{"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/52","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":52,"pages_in_order":375,"rows_per_page":100,"rows":[5101,5200],"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/51","next":"/method/softmax/papers/53","papers":[{"paper":null,"slug":"mams-model-agnostic-module-selection","title":"MAMS: Model-Agnostic Module Selection Framework for Video Captioning","date":"2025-01-30","arxiv_id":"2501.18269","n_code_links":0,"syntology":null},{"paper":"/paper/matir-a-hybrid-mamba-transformer-image","slug":"matir-a-hybrid-mamba-transformer-image","title":"MatIR: A Hybrid Mamba-Transformer Image Restoration Model","date":"2025-01-30","arxiv_id":"2501.18401","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-concentration-levels-of-air","title":"Predicting concentration levels of air pollutants by transfer learning and recurrent neural network","date":"2025-01-30","arxiv_id":"2502.01654","n_code_links":0,"syntology":null},{"paper":null,"slug":"pso-net-development-of-an-automated-psoriasis","title":"PSO-Net: Development of an automated psoriasis assessment system using attention-based interpretable deep neural networks","date":"2025-01-30","arxiv_id":"2501.18782","n_code_links":0,"syntology":null},{"paper":"/paper/rbft-robust-fine-tuning-for-retrieval","slug":"rbft-robust-fine-tuning-for-retrieval","title":"RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects","date":"2025-01-30","arxiv_id":"2501.18365","n_code_links":1,"syntology":null},{"paper":null,"slug":"remote-real-time-ego-motion-tracking-for","title":"REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning","date":"2025-01-30","arxiv_id":"2501.18124","n_code_links":0,"syntology":null},{"paper":null,"slug":"rethinking-the-upsampling-layer-in","title":"Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution","date":"2025-01-30","arxiv_id":"2501.18664","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-generation-based-llm","title":"Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning","date":"2025-01-30","arxiv_id":"2502.15727","n_code_links":0,"syntology":null},{"paper":null,"slug":"rope-to-nope-and-back-again-a-new-hybrid","title":"Rope to Nope and Back Again: A New Hybrid Attention Strategy","date":"2025-01-30","arxiv_id":"2501.18795","n_code_links":0,"syntology":null},{"paper":null,"slug":"run-reversible-unfolding-network-for","title":"RUN: Reversible Unfolding Network for Concealed Object Segmentation","date":"2025-01-30","arxiv_id":"2501.18783","n_code_links":0,"syntology":null},{"paper":"/paper/sana-1-5-efficient-scaling-of-training-time","slug":"sana-1-5-efficient-scaling-of-training-time","title":"SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer","date":"2025-01-30","arxiv_id":"2501.18427","n_code_links":1,"syntology":null},{"paper":null,"slug":"scalable-and-cost-efficient-ml-inference","title":"Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions","date":"2025-01-30","arxiv_id":"2502.12017","n_code_links":0,"syntology":null},{"paper":null,"slug":"state-stream-transformer-sst-emergent","title":"State Stream Transformer (SST) : Emergent Metacognitive Behaviours Through Latent State Persistence","date":"2025-01-30","arxiv_id":"2501.18356","n_code_links":0,"syntology":null},{"paper":null,"slug":"structure-development-in-list-sorting","title":"Structure Development in List-Sorting Transformers","date":"2025-01-30","arxiv_id":"2501.18666","n_code_links":0,"syntology":null},{"paper":null,"slug":"survey-and-improvement-strategies-for-gene","title":"Survey and Improvement Strategies for Gene Prioritization with Large Language Models","date":"2025-01-30","arxiv_id":"2501.18794","n_code_links":0,"syntology":null},{"paper":null,"slug":"transfer-learning-for-keypoint-detection-in","title":"Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images","date":"2025-01-30","arxiv_id":"2501.18453","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-semantic-genetic-programming-for","title":"Transformer Semantic Genetic Programming for Symbolic Regression","date":"2025-01-30","arxiv_id":"2501.18479","n_code_links":0,"syntology":null},{"paper":"/paper/unraveling-the-capabilities-of-language","slug":"unraveling-the-capabilities-of-language","title":"Unraveling the Capabilities of Language Models in News Summarization","date":"2025-01-30","arxiv_id":"2501.18128","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-computer-vision-for-skin-disease","title":"Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification","date":"2025-01-30","arxiv_id":"2501.18161","n_code_links":0,"syntology":null},{"paper":"/paper/wildchat-50m-a-deep-dive-into-the-role-of","slug":"wildchat-50m-a-deep-dive-into-the-role-of","title":"WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training","date":"2025-01-30","arxiv_id":"2501.18511","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":6,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["penfever/wildchat-50m"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/2ssp-a-two-stage-framework-for-structured","slug":"2ssp-a-two-stage-framework-for-structured","title":"2SSP: A Two-Stage Framework for Structured Pruning of LLMs","date":"2025-01-29","arxiv_id":"2501.17771","n_code_links":1,"syntology":null},{"paper":null,"slug":"boosting-weak-positives-for-text-based-person","title":"Boosting Weak Positives for Text Based Person Search","date":"2025-01-29","arxiv_id":"2501.17586","n_code_links":0,"syntology":null},{"paper":null,"slug":"byzantine-robust-federated-learning-over-ring","title":"Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing","date":"2025-01-29","arxiv_id":"2501.17392","n_code_links":0,"syntology":null},{"paper":"/paper/consistency-guided-robust-learning-for","slug":"consistency-guided-robust-learning-for","title":"Consistency-Guided Robust Learning for Content-Agnostic Radio Frequency Fingerprinting","date":"2025-01-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"context-aware-semantic-recomposition","title":"Context-Aware Semantic Recomposition Mechanism for Large Language Models","date":"2025-01-29","arxiv_id":"2501.17386","n_code_links":0,"syntology":null},{"paper":"/paper/contourformer-real-time-contour-based-end-to","slug":"contourformer-real-time-contour-based-end-to","title":"ContourFormer:Real-Time Contour-Based End-to-End Instance Segmentation Transformer","date":"2025-01-29","arxiv_id":"2501.17688","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-lingual-embedding-clustering-for","title":"Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition","date":"2025-01-29","arxiv_id":"2501.17615","n_code_links":0,"syntology":null},{"paper":null,"slug":"dint-transformer","title":"DINT Transformer","date":"2025-01-29","arxiv_id":"2501.17486","n_code_links":0,"syntology":null},{"paper":null,"slug":"extracting-inter-protein-interactions-via","title":"Extracting Inter-Protein Interactions Via Multitasking Graph Structure Learning","date":"2025-01-29","arxiv_id":"2501.17589","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-graphs-for-table-and-text-based","title":"Hybrid Graphs for Table-and-Text based Question Answering using LLMs","date":"2025-01-29","arxiv_id":"2501.17767","n_code_links":0,"syntology":null},{"paper":"/paper/large-language-models-think-too-fast-to","slug":"large-language-models-think-too-fast-to","title":"Large Language Models Think Too Fast To Explore Effectively","date":"2025-01-29","arxiv_id":"2501.18009","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-in-context-learning-and-retrieval","title":"Leveraging In-Context Learning and Retrieval-Augmented Generation for Automatic Question Generation in Educational Domains","date":"2025-01-29","arxiv_id":"2501.17397","n_code_links":0,"syntology":null},{"paper":null,"slug":"matrix-product-sketching-via-coordinated","title":"Matrix Product Sketching via Coordinated Sampling","date":"2025-01-29","arxiv_id":"2501.17836","n_code_links":0,"syntology":null},{"paper":null,"slug":"nf-mkv-net-a-constraint-preserving-neural","title":"NF-MKV Net: A Constraint-Preserving Neural Network Approach to Solving Mean-Field Games Equilibrium","date":"2025-01-29","arxiv_id":"2501.17450","n_code_links":0,"syntology":null},{"paper":null,"slug":"p-tame-explain-any-image-classifier-with","title":"P-TAME: Explain Any Image Classifier with Trained Perturbations","date":"2025-01-29","arxiv_id":"2501.17813","n_code_links":0,"syntology":null},{"paper":null,"slug":"prompt-oriented-output-of-culture-specific","title":"Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Model: An Initial Multi-layered Tabular Review","date":"2025-01-29","arxiv_id":"2501.18644","n_code_links":0,"syntology":null},{"paper":"/paper/pulmofusion-advancing-pulmonary-health-with","slug":"pulmofusion-advancing-pulmonary-health-with","title":"PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion","date":"2025-01-29","arxiv_id":"2501.17699","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-frameworks-for-speaker","slug":"self-supervised-frameworks-for-speaker","title":"Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling","date":"2025-01-29","arxiv_id":"2501.17772","n_code_links":1,"syntology":null},{"paper":null,"slug":"shared-diff-transformer","title":"Shared DIFF Transformer","date":"2025-01-29","arxiv_id":"2501.17900","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-context-recomposition-for-large","title":"Structured Context Recomposition for Large Language Models Using Probabilistic Layer Realignment","date":"2025-01-29","arxiv_id":"2501.17617","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-imitation-game-according-to-turing","title":"The Imitation Game According To Turing","date":"2025-01-29","arxiv_id":"2501.17629","n_code_links":0,"syntology":null},{"paper":null,"slug":"transformer-based-time-series-forecasting-for","title":"Transformer Based Time-Series Forecasting for Stock","date":"2025-01-29","arxiv_id":"2502.09625","n_code_links":0,"syntology":null},{"paper":"/paper/transrad-retentive-vision-transformer-for","slug":"transrad-retentive-vision-transformer-for","title":"TransRAD: Retentive Vision Transformer for Enhanced Radar Object Detection","date":"2025-01-29","arxiv_id":"2501.17977","n_code_links":1,"syntology":null},{"paper":null,"slug":"watch-your-stepp-semantic-traversability","title":"Watch Your STEPP: Semantic Traversability Estimation using Pose Projected Features","date":"2025-01-29","arxiv_id":"2501.17594","n_code_links":0,"syntology":null},{"paper":"/paper/an-attention-locating-algorithm-for","slug":"an-attention-locating-algorithm-for","title":"An Attention-Locating Algorithm for Eliminating Background Effects in Fine-grained Visual Classification","date":"2025-01-28","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"astral-automated-safety-testing-of-large","title":"ASTRAL: Automated Safety Testing of Large Language Models","date":"2025-01-28","arxiv_id":"2501.17132","n_code_links":0,"syntology":null},{"paper":null,"slug":"attribution-analysis-of-legal-language-as","title":"Attribution analysis of legal language as used by LLM","date":"2025-01-28","arxiv_id":"2501.17330","n_code_links":0,"syntology":null},{"paper":null,"slug":"balancing-content-size-in-rag-text2sql-system","title":"Balancing Content Size in RAG-Text2SQL System","date":"2025-01-28","arxiv_id":"2502.15723","n_code_links":0,"syntology":null},{"paper":"/paper/cascadev-an-implementation-of-wurstchen","slug":"cascadev-an-implementation-of-wurstchen","title":"CascadeV: An Implementation of Wurstchen Architecture for Video Generation","date":"2025-01-28","arxiv_id":"2501.16612","n_code_links":1,"syntology":null},{"paper":null,"slug":"chinese-stock-prediction-based-on-a-multi","title":"Chinese Stock Prediction Based on a Multi-Modal Transformer Framework: Macro-Micro Information Fusion","date":"2025-01-28","arxiv_id":"2501.16621","n_code_links":0,"syntology":null},{"paper":null,"slug":"cortical-temporal-mismatch-compensation-in","title":"Cortical Temporal Mismatch Compensation in Bimodal Cochlear Implant Users: Selective Attention Decoding and Pupillometry Study","date":"2025-01-28","arxiv_id":"2501.17048","n_code_links":0,"syntology":null},{"paper":null,"slug":"cubediff-repurposing-diffusion-based-image","title":"CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation","date":"2025-01-28","arxiv_id":"2501.17162","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-free-model-related-attacks-unleashing","title":"Data-Free Model-Related Attacks: Unleashing the Potential of Generative AI","date":"2025-01-28","arxiv_id":"2501.16671","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-harassment-and-defamation-in","slug":"detecting-harassment-and-defamation-in","title":"Detecting harassment and defamation in cyberbullying with emotion-adaptive training","date":"2025-01-28","arxiv_id":"2501.16925","n_code_links":1,"syntology":null},{"paper":null,"slug":"dfcon-attention-driven-supervised-contrastive","title":"DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection","date":"2025-01-28","arxiv_id":"2501.16704","n_code_links":0,"syntology":null},{"paper":"/paper/exponential-family-attention","slug":"exponential-family-attention","title":"Exponential Family Attention","date":"2025-01-28","arxiv_id":"2501.16790","n_code_links":1,"syntology":null},{"paper":"/paper/extending-information-bottleneck-attribution","slug":"extending-information-bottleneck-attribution","title":"Extending Information Bottleneck Attribution to Video Sequences","date":"2025-01-28","arxiv_id":"2501.16889","n_code_links":1,"syntology":null},{"paper":null,"slug":"flexmotion-lightweight-physics-aware-and","title":"FlexMotion: Lightweight, Physics-Aware, and Controllable Human Motion Generation","date":"2025-01-28","arxiv_id":"2501.16778","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-quantum-combinatorial-optimization","title":"Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver","date":"2025-01-28","arxiv_id":"2501.16986","n_code_links":0,"syntology":null},{"paper":"/paper/graph-of-attacks-with-pruning-optimizing","slug":"graph-of-attacks-with-pruning-optimizing","title":"Graph of Attacks with Pruning: Optimizing Stealthy Jailbreak Prompt Generation for Enhanced LLM Content Moderation","date":"2025-01-28","arxiv_id":"2501.18638","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-transformers-for-inverse-physics","title":"Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils","date":"2025-01-28","arxiv_id":"2501.17081","n_code_links":0,"syntology":null},{"paper":"/paper/histoires-morales-a-french-dataset-for","slug":"histoires-morales-a-french-dataset-for","title":"Histoires Morales: A French Dataset for Assessing Moral Alignment","date":"2025-01-28","arxiv_id":"2501.17117","n_code_links":1,"syntology":null},{"paper":null,"slug":"itvton-virtual-try-on-diffusion-transformer","title":"ITVTON:Virtual Try-On Diffusion Transformer Model Based on Integrated Image and Text","date":"2025-01-28","arxiv_id":"2501.16757","n_code_links":0,"syntology":null},{"paper":"/paper/jre-l-journalist-reader-and-editor-llms-in","slug":"jre-l-journalist-reader-and-editor-llms-in","title":"JRE-L: Journalist, Reader, and Editor LLMs in the Loop for Science Journalism for the General Audience","date":"2025-01-28","arxiv_id":"2501.16865","n_code_links":1,"syntology":null},{"paper":"/paper/mamba-shedder-post-transformer-compression","slug":"mamba-shedder-post-transformer-compression","title":"Mamba-Shedder: Post-Transformer Compression for Efficient Selective Structured State Space Models","date":"2025-01-28","arxiv_id":"2501.17088","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["intellabs/hardware-aware-automated-machine-learning"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"maucell-an-adaptive-multi-attention-framework","title":"MAUCell: An Adaptive Multi-Attention Framework for Video Frame Prediction","date":"2025-01-28","arxiv_id":"2501.16997","n_code_links":0,"syntology":null},{"paper":null,"slug":"midi-gpt-a-controllable-generative-model-for","title":"MIDI-GPT: A Controllable Generative Model for Computer-Assisted Multitrack Music Composition","date":"2025-01-28","arxiv_id":"2501.17011","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-physics-simulations-via-coupled-fourier","title":"Multi-Physics Simulations via Coupled Fourier Neural Operator","date":"2025-01-28","arxiv_id":"2501.17296","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-abstraction-level-retrieve-augment","title":"Multiple Abstraction Level Retrieve Augment Generation","date":"2025-01-28","arxiv_id":"2501.16952","n_code_links":0,"syntology":null},{"paper":null,"slug":"one-head-eight-arms-block-matrix-based-low","title":"One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning","date":"2025-01-28","arxiv_id":"2501.16720","n_code_links":0,"syntology":null},{"paper":null,"slug":"open-source-retrieval-augmented-generation","title":"Open-Source Retrieval Augmented Generation Framework for Retrieving Accurate Medication Insights from Formularies for African Healthcare Workers","date":"2025-01-28","arxiv_id":"2502.15722","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-system-environment-synergistic","title":"Quantifying system-environment synergistic information by effective information decomposition","date":"2025-01-28","arxiv_id":"2501.16676","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantifying-uncertainty-and-variability-in","title":"Quantifying Uncertainty and Variability in Machine Learning: Confidence Intervals for Quantiles in Performance Metric Distributions","date":"2025-01-28","arxiv_id":"2501.16931","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-functional-brain-connectome","slug":"rethinking-functional-brain-connectome","title":"Rethinking Functional Brain Connectome Analysis: Do Graph Deep Learning Models Help?","date":"2025-01-28","arxiv_id":"2501.17207","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["learningkeqi/rethinkingbca"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"paper":"/paper/saferag-benchmarking-security-in-retrieval","slug":"saferag-benchmarking-security-in-retrieval","title":"SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model","date":"2025-01-28","arxiv_id":"2501.18636","n_code_links":1,"syntology":null},{"paper":null,"slug":"scenario-understanding-of-traffic-scenes","title":"Scenario Understanding of Traffic Scenes Through Large Visual Language Models","date":"2025-01-28","arxiv_id":"2501.17131","n_code_links":0,"syntology":null},{"paper":null,"slug":"separate-motion-from-appearance-customizing","title":"Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models","date":"2025-01-28","arxiv_id":"2501.16714","n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-relative-positional-encoding-in","title":"Toward Relative Positional Encoding in Spiking Transformers","date":"2025-01-28","arxiv_id":"2501.16745","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-the-generalization-of-multi-view","title":"Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis","date":"2025-01-28","arxiv_id":"2501.16768","n_code_links":0,"syntology":null},{"paper":"/paper/vit-2spn-vision-transformer-based-dual-stream","slug":"vit-2spn-vision-transformer-based-dual-stream","title":"ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification","date":"2025-01-28","arxiv_id":"2501.17260","n_code_links":1,"syntology":null},{"paper":null,"slug":"what-really-matters-for-learning-based-lidar","title":"What Really Matters for Learning-based LiDAR-Camera Calibration","date":"2025-01-28","arxiv_id":"2501.16969","n_code_links":0,"syntology":null},{"paper":null,"slug":"whispers-of-sound-enhancing-information","title":"Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence","date":"2025-01-28","arxiv_id":"2501.16813","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comprehensive-study-on-fine-tuning-large","title":"A Comprehensive Study on Fine-Tuning Large Language Models for Medical Question Answering Using Classification Models and Comparative Analysis","date":"2025-01-27","arxiv_id":"2501.17190","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-optimized-vertical-axis","title":"A machine-learning optimized vertical-axis wind turbine","date":"2025-01-27","arxiv_id":"2501.17886","n_code_links":0,"syntology":null},{"paper":null,"slug":"arflow-autogressive-flow-with-hybrid-linear","title":"ARFlow: Autogressive Flow with Hybrid Linear Attention","date":"2025-01-27","arxiv_id":"2501.16085","n_code_links":0,"syntology":null},{"paper":null,"slug":"clearsight-human-vision-inspired-solutions","title":"ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring","date":"2025-01-27","arxiv_id":"2501.15808","n_code_links":0,"syntology":null},{"paper":null,"slug":"copyright-and-competition-estimating-supply","title":"Copyright and Competition: Estimating Supply and Demand with Unstructured Data","date":"2025-01-27","arxiv_id":"2501.16120","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-domain-semantic-segmentation-with-large","title":"Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation","date":"2025-01-27","arxiv_id":"2501.16467","n_code_links":0,"syntology":null},{"paper":"/paper/csf-net-cross-modal-spatiotemporal-fusion","slug":"csf-net-cross-modal-spatiotemporal-fusion","title":"CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting","date":"2025-01-27","arxiv_id":"2501.16400","n_code_links":1,"syntology":null},{"paper":null,"slug":"do-existing-testing-tools-really-uncover","title":"Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models?","date":"2025-01-27","arxiv_id":"2501.15775","n_code_links":0,"syntology":null},{"paper":null,"slug":"edsep-an-effective-diffusion-based-method-for","title":"EDSep: An Effective Diffusion-Based Method for Speech Source Separation","date":"2025-01-27","arxiv_id":"2501.15965","n_code_links":0,"syntology":null},{"paper":"/paper/efficiency-bottlenecks-of-convolutional","slug":"efficiency-bottlenecks-of-convolutional","title":"Efficiency Bottlenecks of Convolutional Kolmogorov-Arnold Networks: A Comprehensive Scrutiny with ImageNet, AlexNet, LeNet and Tabular Classification","date":"2025-01-27","arxiv_id":"2501.15757","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-object-detection-of-marine-debris","title":"Efficient Object Detection of Marine Debris using Pruned YOLO Model","date":"2025-01-27","arxiv_id":"2501.16571","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-and-exploring-mild-cognitive","title":"Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0","date":"2025-01-27","arxiv_id":"2501.16201","n_code_links":0,"syntology":null},{"paper":null,"slug":"falcon-resolving-visual-redundancy-and","title":"FALCON: Resolving Visual Redundancy and Fragmentation in High-resolution Multimodal Large Language Models via Visual Registers","date":"2025-01-27","arxiv_id":"2501.16297","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-molecules-to-mixtures-learning","title":"From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases","date":"2025-01-27","arxiv_id":"2501.16271","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-ai-for-lyapunov-optimization","title":"Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking","date":"2025-01-27","arxiv_id":"2501.15928","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernels-of-selfhood-gpt-4o-shows-humanlike","title":"Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive consistency moderated by free choice","date":"2025-01-27","arxiv_id":"2502.07088","n_code_links":0,"syntology":null},{"paper":"/paper/lctg-bench-llm-controlled-text-generation","slug":"lctg-bench-llm-controlled-text-generation","title":"LCTG Bench: LLM Controlled Text Generation Benchmark","date":"2025-01-27","arxiv_id":"2501.15875","n_code_links":1,"syntology":null},{"paper":null,"slug":"lemmahead-rag-assisted-proof-generation-using","title":"LemmaHead: RAG Assisted Proof Generation Using Large Language Models","date":"2025-01-27","arxiv_id":"2501.15797","n_code_links":0,"syntology":null}],"record_sha256":"bed804f123300df853d059bddcb46a6ba7db5367255ba5c25ad8d4a2670f5218","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}