{"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/132","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":132,"pages_in_order":375,"rows_per_page":100,"rows":[13101,13200],"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/131","next":"/method/softmax/papers/133","papers":[{"paper":null,"slug":"a-simple-attention-based-mechanism-for","title":"A Simple Attention-Based Mechanism for Bimodal Emotion Classification","date":"2024-06-28","arxiv_id":"2407.00134","n_code_links":0,"syntology":null},{"paper":"/paper/anomallmy-detecting-anomalous-tokens-in-black","slug":"anomallmy-detecting-anomalous-tokens-in-black","title":"AnomaLLMy -- Detecting anomalous tokens in black-box LLMs through low-confidence single-token predictions","date":"2024-06-28","arxiv_id":"2406.19840","n_code_links":1,"syntology":null},{"paper":null,"slug":"applying-rlaif-for-code-generation-with-api","title":"Applying RLAIF for Code Generation with API-usage in Lightweight LLMs","date":"2024-06-28","arxiv_id":"2406.20060","n_code_links":0,"syntology":null},{"paper":"/paper/astmatch-adversarial-self-training","slug":"astmatch-adversarial-self-training","title":"AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation","date":"2024-06-28","arxiv_id":"2406.19649","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-meets-uavs-a-comprehensive","title":"Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs","date":"2024-06-28","arxiv_id":"2406.19881","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-first-order-a-multi-scale-approach-to","title":"Beyond First-Order: A Multi-Scale Approach to Finger Knuckle Print Biometrics","date":"2024-06-28","arxiv_id":"2406.19672","n_code_links":0,"syntology":null},{"paper":null,"slug":"biomner-a-dataset-for-biomedical-method","title":"BioMNER: A Dataset for Biomedical Method Entity Recognition","date":"2024-06-28","arxiv_id":"2406.20038","n_code_links":0,"syntology":null},{"paper":null,"slug":"bmw-agents-a-framework-for-task-automation","title":"BMW Agents -- A Framework For Task Automation Through Multi-Agent Collaboration","date":"2024-06-28","arxiv_id":"2406.20041","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-gpt-4-help-detect-quit-vaping-intentions","title":"Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach","date":"2024-06-28","arxiv_id":"2407.00167","n_code_links":0,"syntology":null},{"paper":null,"slug":"composite-adaptive-disturbance-rejection-in","title":"Composite Adaptive Disturbance Rejection in Robotics via Instrumental Variables based DREM","date":"2024-06-28","arxiv_id":"2406.19838","n_code_links":0,"syntology":null},{"paper":null,"slug":"covert-malicious-finetuning-challenges-in","title":"Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation","date":"2024-06-28","arxiv_id":"2406.20053","n_code_links":0,"syntology":null},{"paper":null,"slug":"directly-training-temporal-spiking-neural","title":"Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient","date":"2024-06-28","arxiv_id":"2406.19645","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-human-alignment-and-model","title":"Evaluating Human Alignment and Model Faithfulness of LLM Rationale","date":"2024-06-28","arxiv_id":"2407.00219","n_code_links":0,"syntology":null},{"paper":"/paper/evf-sam-early-vision-language-fusion-for-text","slug":"evf-sam-early-vision-language-fusion-for-text","title":"EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model","date":"2024-06-28","arxiv_id":"2406.20076","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["hustvl/evf-sam"],"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","unlocated"]}}},{"paper":null,"slug":"explainable-image-captioning-using-cnn-cnn","title":"Explainable Image Captioning using CNN- CNN architecture and Hierarchical Attention","date":"2024-06-28","arxiv_id":"2407.09556","n_code_links":0,"syntology":null},{"paper":"/paper/explore-as-a-storm-exploit-as-a-raindrop-on","slug":"explore-as-a-storm-exploit-as-a-raindrop-on","title":"Explore as a Storm, Exploit as a Raindrop: On the Benefit of Fine-Tuning Kernel Schedulers with Coordinate Descent","date":"2024-06-28","arxiv_id":"2406.20037","n_code_links":1,"syntology":null},{"paper":"/paper/finite-basis-kolmogorov-arnold-networks","slug":"finite-basis-kolmogorov-arnold-networks","title":"Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems","date":"2024-06-28","arxiv_id":"2406.19662","n_code_links":1,"syntology":null},{"paper":null,"slug":"footbots-a-transformer-based-architecture-for","title":"FootBots: A Transformer-based Architecture for Motion Prediction in Soccer","date":"2024-06-28","arxiv_id":"2406.19852","n_code_links":0,"syntology":null},{"paper":null,"slug":"fred-flexible-reduction-distribution","title":"FRED: Flexible REduction-Distribution Interconnect and Communication Implementation for Wafer-Scale Distributed Training of DNN Models","date":"2024-06-28","arxiv_id":"2406.19580","n_code_links":0,"syntology":null},{"paper":"/paper/fuzzy-logic-guided-reward-function-variation","slug":"fuzzy-logic-guided-reward-function-variation","title":"Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs","date":"2024-06-28","arxiv_id":"2406.19812","n_code_links":1,"syntology":null},{"paper":"/paper/generative-iris-prior-embedded-transformer","slug":"generative-iris-prior-embedded-transformer","title":"Generative Iris Prior Embedded Transformer for Iris Restoration","date":"2024-06-28","arxiv_id":"2407.00261","n_code_links":1,"syntology":null},{"paper":null,"slug":"housecrafter-lifting-floorplans-to-3d-scenes","title":"HouseCrafter: Lifting Floorplans to 3D Scenes with 2D Diffusion Model","date":"2024-06-28","arxiv_id":"2406.20077","n_code_links":0,"syntology":null},{"paper":"/paper/infinigen-efficient-generative-inference-of","slug":"infinigen-efficient-generative-inference-of","title":"InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management","date":"2024-06-28","arxiv_id":"2406.19707","n_code_links":1,"syntology":{"ran":14,"of":15,"n_ran_checked":10,"n_instrument":4,"unverified":1,"pointer_only":1,"phrase":"14 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; 4 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"joint-beamforming-and-antenna-position","title":"Joint Beamforming and Antenna Position Optimization for Movable Antenna-Assisted Spectrum Sharing","date":"2024-06-28","arxiv_id":"2406.19590","n_code_links":0,"syntology":null},{"paper":null,"slug":"less-is-more-accurate-speech-recognition","title":"Less is More: Accurate Speech Recognition & Translation without Web-Scale Data","date":"2024-06-28","arxiv_id":"2406.19674","n_code_links":0,"syntology":null},{"paper":"/paper/machine-learning-predictors-for-min-entropy","slug":"machine-learning-predictors-for-min-entropy","title":"Machine Learning Predictors for Min-Entropy Estimation","date":"2024-06-28","arxiv_id":"2406.19983","n_code_links":1,"syntology":null},{"paper":null,"slug":"mind-the-gap-analyzing-lacunae-with","title":"Mind the Gap: Analyzing Lacunae with Transformer-Based Transcription","date":"2024-06-28","arxiv_id":"2407.00250","n_code_links":0,"syntology":null},{"paper":"/paper/mixture-of-in-context-experts-enhance-llms","slug":"mixture-of-in-context-experts-enhance-llms","title":"Mixture of In-Context Experts Enhance LLMs' Long Context Awareness","date":"2024-06-28","arxiv_id":"2406.19598","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["p1nksnow/moice"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"modeling-the-real-world-with-high-density","title":"Modeling the Real World with High-Density Visual Particle Dynamics","date":"2024-06-28","arxiv_id":"2406.19800","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-satellite-mimo-systems-for-direct-user","title":"Multi-Satellite MIMO Systems for Direct User-Satellite Communications: A Survey","date":"2024-06-28","arxiv_id":"2407.00196","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-prototyping-for-cancer-survival","slug":"multimodal-prototyping-for-cancer-survival","title":"Multimodal Prototyping for cancer survival prediction","date":"2024-06-28","arxiv_id":"2407.00224","n_code_links":1,"syntology":{"ran":7,"of":12,"n_ran_checked":4,"n_instrument":3,"unverified":5,"pointer_only":12,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","official":{"repos":["mahmoodlab/MMP"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/pathgen-1-6m-1-6-million-pathology-image-text","slug":"pathgen-1-6m-1-6-million-pathology-image-text","title":"PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration","date":"2024-06-28","arxiv_id":"2407.00203","n_code_links":1,"syntology":null},{"paper":null,"slug":"poliformer-scaling-on-policy-rl-with","title":"PoliFormer: Scaling On-Policy RL with Transformers Results in Masterful Navigators","date":"2024-06-28","arxiv_id":"2406.20083","n_code_links":0,"syntology":null},{"paper":null,"slug":"pptformer-pseudo-multi-perspective","title":"PPTFormer: Pseudo Multi-Perspective Transformer for UAV Segmentation","date":"2024-06-28","arxiv_id":"2406.19632","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalebio-scalable-bilevel-optimization-for","title":"ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting","date":"2024-06-28","arxiv_id":"2406.19976","n_code_links":0,"syntology":null},{"paper":"/paper/shortcutsbench-a-large-scale-real-world","slug":"shortcutsbench-a-large-scale-real-world","title":"ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agents","date":"2024-06-28","arxiv_id":"2407.00132","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":4,"n_instrument":2,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["eachsheep/shortcutsbench"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"sk-vqa-synthetic-knowledge-generation-at","title":"SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs","date":"2024-06-28","arxiv_id":"2406.19593","n_code_links":0,"syntology":null},{"paper":null,"slug":"smlt-mugc-small-medium-and-large-texts","title":"SMLT-MUGC: Small, Medium, and Large Texts -- Machine versus User-Generated Content Detection and Comparison","date":"2024-06-28","arxiv_id":"2407.12815","n_code_links":0,"syntology":null},{"paper":"/paper/solving-token-gradient-conflict-in-mixture-of","slug":"solving-token-gradient-conflict-in-mixture-of","title":"Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model","date":"2024-06-28","arxiv_id":"2406.19905","n_code_links":1,"syntology":null},{"paper":"/paper/the-computational-curse-of-big-data-for","slug":"the-computational-curse-of-big-data-for","title":"The Computational Curse of Big Data for Bayesian Additive Regression Trees: A Hitting Time Analysis","date":"2024-06-28","arxiv_id":"2406.19958","n_code_links":1,"syntology":null},{"paper":null,"slug":"uncertainty-quantification-in-large-language","title":"Uncertainty Quantification in Large Language Models Through Convex Hull Analysis","date":"2024-06-28","arxiv_id":"2406.19712","n_code_links":0,"syntology":null},{"paper":null,"slug":"vision-transformer-with-key-select-routing","title":"Vision Transformer with Key-select Routing Attention for Single Image Dehazing","date":"2024-06-28","arxiv_id":"2406.19703","n_code_links":0,"syntology":null},{"paper":"/paper/a-sanity-check-for-ai-generated-image","slug":"a-sanity-check-for-ai-generated-image","title":"A Sanity Check for AI-generated Image Detection","date":"2024-06-27","arxiv_id":"2406.19435","n_code_links":2,"syntology":{"ran":8,"of":8,"n_ran_checked":6,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"8 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["shilinyan99/aide"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"aligning-teacher-with-student-preferences-for","title":"Aligning Teacher with Student Preferences for Tailored Training Data Generation","date":"2024-06-27","arxiv_id":"2406.19227","n_code_links":0,"syntology":null},{"paper":"/paper/an-interpretable-and-efficient-sleep-staging","slug":"an-interpretable-and-efficient-sleep-staging","title":"An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNet","date":"2024-06-27","arxiv_id":"2406.19246","n_code_links":1,"syntology":null},{"paper":"/paper/automated-web-based-malaria-detection-system","slug":"automated-web-based-malaria-detection-system","title":"Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques: A Comparative Analysis","date":"2024-06-27","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/automated-web-based-malaria-detection-system-1","slug":"automated-web-based-malaria-detection-system-1","title":"Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques","date":"2024-06-27","arxiv_id":"2407.00120","n_code_links":1,"syntology":null},{"paper":"/paper/autopuredata-automated-filtering-of-web-data","slug":"autopuredata-automated-filtering-of-web-data","title":"AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM Knowledge","date":"2024-06-27","arxiv_id":"2406.19271","n_code_links":1,"syntology":null},{"paper":null,"slug":"autorag-hp-automatic-online-hyper-parameter","title":"AutoRAG-HP: Automatic Online Hyper-Parameter Tuning for Retrieval-Augmented Generation","date":"2024-06-27","arxiv_id":"2406.19251","n_code_links":0,"syntology":null},{"paper":"/paper/can-large-language-models-generate-high","slug":"can-large-language-models-generate-high","title":"Can Large Language Models Generate High-quality Patent Claims?","date":"2024-06-27","arxiv_id":"2406.19465","n_code_links":1,"syntology":null},{"paper":null,"slug":"cost-efficient-active-illumination-camera-for","title":"Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction","date":"2024-06-27","arxiv_id":"2406.19560","n_code_links":0,"syntology":null},{"paper":null,"slug":"diminishing-stereotype-bias-in-image","title":"Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback","date":"2024-06-27","arxiv_id":"2407.09551","n_code_links":0,"syntology":null},{"paper":null,"slug":"elcorec-enhance-language-understanding-with","title":"ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation","date":"2024-06-27","arxiv_id":"2406.18825","n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-video-language-representations-with","title":"Enhancing Video-Language Representations with Structural Spatio-Temporal Alignment","date":"2024-06-27","arxiv_id":"2406.19255","n_code_links":0,"syntology":null},{"paper":null,"slug":"fairness-and-bias-in-multimodal-ai-a-survey","title":"Fairness and Bias in Multimodal AI: A Survey","date":"2024-06-27","arxiv_id":"2406.19097","n_code_links":0,"syntology":null},{"paper":"/paper/fibottention-inceptive-visual-representation","slug":"fibottention-inceptive-visual-representation","title":"Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads","date":"2024-06-27","arxiv_id":"2406.19391","n_code_links":1,"syntology":null},{"paper":null,"slug":"fine-tuned-network-relies-on-generic","title":"Fine-tuned network relies on generic representation to solve unseen cognitive task","date":"2024-06-27","arxiv_id":"2406.18926","n_code_links":0,"syntology":null},{"paper":"/paper/from-artificial-needles-to-real-haystacks","slug":"from-artificial-needles-to-real-haystacks","title":"From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data","date":"2024-06-27","arxiv_id":"2406.19292","n_code_links":1,"syntology":null},{"paper":null,"slug":"granite-function-calling-model-introducing","title":"Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks","date":"2024-06-27","arxiv_id":"2407.00121","n_code_links":0,"syntology":null},{"paper":null,"slug":"historia-magistra-vitae-dynamic-topic","title":"Historia Magistra Vitae: Dynamic Topic Modeling of Roman Literature using Neural Embeddings","date":"2024-06-27","arxiv_id":"2406.18907","n_code_links":0,"syntology":null},{"paper":"/paper/human-aware-vision-and-language-navigation","slug":"human-aware-vision-and-language-navigation","title":"Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions","date":"2024-06-27","arxiv_id":"2406.19236","n_code_links":1,"syntology":{"ran":9,"of":10,"n_ran_checked":9,"n_instrument":0,"unverified":1,"pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["lpercc/ha3d_simulator"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"paper":"/paper/huwsod-holistic-self-training-for-unified","slug":"huwsod-holistic-self-training-for-unified","title":"HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection","date":"2024-06-27","arxiv_id":"2406.19394","n_code_links":1,"syntology":null},{"paper":null,"slug":"indotoxic2024-a-demographically-enriched","title":"IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language","date":"2024-06-27","arxiv_id":"2406.19349","n_code_links":0,"syntology":null},{"paper":"/paper/learning-retrieval-augmentation-for","slug":"learning-retrieval-augmentation-for","title":"Learning Retrieval Augmentation for Personalized Dialogue Generation","date":"2024-06-27","arxiv_id":"2406.18847","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":10,"n_instrument":1,"unverified":3,"pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["hqsiswiliam/lapdog"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"leveraging-contrastive-learning-for-enhanced","title":"Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers","date":"2024-06-27","arxiv_id":"2406.19258","n_code_links":0,"syntology":null},{"paper":"/paper/looking-3d-anomaly-detection-with-2d-3d-1","slug":"looking-3d-anomaly-detection-with-2d-3d-1","title":"Looking 3D: Anomaly Detection with 2D-3D Alignment","date":"2024-06-27","arxiv_id":"2406.19393","n_code_links":1,"syntology":null},{"paper":"/paper/mamba-or-rwkv-exploring-high-quality-and-high","slug":"mamba-or-rwkv-exploring-high-quality-and-high","title":"Mamba or RWKV: Exploring High-Quality and High-Efficiency Segment Anything Model","date":"2024-06-27","arxiv_id":"2406.19369","n_code_links":1,"syntology":null},{"paper":null,"slug":"ntformer-a-composite-node-tokenized-graph","title":"NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification","date":"2024-06-27","arxiv_id":"2406.19249","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-depression-and-anxiety-risk-in","title":"Predicting Depression and Anxiety Risk in Dutch Neighborhoods from Street-View Images","date":"2024-06-27","arxiv_id":"2407.09547","n_code_links":0,"syntology":null},{"paper":null,"slug":"raven-multitask-retrieval-augmented-vision","title":"RAVEN: Multitask Retrieval Augmented Vision-Language Learning","date":"2024-06-27","arxiv_id":"2406.19150","n_code_links":0,"syntology":null},{"paper":"/paper/retain-blend-and-exchange-a-quality-aware","slug":"retain-blend-and-exchange-a-quality-aware","title":"Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition","date":"2024-06-27","arxiv_id":"2406.18845","n_code_links":1,"syntology":null},{"paper":"/paper/seakr-self-aware-knowledge-retrieval-for","slug":"seakr-self-aware-knowledge-retrieval-for","title":"SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation","date":"2024-06-27","arxiv_id":"2406.19215","n_code_links":1,"syntology":{"ran":10,"of":12,"n_ran_checked":9,"n_instrument":1,"unverified":2,"pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["thu-keg/seakr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"seeing-is-believing-black-box-membership","title":"Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation","date":"2024-06-27","arxiv_id":"2406.19234","n_code_links":0,"syntology":null},{"paper":null,"slug":"segment-anything-model-for-automated-image","title":"Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO","date":"2024-06-27","arxiv_id":"2406.19057","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-concept-bottleneck-models","title":"Semi-supervised Concept Bottleneck Models","date":"2024-06-27","arxiv_id":"2406.18992","n_code_links":0,"syntology":null},{"paper":"/paper/simtxtseg-weakly-supervised-medical-image","slug":"simtxtseg-weakly-supervised-medical-image","title":"SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cues","date":"2024-06-27","arxiv_id":"2406.19364","n_code_links":1,"syntology":null},{"paper":null,"slug":"single-image-estimation-of-cell-migration","title":"Single Image Estimation of Cell Migration Direction by Deep Circular Regression","date":"2024-06-27","arxiv_id":"2406.19162","n_code_links":0,"syntology":null},{"paper":"/paper/sonnet-or-not-bot-poetry-evaluation-for-large","slug":"sonnet-or-not-bot-poetry-evaluation-for-large","title":"Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets","date":"2024-06-27","arxiv_id":"2406.18906","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["maria-antoniak/poetry-eval"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/statistical-test-for-data-analysis-pipeline","slug":"statistical-test-for-data-analysis-pipeline","title":"Statistical Test for Feature Selection Pipelines by Selective Inference","date":"2024-06-27","arxiv_id":"2406.18902","n_code_links":1,"syntology":null},{"paper":"/paper/structural-attention-rethinking-transformer","slug":"structural-attention-rethinking-transformer","title":"Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis","date":"2024-06-27","arxiv_id":"2406.18967","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-model-arena-for-cross-lingual-sentiment","title":"The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models","date":"2024-06-27","arxiv_id":"2406.19358","n_code_links":0,"syntology":null},{"paper":"/paper/think-step-by-step-chain-of-gesture-prompting","slug":"think-step-by-step-chain-of-gesture-prompting","title":"Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos","date":"2024-06-27","arxiv_id":"2406.19217","n_code_links":1,"syntology":null},{"paper":"/paper/unigen-a-unified-framework-for-textual","slug":"unigen-a-unified-framework-for-textual","title":"UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models","date":"2024-06-27","arxiv_id":"2406.18966","n_code_links":1,"syntology":{"ran":8,"of":13,"n_ran_checked":8,"n_instrument":0,"unverified":5,"pointer_only":13,"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) · 5 unverified","official":{"repos":["howiehwong/unigen"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/yzs-model-a-predictive-model-for-organic-drug","slug":"yzs-model-a-predictive-model-for-organic-drug","title":"YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention","date":"2024-06-27","arxiv_id":"2406.19136","n_code_links":1,"syntology":null},{"paper":null,"slug":"3d-mvp-3d-multiview-pretraining-for-robotic","title":"3D-MVP: 3D Multiview Pretraining for Robotic Manipulation","date":"2024-06-26","arxiv_id":"2406.18158","n_code_links":0,"syntology":null},{"paper":"/paper/a-closer-look-into-mixture-of-experts-in","slug":"a-closer-look-into-mixture-of-experts-in","title":"A Closer Look into Mixture-of-Experts in Large Language Models","date":"2024-06-26","arxiv_id":"2406.18219","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-multi-stage-goal-driven-network-for","title":"A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction","date":"2024-06-26","arxiv_id":"2406.18050","n_code_links":0,"syntology":null},{"paper":"/paper/a-stem-agnostic-single-decoder-system-for","slug":"a-stem-agnostic-single-decoder-system-for","title":"A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems","date":"2024-06-26","arxiv_id":"2406.18747","n_code_links":1,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["kwatcharasupat/query-bandit"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-survey-on-mixture-of-experts","slug":"a-survey-on-mixture-of-experts","title":"A Survey on Mixture of Experts","date":"2024-06-26","arxiv_id":"2407.06204","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarial-search-engine-optimization-for","title":"Adversarial Search Engine Optimization for Large Language Models","date":"2024-06-26","arxiv_id":"2406.18382","n_code_links":0,"syntology":null},{"paper":null,"slug":"apigen-automated-pipeline-for-generating","title":"APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets","date":"2024-06-26","arxiv_id":"2406.18518","n_code_links":0,"syntology":null},{"paper":"/paper/badge-badminton-report-generation-and","slug":"badge-badminton-report-generation-and","title":"BADGE: BADminton report Generation and Evaluation with LLM","date":"2024-06-26","arxiv_id":"2406.18116","n_code_links":1,"syntology":null},{"paper":null,"slug":"continuous-sign-language-recognition-using","title":"Continuous Sign Language Recognition Using Intra-inter Gloss Attention","date":"2024-06-26","arxiv_id":"2406.18333","n_code_links":0,"syntology":null},{"paper":"/paper/diffusehigh-training-free-progressive-high","slug":"diffusehigh-training-free-progressive-high","title":"DiffuseHigh: Training-free Progressive High-Resolution Image Synthesis through Structure Guidance","date":"2024-06-26","arxiv_id":"2406.18459","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yhyun225/DiffuseHigh"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/diffusion-model-based-video-editing-a-survey","slug":"diffusion-model-based-video-editing-a-survey","title":"Diffusion Model-Based Video Editing: A Survey","date":"2024-06-26","arxiv_id":"2407.07111","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, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["wenhao728/awesome-diffusion-v2v"],"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":"efcnet-every-feature-counts-for-small-medical","title":"EFCNet: Every Feature Counts for Small Medical Object Segmentation","date":"2024-06-26","arxiv_id":"2406.18201","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-quality-of-answers-for-retrieval","title":"Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need","date":"2024-06-26","arxiv_id":"2406.18064","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-energy-based-models-for-out-of","title":"Exploring Energy-Based Models for Out-of-Distribution Detection in Dialect Identification","date":"2024-06-26","arxiv_id":"2406.18067","n_code_links":0,"syntology":null},{"paper":"/paper/factfinders-at-checkthat-2024-refining-check","slug":"factfinders-at-checkthat-2024-refining-check","title":"FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning","date":"2024-06-26","arxiv_id":"2406.18297","n_code_links":1,"syntology":null},{"paper":null,"slug":"generative-artificial-intelligence-in-2","title":"Generative artificial intelligence in ophthalmology: multimodal retinal images for the diagnosis of Alzheimer's disease with convolutional neural networks","date":"2024-06-26","arxiv_id":"2406.18247","n_code_links":0,"syntology":null}],"record_sha256":"0c2ff9b743328564ec35fa5f22b90d933b0bdf70d2cab4509a56b9785408d83c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}