtest-automated-validation-of-language","title":"VALTEST: Automated Validation of Language Model Generated Test Cases","date":"2024-11-13","arxiv_id":"2411.08254","n_code_links":0,"syntology":null},{"paper":"/paper/xiyan-sql-a-multi-generator-ensemble","slug":"xiyan-sql-a-multi-generator-ensemble","title":"A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL","date":"2024-11-13","arxiv_id":"2411.08599","n_code_links":5,"syntology":null},{"paper":"/paper/breaking-the-low-rank-dilemma-of-linear","slug":"breaking-the-low-rank-dilemma-of-linear","title":"Breaking the Low-Rank Dilemma of Linear Attention","date":"2024-11-12","arxiv_id":"2411.07635","n_code_links":1,"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":["qhfan/rala"],"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":null,"slug":"budgetmlagent-a-cost-effective-llm-multi","title":"BudgetMLAgent: A Cost-Effective LLM Multi-Agent system for Automating Machine Learning Tasks","date":"2024-11-12","arxiv_id":"2411.07464","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-adversarial-attacks-by-large-language","title":"Can adversarial attacks by large language models be attributed?","date":"2024-11-12","arxiv_id":"2411.08003","n_code_links":0,"syntology":null},{"paper":null,"slug":"circuit-complexity-bounds-for-rope-based","title":"Circuit Complexity Bounds for RoPE-based Transformer Architecture","date":"2024-11-12","arxiv_id":"2411.07602","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-language-prompting-to-ease-false","slug":"contrastive-language-prompting-to-ease-false","title":"Contrastive Language Prompting to Ease False Positives in Medical Anomaly Detection","date":"2024-11-12","arxiv_id":"2411.07546","n_code_links":1,"syntology":null},{"paper":"/paper/controlled-evaluation-of-syntactic-knowledge","slug":"controlled-evaluation-of-syntactic-knowledge","title":"Controlled Evaluation of Syntactic Knowledge in Multilingual Language Models","date":"2024-11-12","arxiv_id":"2411.07474","n_code_links":1,"syntology":null},{"paper":"/paper/deceiving-question-answering-models-a-hybrid","slug":"deceiving-question-answering-models-a-hybrid","title":"Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial Approach","date":"2024-11-12","arxiv_id":"2411.08248","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-2-0-artificial-neurons-that","title":"Deep Learning 2.0: Artificial Neurons That Matter -- Reject Correlation, Embrace Orthogonality","date":"2024-11-12","arxiv_id":"2411.08085","n_code_links":0,"syntology":null},{"paper":null,"slug":"depthwise-separable-convolutions-with-deep","title":"Depthwise Separable Convolutions with Deep Residual Convolutions","date":"2024-11-12","arxiv_id":"2411.07544","n_code_links":0,"syntology":null},{"paper":null,"slug":"dino-lg-a-task-specific-dino-model-for","title":"DINO-LG: A Task-Specific DINO Model for Coronary Calcium Scoring","date":"2024-11-12","arxiv_id":"2411.07976","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-federated-finetuning-of-tiny","title":"Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices","date":"2024-11-12","arxiv_id":"2411.07826","n_code_links":0,"syntology":null},{"paper":null,"slug":"emotion-classification-of-children","title":"Emotion Classification of Children Expressions","date":"2024-11-12","arxiv_id":"2411.07708","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-link-prediction-with-fuzzy-graph","title":"Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling","date":"2024-11-12","arxiv_id":"2411.07482","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-chatgpt-3-5-efficiency-in-solving","slug":"evaluating-chatgpt-3-5-efficiency-in-solving","title":"Evaluating ChatGPT-3.5 Efficiency in Solving Coding Problems of Different Complexity Levels: An Empirical Analysis","date":"2024-11-12","arxiv_id":"2411.07529","n_code_links":1,"syntology":null},{"paper":"/paper/fair-summarization-bridging-quality-and","slug":"fair-summarization-bridging-quality-and","title":"Fair Summarization: Bridging Quality and Diversity in Extractive Summaries","date":"2024-11-12","arxiv_id":"2411.07521","n_code_links":1,"syntology":{"ran":4,"of":5,"n_ran_checked":1,"n_instrument":3,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["PortNLP/FairEXTSummarizer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/fast-disentangled-slim-tensor-learning-for","slug":"fast-disentangled-slim-tensor-learning-for","title":"Fast Disentangled Slim Tensor Learning for Multi-view Clustering","date":"2024-11-12","arxiv_id":"2411.07685","n_code_links":1,"syntology":null},{"paper":"/paper/fm-ts-flow-matching-for-time-series","slug":"fm-ts-flow-matching-for-time-series","title":"FM-TS: Flow Matching for Time Series Generation","date":"2024-11-12","arxiv_id":"2411.07506","n_code_links":1,"syntology":{"ran":20,"of":20,"n_ran_checked":16,"n_instrument":4,"unverified":0,"pointer_only":20,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 2 honoured, 3 violated, 11 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["unites-lab/fmts"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/hmil-hierarchical-multi-instance-learning-for","slug":"hmil-hierarchical-multi-instance-learning-for","title":"HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification","date":"2024-11-12","arxiv_id":"2411.07660","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-grapheme-to-phoneme-conversion","title":"Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models","date":"2024-11-12","arxiv_id":"2411.07563","n_code_links":0,"syntology":null},{"paper":"/paper/interaction-asymmetry-a-general-principle-for","slug":"interaction-asymmetry-a-general-principle-for","title":"Interaction Asymmetry: A General Principle for Learning Composable Abstractions","date":"2024-11-12","arxiv_id":"2411.07784","n_code_links":1,"syntology":{"ran":8,"of":10,"n_ran_checked":8,"n_instrument":0,"unverified":2,"pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jackbrady/interaction-asymmetry"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/joint-multi-dimensional-dynamic-attention-and","slug":"joint-multi-dimensional-dynamic-attention-and","title":"Joint multi-dimensional dynamic attention and transformer for general image restoration","date":"2024-11-12","arxiv_id":"2411.07893","n_code_links":1,"syntology":null},{"paper":"/paper/large-language-models-can-self-improve-in","slug":"large-language-models-can-self-improve-in","title":"Large Language Models Can Self-Improve in Long-context Reasoning","date":"2024-11-12","arxiv_id":"2411.08147","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":6,"phrase":"6 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; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["sihengli99/sealong"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"leveraging-multimodal-models-for-enhanced","title":"Leveraging Multimodal Models for Enhanced Neuroimaging Diagnostics in Alzheimer's Disease","date":"2024-11-12","arxiv_id":"2411.07871","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-app-squatting-and-cloning","title":"LLM App Squatting and Cloning","date":"2024-11-12","arxiv_id":"2411.07518","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-feature-enhancement-network-for-no","title":"Multi-task Feature Enhancement Network for No-Reference Image Quality Assessment","date":"2024-11-12","arxiv_id":"2411.07556","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-clinical-reasoning-through","title":"Multimodal Clinical Reasoning through Knowledge-augmented Rationale Generation","date":"2024-11-12","arxiv_id":"2411.07611","n_code_links":0,"syntology":null},{"paper":null,"slug":"new-emerged-security-and-privacy-of-pre","title":"New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook","date":"2024-11-12","arxiv_id":"2411.07691","n_code_links":0,"syntology":null},{"paper":null,"slug":"query-optimization-for-parametric-knowledge","title":"Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models","date":"2024-11-12","arxiv_id":"2411.07820","n_code_links":0,"syntology":null},{"paper":null,"slug":"rendering-oriented-3d-point-cloud-attribute","title":"Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer","date":"2024-11-12","arxiv_id":"2411.07899","n_code_links":0,"syntology":null},{"paper":"/paper/retrieval-augmented-time-series-forecasting","slug":"retrieval-augmented-time-series-forecasting","title":"Retrieval Augmented Time Series Forecasting","date":"2024-11-12","arxiv_id":"2411.08249","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 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["kutaytire/retrieval-augmented-time-series-forecasting"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"spatially-regularized-graph-attention","title":"Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes","date":"2024-11-12","arxiv_id":"2411.07753","n_code_links":0,"syntology":null},{"paper":null,"slug":"trustful-llms-customizing-and-grounding-text","title":"Trustful LLMs: Customizing and Grounding Text Generation with Knowledge Bases and Dual Decoders","date":"2024-11-12","arxiv_id":"2411.07870","n_code_links":0,"syntology":null},{"paper":null,"slug":"two-layer-attention-optimization-for-bimanual","title":"Two-Layer Attention Optimization for Bimanual Coordination","date":"2024-11-12","arxiv_id":"2411.07470","n_code_links":0,"syntology":null},{"paper":null,"slug":"unraveling-the-gradient-descent-dynamics-of","title":"Unraveling the Gradient Descent Dynamics of Transformers","date":"2024-11-12","arxiv_id":"2411.07538","n_code_links":0,"syntology":null},{"paper":"/paper/verbosity-neq-veracity-demystify-verbosity","slug":"verbosity-neq-veracity-demystify-verbosity","title":"Verbosity $\\neq$ Veracity: Demystify Verbosity Compensation Behavior of Large Language Models","date":"2024-11-12","arxiv_id":"2411.07858","n_code_links":1,"syntology":null},{"paper":null,"slug":"world-models-the-safety-perspective","title":"World Models: The Safety Perspective","date":"2024-11-12","arxiv_id":"2411.07690","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-unified-multi-task-learning-architecture","title":"A Unified Multi-Task Learning Architecture for Hate Detection Leveraging User-Based Information","date":"2024-11-11","arxiv_id":"2411.06855","n_code_links":0,"syntology":null},{"paper":"/paper/add-it-training-free-object-insertion-in","slug":"add-it-training-free-object-insertion-in","title":"Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models","date":"2024-11-11","arxiv_id":"2411.07232","n_code_links":1,"syntology":null},{"paper":"/paper/aeromamba-an-efficient-architecture-for-audio","slug":"aeromamba-an-efficient-architecture-for-audio","title":"AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space models","date":"2024-11-11","arxiv_id":"2411.07364","n_code_links":1,"syntology":null},{"paper":null,"slug":"ambient-ai-scribing-support-comparing-the","title":"Ambient AI Scribing Support: Comparing the Performance of Specialized AI Agentic Architecture to Leading Foundational Models","date":"2024-11-11","arxiv_id":"2411.06713","n_code_links":0,"syntology":null},{"paper":"/paper/an-efficient-memory-module-for-graph-few-shot","slug":"an-efficient-memory-module-for-graph-few-shot","title":"An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning","date":"2024-11-11","arxiv_id":"2411.06659","n_code_links":1,"syntology":null},{"paper":"/paper/assistrag-boosting-the-potential-of-large","slug":"assistrag-boosting-the-potential-of-large","title":"AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant","date":"2024-11-11","arxiv_id":"2411.06805","n_code_links":1,"syntology":{"ran":10,"of":13,"n_ran_checked":8,"n_instrument":2,"unverified":3,"pointer_only":13,"phrase":"10 ran (of which 1 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["smallporridge/assistrag"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":1,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/autonomous-droplet-microfluidic-design","slug":"autonomous-droplet-microfluidic-design","title":"Autonomous Droplet Microfluidic Design Framework with Large Language Models","date":"2024-11-11","arxiv_id":"2411.06691","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-kan-work-exploring-the-potential-of","title":"Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision","date":"2024-11-11","arxiv_id":"2411.06727","n_code_links":0,"syntology":null},{"paper":null,"slug":"cancer-answer-empowering-cancer-care-with","title":"Cancer-Answer: Empowering Cancer Care with Advanced Large Language Models","date":"2024-11-11","arxiv_id":"2411.06946","n_code_links":0,"syntology":null},{"paper":"/paper/convmixformer-a-resource-efficient","slug":"convmixformer-a-resource-efficient","title":"ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognition","date":"2024-11-11","arxiv_id":"2411.07118","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-driven-analysis-of-ai-in-medical-device","title":"Data-Driven Analysis of AI in Medical Device Software in China: Deep Learning and General AI Trends Based on Regulatory Data","date":"2024-11-11","arxiv_id":"2411.07378","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-large-language-models-on-financial","title":"Evaluating Large Language Models on Financial Report Summarization: An Empirical Study","date":"2024-11-11","arxiv_id":"2411.06852","n_code_links":0,"syntology":null},{"paper":null,"slug":"explore-the-reasoning-capability-of-llms-in","title":"Explore the Reasoning Capability of LLMs in the Chess Testbed","date":"2024-11-11","arxiv_id":"2411.06655","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-and-robust-contextual-node","title":"Fast and Robust Contextual Node Representation Learning over Dynamic Graphs","date":"2024-11-11","arxiv_id":"2411.07123","n_code_links":0,"syntology":null},{"paper":null,"slug":"gta-net-an-iot-integrated-3d-human-pose","title":"GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction","date":"2024-11-11","arxiv_id":"2411.06725","n_code_links":0,"syntology":null},{"paper":null,"slug":"hstrack-bootstrap-end-to-end-multi-camera-3d","title":"SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking","date":"2024-11-11","arxiv_id":"2411.06780","n_code_links":0,"syntology":null},{"paper":null,"slug":"invar-rag-invariant-llm-aligned-retrieval-for","title":"Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation","date":"2024-11-11","arxiv_id":"2411.07021","n_code_links":0,"syntology":null},{"paper":null,"slug":"isochrony-controlled-speech-to-text","title":"Isochrony-Controlled Speech-to-Text Translation: A study on translating from Sino-Tibetan to Indo-European Languages","date":"2024-11-11","arxiv_id":"2411.07387","n_code_links":0,"syntology":null},{"paper":null,"slug":"la4sr-illuminating-the-dark-proteome-with","title":"LA4SR: illuminating the dark proteome with generative AI","date":"2024-11-11","arxiv_id":"2411.06798","n_code_links":0,"syntology":null},{"paper":null,"slug":"layout-control-and-semantic-guidance-with","title":"Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model","date":"2024-11-11","arxiv_id":"2411.06692","n_code_links":0,"syntology":null},{"paper":"/paper/longsafetybench-long-context-llms-struggle","slug":"longsafetybench-long-context-llms-struggle","title":"LongSafetyBench: Long-Context LLMs Struggle with Safety Issues","date":"2024-11-11","arxiv_id":"2411.06899","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["luther-sparks/longsafetybench"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/mapsam-adapting-segment-anything-model-for","slug":"mapsam-adapting-segment-anything-model-for","title":"MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps","date":"2024-11-11","arxiv_id":"2411.06971","n_code_links":1,"syntology":null},{"paper":null,"slug":"modeling-variable-guide-efficiency-in-pooled","title":"Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+","date":"2024-11-11","arxiv_id":"2411.08072","n_code_links":0,"syntology":null},{"paper":"/paper/more-expressive-attention-with-negative","slug":"more-expressive-attention-with-negative","title":"More Expressive Attention with Negative Weights","date":"2024-11-11","arxiv_id":"2411.07176","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["trestad/cogattn"],"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","unlocated"]}}},{"paper":null,"slug":"multi-modal-interpretable-automatic-video","title":"Multi-Modal interpretable automatic video captioning","date":"2024-11-11","arxiv_id":"2411.06872","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-active-privacy-auditing-in-supervised-fine","title":"On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models","date":"2024-11-11","arxiv_id":"2411.07070","n_code_links":0,"syntology":null},{"paper":"/paper/pcnet-a-human-pose-compensation-network-based","slug":"pcnet-a-human-pose-compensation-network-based","title":"PCNet: a human pose compensation network based on incremental learning for sports actions estimation","date":"2024-11-11","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/scalekd-strong-vision-transformers-could-be","slug":"scalekd-strong-vision-transformers-could-be","title":"ScaleKD: Strong Vision Transformers Could Be Excellent Teachers","date":"2024-11-11","arxiv_id":"2411.06786","n_code_links":1,"syntology":null}],"record_sha256":"d6fedc1ea87d83b0280e77518aecf0a35bb3cf6d6a2e635cb4752e4cbf149694","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}