{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/attention/papers/58","list_of":"/method/attention","method":"Attention","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":58,"pages_in_order":316,"rows_per_page":100,"rows":[5701,5800],"of":31583,"counts":{"archive_papers_tagged":31583,"with_a_code_link":13473,"where_syntology_ran_a_sample":3998,"not_listed_spam_title":0,"listed":31583,"listed_where_code_ran":3998,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":3366,"every_run_a_failure_of_syntologys_instrument":632,"listed_with_a_run_with_no_instrument_failure":3366,"listed_every_run_a_failure_of_syntologys_instrument":632,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/attention","prev":"/method/attention/papers/57","next":"/method/attention/papers/59","papers":[{"paper":"/paper/prompt-cam-making-vision-transformers","slug":"prompt-cam-making-vision-transformers","title":"Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"prompthash-affinity-prompted-collaborative-1","title":"PromptHash:Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"prototype-based-image-prompting-for-weakly","title":"Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/ps-diffusion-photorealistic-subject-driven","slug":"ps-diffusion-photorealistic-subject-driven","title":"PS-Diffusion: Photorealistic Subject-Driven Image Editing with Disentangled Control and Attention","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"r2c-mapping-room-to-chessboard-to-unlock-llm","title":"R2C: Mapping Room to Chessboard to Unlock LLM As Low-Level Action Planner","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"radiov2-5-improved-baselines-for","title":"RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"reasoning-mamba-hypergraph-guided-region","title":"Reasoning Mamba: Hypergraph-Guided Region Relation Calculating for Weakly Supervised Affordance Grounding","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/relation3d-enhancing-relation-modeling-for","slug":"relation3d-enhancing-relation-modeling-for","title":"Relation3D : Enhancing Relation Modeling for Point Cloud Instance Segmentation","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-addressing-in-language-models-via","slug":"rethinking-addressing-in-language-models-via","title":"Rethinking Addressing in Language Models via Contexualized Equivariant Positional Encoding","date":"2025-01-01","arxiv_id":"2501.00712","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["vita-group/tape"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"rethinking-noisy-video-text-retrieval-via","title":"Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-spiking-self-attention-mechanism","title":"Rethinking Spiking Self-Attention Mechanism: Implementing a-XNOR Similarity Calculation in Spiking Transformers","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-token-reduction-with-parameter","slug":"rethinking-token-reduction-with-parameter","title":"Rethinking Token Reduction with Parameter-Efficient Fine-Tuning in ViT for Pixel-Level Tasks","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-audio-visual-segmentation-with","title":"Revisiting Audio-Visual Segmentation with Vision-Centric Transformer","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-generative-replay-for-class","slug":"revisiting-generative-replay-for-class","title":"Revisiting Generative Replay for Class Incremental Object Detection","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"robsense-a-robust-multi-modal-foundation","title":"RobSense: A Robust Multi-modal Foundation Model for Remote Sensing with Static, Temporal, and Incomplete Data Adaptability","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-multimodal-survival-prediction-with-1","title":"Robust Multimodal Survival Prediction with Conditional Latent Differentiation Variational AutoEncoder","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"s-3-face-sss-compliant-facial-reflectance","title":"S^3-Face: SSS-Compliant Facial Reflectance Estimation via Diffusion Priors","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sapiensid-foundation-for-human-recognition","title":"SapiensID: Foundation for Human Recognition","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scenediffuser-city-scale-traffic-simulation","title":"SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scsa-a-plug-and-play-semantic-continuous","title":"SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sdgocc-semantic-and-depth-guided-bird-s-eye","title":"SDGOCC: Semantic and Depth-Guided Bird's-Eye View Transformation for 3D Multimodal Occupancy Prediction","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"sealion-semantic-part-aware-latent-point","title":"SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"seeing-is-not-believing-adversarial-natural","title":"Seeing is Not Believing: Adversarial Natural Object Optimization for Hard-Label 3D Scene Attacks","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"seek-common-ground-while-reserving","title":"Seek Common Ground While Reserving Differences: Semi-Supervised Image-Text Sentiment Recognition","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"seen-da-semantic-entropy-guided-domain-aware","title":"SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-controlnet-with-spatio","title":"Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-cross-view-correspondence","title":"Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"separation-of-powers-on-segregating-knowledge","title":"Separation of Powers: On Segregating Knowledge from Observation in LLM-enabled Knowledge-based Visual Question Answering","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-temporal-attention-based-target","title":"Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles","date":"2025-01-01","arxiv_id":"2501.00890","n_code_links":0,"syntology":null},{"paper":null,"slug":"spiking-transformer-introducing-accurate-1","title":"Spiking Transformer: Introducing Accurate Addition-Only Spiking Self-Attention for Transformer","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"splatter-360-generalizable-360-gaussian","title":"Splatter-360: Generalizable 360 Gaussian Splatting for Wide-baseline Panoramic Images","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"star-with-bilinear-mapping","title":"Star with Bilinear Mapping","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/subspace-constraint-and-contribution","slug":"subspace-constraint-and-contribution","title":"Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/task-aware-clustering-for-prompting-vision","slug":"task-aware-clustering-for-prompting-vision","title":"Task-Aware Clustering for Prompting Vision-Language Models","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"task-aware-cross-modal-feature-refinement","title":"Task-aware Cross-modal Feature Refinement Transformer with Large Language Models for Visual Grounding","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"texgarment-consistent-garment-uv-texture","title":"TexGarment: Consistent Garment UV Texture Generation via Efficient 3D Structure-Guided Diffusion Transformer","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/topnet-transformer-efficient-occupancy","slug":"topnet-transformer-efficient-occupancy","title":"TopNet: Transformer-Efficient Occupancy Prediction Network for Octree-Structured Point Cloud Geometry Compression","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"toward-real-world-bev-perception-depth","title":"Toward Real-world BEV Perception: Depth Uncertainty Estimation via Gaussian Splatting","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-efficient-foundation-model-for-zero","title":"Towards Efficient Foundation Model for Zero-shot Amodal Segmentation","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ucod-dpl-unsupervised-camouflaged-object","title":"UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unity-in-diversity-video-editing-via-gradient","title":"Unity in Diversity: Video Editing via Gradient-Latent Purification","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unseen-visual-anomaly-generation","title":"Unseen Visual Anomaly Generation","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"v2v3d-view-to-view-denoised-3d-reconstruction-1","title":"V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/vasparse-towards-efficient-visual-1","slug":"vasparse-towards-efficient-visual-1","title":"VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"video-language-model-pretraining-with-spatio","title":"Video Language Model Pretraining with Spatio-temporal Masking","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"vikienet-towards-efficient-3d-object","title":"ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"vodiff-controlling-object-visibility-order-in","title":"VODiff: Controlling Object Visibility Order in Text-to-Image Generation","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/volformer-explore-more-comprehensive-cube","slug":"volformer-explore-more-comprehensive-cube","title":"VolFormer: Explore More Comprehensive Cube Interaction for Hyperspectral Image Restoration and Beyond","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"vsnet-focusing-on-the-linguistic","title":"VSNet: Focusing on the Linguistic Characteristics of Sign Language","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/wavelet-and-prototype-augmented-query-based","slug":"wavelet-and-prototype-augmented-query-based","title":"Wavelet and Prototype Augmented Query-based Transformer for Pixel-level Surface Defect Detection","date":"2025-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"weakly-supervised-semantic-segmentation-via-5","title":"Weakly Supervised Semantic Segmentation via Progressive Confidence Region Expansion","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"yo-chameleon-personalized-vision-and-language","title":"Yo'Chameleon: Personalized Vision and Language Generation","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-convolution-and-attention-mechanism","title":"A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation","date":"2024-12-31","arxiv_id":"2501.01993","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-shape-guided-transformer-network-for","title":"A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images","date":"2024-12-31","arxiv_id":"2501.00360","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-context-length-and-efficient","title":"A Study on Context Length and Efficient Transformers for Biomedical Image Analysis","date":"2024-12-31","arxiv_id":"2501.00619","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-wideband-amplifying-and-filtering","title":"A wideband amplifying and filtering reconfigurable intelligent surface for wireless relay","date":"2024-12-31","arxiv_id":"2501.09759","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-lung-nodule-segmentation-and","title":"Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning","date":"2024-12-31","arxiv_id":"2501.00586","n_code_links":0,"syntology":null},{"paper":"/paper/crrg-clip-automatic-generation-of-chest","slug":"crrg-clip-automatic-generation-of-chest","title":"CRRG-CLIP: Automatic Generation of Chest Radiology Reports and Classification of Chest Radiographs","date":"2024-12-31","arxiv_id":"2501.01989","n_code_links":1,"syntology":null},{"paper":null,"slug":"dementia-detection-using-multi-modal-methods","title":"Dementia Detection using Multi-modal Methods on Audio Data","date":"2024-12-31","arxiv_id":"2501.00465","n_code_links":0,"syntology":null},{"paper":null,"slug":"echoes-in-ai-quantifying-lack-of-plot","title":"Echoes in AI: Quantifying Lack of Plot Diversity in LLM Outputs","date":"2024-12-31","arxiv_id":"2501.00273","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-variability-in-fine-tuned-models","title":"Exploring Variability in Fine-Tuned Models for Text Classification with DistilBERT","date":"2024-12-31","arxiv_id":"2501.00241","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-and-interpretable-mixed-integer-linear","title":"Fast and Interpretable Mixed-Integer Linear Program Solving by Learning Model Reduction","date":"2024-12-31","arxiv_id":"2501.00307","n_code_links":0,"syntology":null},{"paper":null,"slug":"finding-missed-code-size-optimizations-in","title":"Finding Missed Code Size Optimizations in Compilers using LLMs","date":"2024-12-31","arxiv_id":"2501.00655","n_code_links":0,"syntology":null},{"paper":null,"slug":"gpt-4-on-clinic-depression-assessment-an-llm","title":"GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study","date":"2024-12-31","arxiv_id":"2501.00199","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-fusion-for-cross-domain-sequential","title":"Image Fusion for Cross-Domain Sequential Recommendation","date":"2024-12-31","arxiv_id":"2502.15694","n_code_links":0,"syntology":null},{"paper":null,"slug":"innovative-silicosis-and-pneumonia","title":"Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques","date":"2024-12-31","arxiv_id":"2501.00520","n_code_links":0,"syntology":null},{"paper":"/paper/kae-kolmogorov-arnold-auto-encoder-for","slug":"kae-kolmogorov-arnold-auto-encoder-for","title":"KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning","date":"2024-12-31","arxiv_id":"2501.00420","n_code_links":1,"syntology":null},{"paper":null,"slug":"knowra-knowledge-retrieval-augmented-method","title":"KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities","date":"2024-12-31","arxiv_id":"2501.00571","n_code_links":0,"syntology":null},{"paper":null,"slug":"main-rag-multi-agent-filtering-retrieval","title":"MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation","date":"2024-12-31","arxiv_id":"2501.00332","n_code_links":0,"syntology":null},{"paper":null,"slug":"outlier-robust-training-of-machine-learning","title":"Outlier-Robust Training of Machine Learning Models","date":"2024-12-31","arxiv_id":"2501.00265","n_code_links":0,"syntology":null},{"paper":null,"slug":"probing-visual-language-priors-in-vlms","title":"Probing Visual Language Priors in VLMs","date":"2024-12-31","arxiv_id":"2501.00569","n_code_links":0,"syntology":null},{"paper":"/paper/rag-instruct-boosting-llms-with-diverse","slug":"rag-instruct-boosting-llms-with-diverse","title":"RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions","date":"2024-12-31","arxiv_id":"2501.00353","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["freedomintelligence/rag-instruct"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"reformer-generating-radio-fakes-for-data","title":"ReFormer: Generating Radio Fakes for Data Augmentation","date":"2024-12-31","arxiv_id":"2501.00282","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-vehicle-detection-based-on","title":"Research on vehicle detection based on improved YOLOv8 network","date":"2024-12-31","arxiv_id":"2501.00300","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-generation-with-graphs","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","date":"2024-12-31","arxiv_id":"2501.00309","n_code_links":0,"syntology":null},{"paper":"/paper/solving-partial-differential-equations-with-5","slug":"solving-partial-differential-equations-with-5","title":"Solving Partial Differential Equations with Random Feature Models","date":"2024-12-31","arxiv_id":"2501.00288","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatio-temporal-multi-subgraph-gcn-for-3d","title":"Spatio-Temporal Multi-Subgraph GCN for 3D Human Motion Prediction","date":"2024-12-31","arxiv_id":"2501.00317","n_code_links":0,"syntology":null},{"paper":null,"slug":"starformer-a-novel-spatio-temporal","title":"STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis","date":"2024-12-31","arxiv_id":"2501.00378","n_code_links":0,"syntology":null},{"paper":"/paper/storm-spatio-temporal-reconstruction-model","slug":"storm-spatio-temporal-reconstruction-model","title":"STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes","date":"2024-12-31","arxiv_id":"2501.00602","n_code_links":1,"syntology":null},{"paper":"/paper/titans-learning-to-memorize-at-test-time","slug":"titans-learning-to-memorize-at-test-time","title":"Titans: Learning to Memorize at Test Time","date":"2024-12-31","arxiv_id":"2501.00663","n_code_links":1,"syntology":null},{"paper":"/paper/token-pruning-for-caching-better-9-times","slug":"token-pruning-for-caching-better-9-times","title":"Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for Free","date":"2024-12-31","arxiv_id":"2501.00375","n_code_links":1,"syntology":null},{"paper":null,"slug":"why-are-positional-encodings-nonessential-for","title":"Why Are Positional Encodings Nonessential for Deep Autoregressive Transformers? Revisiting a Petroglyph","date":"2024-12-31","arxiv_id":"2501.00659","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-deep-learning-approach-for-facial","slug":"a-novel-deep-learning-approach-for-facial","title":"A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease","date":"2024-12-30","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"align-attention-heads-before-merging-them-an","title":"Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA","date":"2024-12-30","arxiv_id":"2412.20677","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-unsupervised-anomaly-detection-in","title":"An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework","date":"2024-12-30","arxiv_id":"2501.00107","n_code_links":0,"syntology":null},{"paper":"/paper/attention-driven-metapath-encoding-in","slug":"attention-driven-metapath-encoding-in","title":"Attention-Driven Metapath Encoding in Heterogeneous Graphs","date":"2024-12-30","arxiv_id":"2412.20678","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-is-all-you-need-for-mixture-of","title":"Attention Is All You Need For Mixture-of-Depths Routing","date":"2024-12-30","arxiv_id":"2412.20875","n_code_links":0,"syntology":null},{"paper":"/paper/averagelinear-enhance-long-term-time-series","slug":"averagelinear-enhance-long-term-time-series","title":"AverageTime: Enhance Long-Term Time Series Forecasting with Simple Averaging","date":"2024-12-30","arxiv_id":"2412.20727","n_code_links":1,"syntology":null},{"paper":null,"slug":"casesumm-a-large-scale-dataset-for-long","title":"CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions","date":"2024-12-30","arxiv_id":"2501.00097","n_code_links":0,"syntology":null},{"paper":null,"slug":"ecg-guided-individual-identification-via-ppg","title":"ECG-guided individual identification via PPG","date":"2024-12-30","arxiv_id":"2501.01983","n_code_links":0,"syntology":null},{"paper":"/paper/edicho-consistent-image-editing-in-the-wild","slug":"edicho-consistent-image-editing-in-the-wild","title":"Edicho: Consistent Image Editing in the Wild","date":"2024-12-30","arxiv_id":"2412.21079","n_code_links":1,"syntology":null},{"paper":"/paper/facilitating-large-language-model-russian","slug":"facilitating-large-language-model-russian","title":"Facilitating large language model Russian adaptation with Learned Embedding Propagation","date":"2024-12-30","arxiv_id":"2412.21140","n_code_links":1,"syntology":null},{"paper":null,"slug":"federated-learning-with-workload-reduction","title":"Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection","date":"2024-12-30","arxiv_id":"2501.00170","n_code_links":0,"syntology":null},{"paper":"/paper/frequency-aware-event-cloud-network","slug":"frequency-aware-event-cloud-network","title":"Frequency-aware Event Cloud Network","date":"2024-12-30","arxiv_id":"2412.20803","n_code_links":0,"syntology":{"ran":5,"of":7,"n_ran_checked":4,"n_instrument":1,"unverified":2,"pointer_only":7,"phrase":"5 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; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"keygs-a-keyframe-centric-gaussian-splatting","title":"KeyGS: A Keyframe-Centric Gaussian Splatting Method for Monocular Image Sequences","date":"2024-12-30","arxiv_id":"2412.20767","n_code_links":0,"syntology":null},{"paper":null,"slug":"lidar-camera-fusion-for-video-panoptic","title":"LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training","date":"2024-12-30","arxiv_id":"2412.20881","n_code_links":0,"syntology":null},{"paper":"/paper/low-light-image-enhancement-via-generative","slug":"low-light-image-enhancement-via-generative","title":"Low-Light Image Enhancement via Generative Perceptual Priors","date":"2024-12-30","arxiv_id":"2412.20916","n_code_links":1,"syntology":null},{"paper":null,"slug":"ls-gan-human-motion-synthesis-with-latent","title":"LS-GAN: Human Motion Synthesis with Latent-space GANs","date":"2024-12-30","arxiv_id":"2501.01449","n_code_links":0,"syntology":null},{"paper":null,"slug":"marssqe-stereo-quality-enhancement-for","title":"MarsSQE: Stereo Quality Enhancement for Martian Images Using Bi-level Cross-view Attention","date":"2024-12-30","arxiv_id":"2412.20685","n_code_links":0,"syntology":null},{"paper":null,"slug":"measuring-large-language-models-capacity-to","title":"Measuring Large Language Models Capacity to Annotate Journalistic Sourcing","date":"2024-12-30","arxiv_id":"2501.00164","n_code_links":0,"syntology":null}],"record_sha256":"434512bdb9699343c78bc5420bb56089d480e7aefcd55e15843675371cf31b47","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}