{"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/117","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":117,"pages_in_order":375,"rows_per_page":100,"rows":[11601,11700],"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/116","next":"/method/softmax/papers/118","papers":[{"paper":null,"slug":"a-comparison-of-llm-finetuning-methods","title":"A Comparison of LLM Finetuning Methods & Evaluation Metrics with Travel Chatbot Use Case","date":"2024-08-07","arxiv_id":"2408.03562","n_code_links":0,"syntology":null},{"paper":null,"slug":"bi-level-spatial-and-channel-aware","title":"Bi-Level Spatial and Channel-aware Transformer for Learned Image Compression","date":"2024-08-07","arxiv_id":"2408.03842","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-rule-based-insights-enhance-llms-for","title":"Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology","date":"2024-08-07","arxiv_id":"2408.04121","n_code_links":0,"syntology":null},{"paper":"/paper/care-a-clue-guided-assistant-for-csrs-to-read","slug":"care-a-clue-guided-assistant-for-csrs-to-read","title":"CARE: A Clue-guided Assistant for CSRs to Read User Manuals","date":"2024-08-07","arxiv_id":"2408.03633","n_code_links":1,"syntology":null},{"paper":"/paper/cas-vit-convolutional-additive-self-attention","slug":"cas-vit-convolutional-additive-self-attention","title":"CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications","date":"2024-08-07","arxiv_id":"2408.03703","n_code_links":2,"syntology":{"ran":6,"of":6,"n_ran_checked":5,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["tianfang-zhang/cas-vit"],"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":"/paper/concept-conductor-orchestrating-multiple","slug":"concept-conductor-orchestrating-multiple","title":"Concept Conductor: Orchestrating Multiple Personalized Concepts in Text-to-Image Synthesis","date":"2024-08-07","arxiv_id":"2408.03632","n_code_links":1,"syntology":null},{"paper":null,"slug":"could-chatgpt-get-an-engineering-degree","title":"Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants","date":"2024-08-07","arxiv_id":"2408.11841","n_code_links":0,"syntology":null},{"paper":"/paper/early-prediction-of-causes-not-effects-in","slug":"early-prediction-of-causes-not-effects-in","title":"Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting","date":"2024-08-07","arxiv_id":"2408.03816","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-rag-based-vulnerability","slug":"exploring-rag-based-vulnerability","title":"VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs","date":"2024-08-07","arxiv_id":"2408.04125","n_code_links":1,"syntology":null},{"paper":null,"slug":"fiber-neural-networks-for-the-intelligent","title":"Fiber neural networks for the intelligent optical fiber communications","date":"2024-08-07","arxiv_id":"2408.12602","n_code_links":0,"syntology":null},{"paper":null,"slug":"fmifood-multi-modal-contrastive-learning-for","title":"FMiFood: Multi-modal Contrastive Learning for Food Image Classification","date":"2024-08-07","arxiv_id":"2408.03922","n_code_links":0,"syntology":null},{"paper":"/paper/global-local-progressive-integration-network","slug":"global-local-progressive-integration-network","title":"No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer","date":"2024-08-07","arxiv_id":"2408.03885","n_code_links":1,"syntology":null},{"paper":"/paper/hique-hierarchical-question-embedding-network","slug":"hique-hierarchical-question-embedding-network","title":"HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection","date":"2024-08-07","arxiv_id":"2408.03648","n_code_links":1,"syntology":null},{"paper":"/paper/image-to-latex-converter-for-mathematical","slug":"image-to-latex-converter-for-mathematical","title":"Image-to-LaTeX Converter for Mathematical Formulas and Text","date":"2024-08-07","arxiv_id":"2408.04015","n_code_links":1,"syntology":null},{"paper":null,"slug":"inter-series-transformer-attending-to","title":"Inter-Series Transformer: Attending to Products in Time Series Forecasting","date":"2024-08-07","arxiv_id":"2408.03872","n_code_links":0,"syntology":null},{"paper":"/paper/is-child-directed-speech-effective-training","slug":"is-child-directed-speech-effective-training","title":"Is Child-Directed Speech Effective Training Data for Language Models?","date":"2024-08-07","arxiv_id":"2408.03617","n_code_links":1,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["styfeng/tinydialogues"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"jarvis-detecting-actions-in-video-using","title":"JARViS: Detecting Actions in Video Using Unified Actor-Scene Context Relation Modeling","date":"2024-08-07","arxiv_id":"2408.03612","n_code_links":0,"syntology":null},{"paper":"/paper/leveraging-llms-for-enhanced-open-vocabulary","slug":"leveraging-llms-for-enhanced-open-vocabulary","title":"Query3D: LLM-Powered Open-Vocabulary Scene Segmentation with Language Embedded 3D Gaussian","date":"2024-08-07","arxiv_id":"2408.03516","n_code_links":1,"syntology":null},{"paper":null,"slug":"maxmind-a-memory-loop-network-to-enhance","title":"MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models","date":"2024-08-07","arxiv_id":"2408.03841","n_code_links":0,"syntology":null},{"paper":"/paper/nacl-a-general-and-effective-kv-cache","slug":"nacl-a-general-and-effective-kv-cache","title":"NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time","date":"2024-08-07","arxiv_id":"2408.03675","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["PaddlePaddle/Research"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"packmamba-efficient-processing-of-variable","title":"PackMamba: Efficient Processing of Variable-Length Sequences in Mamba training","date":"2024-08-07","arxiv_id":"2408.03865","n_code_links":0,"syntology":null},{"paper":"/paper/pavecap-the-first-multimodal-framework-for","slug":"pavecap-the-first-multimodal-framework-for","title":"PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation","date":"2024-08-07","arxiv_id":"2408.04110","n_code_links":1,"syntology":null},{"paper":"/paper/pick-of-the-bunch-detecting-infrared-small","slug":"pick-of-the-bunch-detecting-infrared-small","title":"Pick of the Bunch: Detecting Infrared Small Targets Beyond Hit-Miss Trade-Offs via Selective Rank-Aware Attention","date":"2024-08-07","arxiv_id":"2408.03717","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: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["grokcv/serankdet"],"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":"prism-progressive-dependency-maximization-for","title":"PRISM: PRogressive dependency maxImization for Scale-invariant image Matching","date":"2024-08-07","arxiv_id":"2408.03598","n_code_links":0,"syntology":null},{"paper":"/paper/railtrack-davit-a-vision-transformer-based","slug":"railtrack-davit-a-vision-transformer-based","title":"RailTrack-DaViT: A Vision Transformer-Based Approach for Automated Railway Track Defect Detection","date":"2024-08-07","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"retrieval-augmentation-via-user-interest","title":"Retrieval Augmentation via User Interest Clustering","date":"2024-08-07","arxiv_id":"2408.03886","n_code_links":0,"syntology":null},{"paper":"/paper/sam2-path-a-better-segment-anything-model-for","slug":"sam2-path-a-better-segment-anything-model-for","title":"Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation","date":"2024-08-07","arxiv_id":"2408.03651","n_code_links":1,"syntology":null},{"paper":null,"slug":"socfedgpt-federated-gpt-based-adaptive","title":"SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks","date":"2024-08-07","arxiv_id":"2408.05243","n_code_links":0,"syntology":null},{"paper":null,"slug":"soft-hard-attention-u-net-model-and-benchmark","title":"Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal","date":"2024-08-07","arxiv_id":"2408.03734","n_code_links":0,"syntology":null},{"paper":"/paper/surgformer-surgical-transformer-with","slug":"surgformer-surgical-transformer-with","title":"Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition","date":"2024-08-07","arxiv_id":"2408.03867","n_code_links":1,"syntology":null},{"paper":"/paper/swinshadow-shifted-window-for-ambiguous","slug":"swinshadow-shifted-window-for-ambiguous","title":"SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection","date":"2024-08-07","arxiv_id":"2408.03521","n_code_links":1,"syntology":null},{"paper":null,"slug":"tale-training-free-cross-domain-image","title":"TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization","date":"2024-08-07","arxiv_id":"2408.03637","n_code_links":0,"syntology":null},{"paper":"/paper/time-is-not-enough-time-frequency-based","slug":"time-is-not-enough-time-frequency-based","title":"Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models","date":"2024-08-07","arxiv_id":"2408.03636","n_code_links":1,"syntology":null},{"paper":"/paper/tree-attention-topology-aware-decoding-for","slug":"tree-attention-topology-aware-decoding-for","title":"Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters","date":"2024-08-07","arxiv_id":"2408.04093","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":["zyphra/tree_attention"],"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":"trustworthy-image-semantic-communication-with","title":"Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency","date":"2024-08-07","arxiv_id":"2408.03806","n_code_links":0,"syntology":null},{"paper":null,"slug":"zero-delay-qkv-compression-for-mitigating-kv","title":"FDC: Fast KV Dimensionality Compression for Efficient LLM Inference","date":"2024-08-07","arxiv_id":"2408.04107","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02888","title":"VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation","date":"2024-08-06","arxiv_id":"2408.02888","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02923","title":"Intermediate direct preference optimization","date":"2024-08-06","arxiv_id":"2408.02923","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02946","slug":"2408-02946","title":"Data Poisoning in LLMs: Jailbreak-Tuning and Scaling Laws","date":"2024-08-06","arxiv_id":"2408.02946","n_code_links":2,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"pointer_only":5,"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) · 4 unverified","official":{"repos":["alignmentresearch/scaling-poisoning"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":"/paper/2408-02970","slug":"2408-02970","title":"EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization","date":"2024-08-06","arxiv_id":"2408.02970","n_code_links":1,"syntology":null},{"paper":"/paper/2408-02976","slug":"2408-02976","title":"Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation","date":"2024-08-06","arxiv_id":"2408.02976","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-03030","title":"Nighttime Pedestrian Detection Based on Fore-Background Contrast Learning","date":"2024-08-06","arxiv_id":"2408.03030","n_code_links":0,"syntology":null},{"paper":"/paper/2408-03035","slug":"2408-03035","title":"Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram Synthesis","date":"2024-08-06","arxiv_id":"2408.03035","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-03062","title":"Analysis of Argument Structure Constructions in a Deep Recurrent Language Model","date":"2024-08-06","arxiv_id":"2408.03062","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03065","title":"SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions","date":"2024-08-06","arxiv_id":"2408.03065","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03088","title":"QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction","date":"2024-08-06","arxiv_id":"2408.03088","n_code_links":0,"syntology":null},{"paper":"/paper/2408-03099","slug":"2408-03099","title":"Topic Modeling with Fine-tuning LLMs and Bag of Sentences","date":"2024-08-06","arxiv_id":"2408.03099","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["johntailor/ft-topic"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"2408-03119","title":"Evaluating the Translation Performance of Large Language Models Based on Euas-20","date":"2024-08-06","arxiv_id":"2408.03119","n_code_links":0,"syntology":null},{"paper":"/paper/2408-03149","slug":"2408-03149","title":"Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization","date":"2024-08-06","arxiv_id":"2408.03149","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-03156","title":"Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models","date":"2024-08-06","arxiv_id":"2408.03156","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03172","title":"Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi","date":"2024-08-06","arxiv_id":"2408.03172","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03194","title":"SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention","date":"2024-08-06","arxiv_id":"2408.03194","n_code_links":0,"syntology":null},{"paper":"/paper/2408-03219","slug":"2408-03219","title":"Learning to Learn without Forgetting using Attention","date":"2024-08-06","arxiv_id":"2408.03219","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-03238","title":"LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion","date":"2024-08-06","arxiv_id":"2408.03238","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03284","title":"ReSyncer: Rewiring Style-based Generator for Unified Audio-Visually Synced Facial Performer","date":"2024-08-06","arxiv_id":"2408.03284","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03291","title":"DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers","date":"2024-08-06","arxiv_id":"2408.03291","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-03312","title":"MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation","date":"2024-08-06","arxiv_id":"2408.03312","n_code_links":0,"syntology":null},{"paper":"/paper/advancing-eeg-based-gaze-prediction-using","slug":"advancing-eeg-based-gaze-prediction-using","title":"Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing","date":"2024-08-06","arxiv_id":"2408.03480","n_code_links":1,"syntology":null},{"paper":null,"slug":"can-llms-serve-as-time-series-anomaly","title":"Can LLMs Serve As Time Series Anomaly Detectors?","date":"2024-08-06","arxiv_id":"2408.03475","n_code_links":0,"syntology":null},{"paper":null,"slug":"fda-jamming-against-airborne-phased-mimo-1","title":"FDA Jamming Against Airborne Phased-MIMO Radar-Part I: Matched Filtering and Spatial Filtering","date":"2024-08-06","arxiv_id":"2408.03050","n_code_links":0,"syntology":null},{"paper":null,"slug":"flash-federated-learning-based-llms-for","title":"FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG","date":"2024-08-06","arxiv_id":"2408.05242","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-aided-compilation-for-tensor-accelerators","title":"LLM-Aided Compilation for Tensor Accelerators","date":"2024-08-06","arxiv_id":"2408.03408","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-based-mofs-synthesis-condition-extraction","title":"LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations","date":"2024-08-06","arxiv_id":"2408.04665","n_code_links":0,"syntology":null},{"paper":null,"slug":"post-mortem-human-iris-segmentation-analysis","title":"Post-Mortem Human Iris Segmentation Analysis with Deep Learning","date":"2024-08-06","arxiv_id":"2408.03448","n_code_links":0,"syntology":null},{"paper":null,"slug":"raygauss-volumetric-gaussian-based-ray","title":"RayGauss: Volumetric Gaussian-Based Ray Casting for Photorealistic Novel View Synthesis","date":"2024-08-06","arxiv_id":"2408.03356","n_code_links":0,"syntology":null},{"paper":null,"slug":"set2seq-transformer-learning-permutation","title":"Set2Seq Transformer: Learning Permutation Aware Set Representations of Artistic Sequences","date":"2024-08-06","arxiv_id":"2408.03404","n_code_links":0,"syntology":null},{"paper":null,"slug":"static-ir-drop-prediction-with-attention-u","title":"Static IR Drop Prediction with Attention U-Net and Saliency-Based Explainability","date":"2024-08-06","arxiv_id":"2408.03292","n_code_links":0,"syntology":null},{"paper":"/paper/tf-locoformer-transformer-with-local-modeling","slug":"tf-locoformer-transformer-with-local-modeling","title":"TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement","date":"2024-08-06","arxiv_id":"2408.03440","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-use-of-large-language-models-llm-for","title":"The Use of Large Language Models (LLM) for Cyber Threat Intelligence (CTI) in Cybercrime Forums","date":"2024-08-06","arxiv_id":"2408.03354","n_code_links":0,"syntology":null},{"paper":"/paper/trafficgpt-an-llm-approach-for-open-set","slug":"trafficgpt-an-llm-approach-for-open-set","title":"TrafficGPT: An LLM Approach for Open-Set Encrypted Traffic Classification","date":"2024-08-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/training-llms-to-recognize-hedges-in","slug":"training-llms-to-recognize-hedges-in","title":"Training LLMs to Recognize Hedges in Spontaneous Narratives","date":"2024-08-06","arxiv_id":"2408.03319","n_code_links":1,"syntology":null},{"paper":"/paper/ullme-a-unified-framework-for-large-language","slug":"ullme-a-unified-framework-for-large-language","title":"ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning","date":"2024-08-06","arxiv_id":"2408.03402","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":["nlp-uoregon/ullme"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/2408-02181","slug":"2408-02181","title":"AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines","date":"2024-08-05","arxiv_id":"2408.02181","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02213","title":"Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation","date":"2024-08-05","arxiv_id":"2408.02213","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02222","slug":"2408-02222","title":"Cross-modulated Attention Transformer for RGBT Tracking","date":"2024-08-05","arxiv_id":"2408.02222","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02237","title":"Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings","date":"2024-08-05","arxiv_id":"2408.02237","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02272","slug":"2408-02272","title":"COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language Benchmark","date":"2024-08-05","arxiv_id":"2408.02272","n_code_links":1,"syntology":null},{"paper":"/paper/2408-02279","slug":"2408-02279","title":"DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting","date":"2024-08-05","arxiv_id":"2408.02279","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02288","title":"Spin glass model of in-context learning","date":"2024-08-05","arxiv_id":"2408.02288","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02369","slug":"2408-02369","title":"The NPU-ASLP System Description for Visual Speech Recognition in CNVSRC 2024","date":"2024-08-05","arxiv_id":"2408.02369","n_code_links":1,"syntology":null},{"paper":"/paper/2408-02416","slug":"2408-02416","title":"Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models","date":"2024-08-05","arxiv_id":"2408.02416","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02456","title":"Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based Approach","date":"2024-08-05","arxiv_id":"2408.02456","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02462","title":"An investigation into the causes of race bias in AI-based cine CMR segmentation","date":"2024-08-05","arxiv_id":"2408.02462","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02494","title":"HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions","date":"2024-08-05","arxiv_id":"2408.02494","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02545","slug":"2408-02545","title":"RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation","date":"2024-08-05","arxiv_id":"2408.02545","n_code_links":2,"syntology":null},{"paper":"/paper/2408-02654","slug":"2408-02654","title":"On Using Quasirandom Sequences in Machine Learning for Model Weight Initialization","date":"2024-08-05","arxiv_id":"2408.02654","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02705","title":"PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding","date":"2024-08-05","arxiv_id":"2408.02705","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02751","title":"A Novel Hybrid Approach for Tornado Prediction in the United States: Kalman-Convolutional BiLSTM with Multi-Head Attention","date":"2024-08-05","arxiv_id":"2408.02751","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02761","slug":"2408-02761","title":"Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation","date":"2024-08-05","arxiv_id":"2408.02761","n_code_links":1,"syntology":null},{"paper":"/paper/2408-02769","slug":"2408-02769","title":"From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation","date":"2024-08-05","arxiv_id":"2408.02769","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02780","title":"LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection","date":"2024-08-05","arxiv_id":"2408.02780","n_code_links":0,"syntology":null},{"paper":null,"slug":"2408-02788","title":"GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths","date":"2024-08-05","arxiv_id":"2408.02788","n_code_links":0,"syntology":null},{"paper":"/paper/2408-02845","slug":"2408-02845","title":"Heterogeneous graph attention network improves cancer multiomics integration","date":"2024-08-05","arxiv_id":"2408.02845","n_code_links":1,"syntology":null},{"paper":null,"slug":"2408-02854","title":"Wiping out the limitations of Large Language Models -- A Taxonomy for Retrieval Augmented Generation","date":"2024-08-05","arxiv_id":"2408.02854","n_code_links":0,"syntology":null},{"paper":null,"slug":"appagent-v2-advanced-agent-for-flexible","title":"AppAgent v2: Advanced Agent for Flexible Mobile Interactions","date":"2024-08-05","arxiv_id":"2408.11824","n_code_links":0,"syntology":null},{"paper":null,"slug":"llm-agents-improve-semantic-code-search","title":"LLM Agents Improve Semantic Code Search","date":"2024-08-05","arxiv_id":"2408.11058","n_code_links":0,"syntology":null},{"paper":null,"slug":"modelling-visual-semantics-via-image","title":"Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection","date":"2024-08-05","arxiv_id":"2408.02595","n_code_links":0,"syntology":null},{"paper":"/paper/seas-self-evolving-adversarial-safety","slug":"seas-self-evolving-adversarial-safety","title":"SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models","date":"2024-08-05","arxiv_id":"2408.02632","n_code_links":1,"syntology":null},{"paper":null,"slug":"self-taught-evaluators","title":"Self-Taught Evaluators","date":"2024-08-05","arxiv_id":"2408.02666","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-mechanics-of-conceptual-interpretation-in","title":"The Mechanics of Conceptual Interpretation in GPT Models: Interpretative Insights","date":"2024-08-05","arxiv_id":"2408.11827","n_code_links":0,"syntology":null}],"record_sha256":"a66307d61f553820d3af9422fa807a594a289dc8e935eb53fe985ee37f33ef32","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}