{"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/average-pooling/papers/3","list_of":"/method/average-pooling","method":"Average Pooling","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":3,"pages_in_order":52,"rows_per_page":100,"rows":[201,300],"of":5125,"counts":{"archive_papers_tagged":5125,"with_a_code_link":2243,"where_syntology_ran_a_sample":586,"not_listed_spam_title":0,"listed":5125,"listed_where_code_ran":586,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":489,"every_run_a_failure_of_syntologys_instrument":97,"listed_with_a_run_with_no_instrument_failure":489,"listed_every_run_a_failure_of_syntologys_instrument":97,"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/average-pooling","prev":"/method/average-pooling/papers/2","next":"/method/average-pooling/papers/4","papers":[{"paper":null,"slug":"hardware-aware-dnn-compression-for","title":"Hardware-Aware DNN Compression for Homogeneous Edge Devices","date":"2025-01-25","arxiv_id":"2501.15240","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-brownconrady-prediction-of-camera","title":"Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data","date":"2025-01-24","arxiv_id":"2501.14510","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-powered-classification-of","title":"Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays","date":"2025-01-24","arxiv_id":"2501.14279","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-classification-of-acute","title":"Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning","date":"2025-01-24","arxiv_id":"2501.14228","n_code_links":0,"syntology":null},{"paper":null,"slug":"relative-layer-wise-relevance-propagation-a","title":"Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination","date":"2025-01-24","arxiv_id":"2501.14322","n_code_links":0,"syntology":null},{"paper":"/paper/attribute-based-visual-reprogramming-for","slug":"attribute-based-visual-reprogramming-for","title":"Attribute-based Visual Reprogramming for Image Classification with CLIP","date":"2025-01-23","arxiv_id":"2501.13982","n_code_links":1,"syntology":null},{"paper":null,"slug":"one-cycle-structured-pruning-with-stability","title":"One-cycle Structured Pruning with Stability Driven Structure Search","date":"2025-01-23","arxiv_id":"2501.13439","n_code_links":0,"syntology":null},{"paper":"/paper/sparse-identification-of-nonlinear-dynamics-6","slug":"sparse-identification-of-nonlinear-dynamics-6","title":"Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks","date":"2025-01-23","arxiv_id":"2501.13329","n_code_links":1,"syntology":null},{"paper":null,"slug":"aggrotech-leveraging-deep-learning-for","title":"Aggrotech: Leveraging Deep Learning for Sustainable Tomato Disease Management","date":"2025-01-21","arxiv_id":"2501.12052","n_code_links":0,"syntology":null},{"paper":null,"slug":"robustness-of-selected-learning-models-under","title":"Robustness of Selected Learning Models under Label-Flipping Attack","date":"2025-01-21","arxiv_id":"2501.12516","n_code_links":0,"syntology":null},{"paper":"/paper/tackling-small-sample-survival-analysis-via","slug":"tackling-small-sample-survival-analysis-via","title":"Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis","date":"2025-01-21","arxiv_id":"2501.12421","n_code_links":1,"syntology":null},{"paper":"/paper/prediction-of-lung-metastasis-from","slug":"prediction-of-lung-metastasis-from","title":"Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database","date":"2025-01-20","arxiv_id":"2501.11720","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-brain-tumor-segmentation-using","slug":"enhancing-brain-tumor-segmentation-using","title":"Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning","date":"2025-01-19","arxiv_id":"2501.11196","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-multi-scale-feature-extraction-and-fusion","title":"A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases","date":"2025-01-17","arxiv_id":"2501.09938","n_code_links":0,"syntology":null},{"paper":null,"slug":"hypercam-low-power-onboard-computer-vision","title":"HyperCam: Low-Power Onboard Computer Vision for IoT Cameras","date":"2025-01-17","arxiv_id":"2501.10547","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-graph-mlp-mixer-for-spatio-temporal","slug":"temporal-graph-mlp-mixer-for-spatio-temporal","title":"Temporal Graph MLP Mixer for Spatio-Temporal Forecasting","date":"2025-01-17","arxiv_id":"2501.10214","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-distance-map-regression-network-with","title":"Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation","date":"2025-01-15","arxiv_id":"2501.09116","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-feature-level-ensemble-model-for-covid-19","title":"A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images using Choquet Integral and Differential Evolution Optimization","date":"2025-01-14","arxiv_id":"2501.08241","n_code_links":0,"syntology":null},{"paper":"/paper/ai-driven-water-segmentation-with-deep","slug":"ai-driven-water-segmentation-with-deep","title":"AI Driven Water Segmentation with deep learning models for Enhanced Flood Monitoring","date":"2025-01-14","arxiv_id":"2501.08266","n_code_links":1,"syntology":null},{"paper":null,"slug":"decoding-interpretable-logic-rules-from","title":"Decoding Interpretable Logic Rules from Neural Networks","date":"2025-01-14","arxiv_id":"2501.08281","n_code_links":0,"syntology":null},{"paper":null,"slug":"revolutionizing-communication-with-deep","title":"Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition","date":"2025-01-14","arxiv_id":"2501.08169","n_code_links":0,"syntology":null},{"paper":null,"slug":"threshold-attention-network-for-semantic","title":"Threshold Attention Network for Semantic Segmentation of Remote Sensing Images","date":"2025-01-14","arxiv_id":"2501.07984","n_code_links":0,"syntology":null},{"paper":"/paper/bigger-isn-t-always-better-towards-a-general","slug":"bigger-isn-t-always-better-towards-a-general","title":"Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction","date":"2025-01-13","arxiv_id":"2501.07376","n_code_links":1,"syntology":null},{"paper":"/paper/iot-based-real-time-medical-related-human","slug":"iot-based-real-time-medical-related-human","title":"IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare","date":"2025-01-13","arxiv_id":"2501.07039","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-label-scene-classification-in-remote","title":"Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution","date":"2025-01-12","arxiv_id":"2501.06720","n_code_links":0,"syntology":null},{"paper":null,"slug":"natural-language-processing-and-deep-learning","title":"Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences","date":"2025-01-11","arxiv_id":"2501.06564","n_code_links":0,"syntology":null},{"paper":"/paper/parking-space-detection-in-the-city-of","slug":"parking-space-detection-in-the-city-of","title":"Parking Space Detection in the City of Granada","date":"2025-01-11","arxiv_id":"2501.06651","n_code_links":1,"syntology":null},{"paper":null,"slug":"topoformer-integrating-transformers-and","title":"TopoFormer: Integrating Transformers and ConvLSTMs for Coastal Topography Prediction","date":"2025-01-11","arxiv_id":"2501.06494","n_code_links":0,"syntology":null},{"paper":"/paper/an-attention-guided-deep-learning-approach","slug":"an-attention-guided-deep-learning-approach","title":"An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types","date":"2025-01-10","arxiv_id":"2501.05991","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-ct-image-classification-network-framework","title":"A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field","date":"2025-01-09","arxiv_id":"2501.04996","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-ai-enhanced-deep-learning-for","title":"Explainable AI-Enhanced Deep Learning for Pumpkin Leaf Disease Detection: A Comparative Analysis of CNN Architectures","date":"2025-01-09","arxiv_id":"2501.05449","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-neural-models-for-x-ray-image","title":"Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection","date":"2025-01-08","arxiv_id":"2501.04196","n_code_links":0,"syntology":null},{"paper":null,"slug":"planarian-neural-networks-evolutionary","title":"Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria Shaping Modern Artificial Neural Network Architectures","date":"2025-01-08","arxiv_id":"2501.04700","n_code_links":0,"syntology":null},{"paper":null,"slug":"radar-signal-recognition-through-self","title":"Radar Signal Recognition through Self-Supervised Learning and Domain Adaptation","date":"2025-01-07","arxiv_id":"2501.03461","n_code_links":0,"syntology":null},{"paper":null,"slug":"codevision-detecting-llm-generated-code-using","title":"CodeVision: Detecting LLM-Generated Code Using 2D Token Probability Maps and Vision Models","date":"2025-01-06","arxiv_id":"2501.03288","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-forward-forward-algorithm","title":"Scalable Forward-Forward Algorithm","date":"2025-01-06","arxiv_id":"2501.03176","n_code_links":0,"syntology":null},{"paper":null,"slug":"pteenet-post-trained-early-exit-neural","title":"PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization","date":"2025-01-05","arxiv_id":"2501.02508","n_code_links":0,"syntology":null},{"paper":null,"slug":"ai-powered-cow-detection-in-complex-farm","title":"AI-Powered Cow Detection in Complex Farm Environments","date":"2025-01-03","arxiv_id":"2501.02080","n_code_links":0,"syntology":null},{"paper":null,"slug":"fundamental-mmse-rate-performance-limits-of","title":"Fundamental MMSE-Rate Performance Limits of Integrated Sensing and Communication Systems","date":"2025-01-02","arxiv_id":"2501.01053","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-head-explainer-a-general-framework-to","title":"Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers","date":"2025-01-02","arxiv_id":"2501.01311","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-focused-human-body-model-for-accurate","title":"A Focused Human Body Model for Accurate Anthropometric Measurements Extraction","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/foreground-covering-prototype-generation-and","slug":"foreground-covering-prototype-generation-and","title":"Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation","date":"2025-01-01","arxiv_id":"2501.00752","n_code_links":1,"syntology":null},{"paper":null,"slug":"pleas-merging-models-with-permutations-and-1","title":"PLeaS - Merging Models with Permutations and Least Squares","date":"2025-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-shape-guided-transformer-network-for","title":"A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images","date":"2024-12-31","arxiv_id":"2501.00360","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-vehicle-detection-based-on","title":"Research on vehicle detection based on improved YOLOv8 network","date":"2024-12-31","arxiv_id":"2501.00300","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-2d-and-3d-resnet","title":"Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients","date":"2024-12-30","arxiv_id":"2412.21091","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-acoustic-scene-classification-in","title":"Improving Acoustic Scene Classification in Low-Resource Conditions","date":"2024-12-30","arxiv_id":"2412.20722","n_code_links":0,"syntology":null},{"paper":null,"slug":"residual-connection-networks-in-medical-image","title":"Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction","date":"2024-12-30","arxiv_id":"2412.20709","n_code_links":0,"syntology":null},{"paper":"/paper/sample-correlation-for-fingerprinting-deep","slug":"sample-correlation-for-fingerprinting-deep","title":"Sample Correlation for Fingerprinting Deep Face Recognition","date":"2024-12-30","arxiv_id":"2412.20768","n_code_links":2,"syntology":null},{"paper":null,"slug":"enhancing-transfer-learning-for-medical-image","title":"Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study","date":"2024-12-28","arxiv_id":"2412.20235","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-ideal-temporal-graph-neural-networks","title":"Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours","date":"2024-12-28","arxiv_id":"2412.20256","n_code_links":0,"syntology":null},{"paper":null,"slug":"brain-ageing-prediction-using-isolation","title":"Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)","date":"2024-12-26","arxiv_id":"2412.19017","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluating-convolutional-neural-networks-for","title":"Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images","date":"2024-12-26","arxiv_id":"2412.19362","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-the-network-traffic-classification","title":"Improving the network traffic classification using the Packet Vision approach","date":"2024-12-26","arxiv_id":"2412.19360","n_code_links":0,"syntology":null},{"paper":"/paper/mgan-crcm-a-novel-multiple-generative","slug":"mgan-crcm-a-novel-multiple-generative","title":"MGAN-CRCM: A Novel Multiple Generative Adversarial Network and Coarse-Refinement Based Cognizant Method for Image Inpainting","date":"2024-12-25","arxiv_id":"2412.19000","n_code_links":1,"syntology":null},{"paper":null,"slug":"autosculpt-a-pattern-based-model-auto-pruning","title":"AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning","date":"2024-12-24","arxiv_id":"2412.18091","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-intrinsically-explainable-approach-to","title":"An Intrinsically Explainable Approach to Detecting Vertebral Compression Fractures in CT Scans via Neurosymbolic Modeling","date":"2024-12-23","arxiv_id":"2412.17258","n_code_links":0,"syntology":null},{"paper":null,"slug":"collaborative-optimization-in-financial-data","title":"Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt","date":"2024-12-23","arxiv_id":"2412.17314","n_code_links":0,"syntology":null},{"paper":"/paper/layerdropback-a-universally-applicable","slug":"layerdropback-a-universally-applicable","title":"LayerDropBack: A Universally Applicable Approach for Accelerating Training of Deep Networks","date":"2024-12-23","arxiv_id":"2412.18027","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-classification-of-high-frequency","title":"Multi-classification of High-Frequency Oscillations Using iEEG Signals and Deep Learning Models","date":"2024-12-22","arxiv_id":"2412.17145","n_code_links":0,"syntology":null},{"paper":"/paper/wpmixer-efficient-multi-resolution-mixing-for","slug":"wpmixer-efficient-multi-resolution-mixing-for","title":"WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting","date":"2024-12-22","arxiv_id":"2412.17176","n_code_links":1,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Secure-and-Intelligent-Systems-Lab/WPMixer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/enhancing-contrastive-learning-inspired-by","slug":"enhancing-contrastive-learning-inspired-by","title":"Enhancing Contrastive Learning Inspired by the Philosophy of \"The Blind Men and the Elephant\"","date":"2024-12-21","arxiv_id":"2412.16522","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-detection-approaches-to-identifying","title":"Object Detection Approaches to Identifying Hand Images with High Forensic Values","date":"2024-12-21","arxiv_id":"2412.16431","n_code_links":0,"syntology":null},{"paper":null,"slug":"sensitive-image-classification-by-vision","title":"Sensitive Image Classification by Vision Transformers","date":"2024-12-21","arxiv_id":"2412.16446","n_code_links":0,"syntology":null},{"paper":null,"slug":"seagrassfinder-deep-learning-for-eelgrass","title":"SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild","date":"2024-12-20","arxiv_id":"2412.16147","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-radiograph-anatomical-region","title":"Self-Supervised Radiograph Anatomical Region Classification -- How Clean Is Your Real-World Data?","date":"2024-12-20","arxiv_id":"2412.15967","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploring-machine-learning-engineering-for","title":"Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)","date":"2024-12-19","arxiv_id":"2412.15347","n_code_links":0,"syntology":null},{"paper":null,"slug":"maximising-histopathology-segmentation-using","title":"Maximising Histopathology Segmentation using Minimal Labels via Self-Supervision","date":"2024-12-19","arxiv_id":"2412.15389","n_code_links":0,"syntology":null},{"paper":null,"slug":"mitraclip-device-automated-localization-in-3d","title":"MitraClip Device Automated Localization in 3D Transesophageal Echocardiography via Deep Learning","date":"2024-12-19","arxiv_id":"2412.15013","n_code_links":0,"syntology":null},{"paper":null,"slug":"fruit-deformity-classification-through-single","title":"Fruit Deformity Classification through Single-Input and Multi-Input Architectures based on CNN Models using Real and Synthetic Images","date":"2024-12-17","arxiv_id":"2412.12966","n_code_links":0,"syntology":null},{"paper":"/paper/explicit-and-implicit-graduated-optimization","slug":"explicit-and-implicit-graduated-optimization","title":"Explicit and Implicit Graduated Optimization in Deep Neural Networks","date":"2024-12-16","arxiv_id":"2412.11501","n_code_links":1,"syntology":null},{"paper":null,"slug":"samic-segment-anything-with-in-context","title":"SAMIC: Segment Anything with In-Context Spatial Prompt Engineering","date":"2024-12-16","arxiv_id":"2412.11998","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-estimation-of-subsurface-eddy-kinetic","title":"Global Estimation of Subsurface Eddy Kinetic Energy of Mesoscale Eddies Using a Multiple-input Residual Neural Network","date":"2024-12-14","arxiv_id":"2412.10656","n_code_links":0,"syntology":null},{"paper":null,"slug":"understand-the-effectiveness-of-shortcuts","title":"Understand the Effectiveness of Shortcuts through the Lens of DCA","date":"2024-12-13","arxiv_id":"2412.09853","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-ensemble-based-deep-learning-model","title":"A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis","date":"2024-12-12","arxiv_id":"2412.09472","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-efficient-framework-for-enhancing","title":"An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques","date":"2024-12-12","arxiv_id":"2412.09063","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-detection-positioning-and-counting","slug":"automatic-detection-positioning-and-counting","title":"Automatic Detection, Positioning and Counting of Grape Bunches Using Robots","date":"2024-12-12","arxiv_id":"2412.10464","n_code_links":1,"syntology":null},{"paper":null,"slug":"embeddings-are-all-you-need-achieving-high","title":"Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis","date":"2024-12-12","arxiv_id":"2412.09445","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-stage-segmentation-and-cascade","title":"Multi-Stage Segmentation and Cascade Classification Methods for Improving Cardiac MRI Analysis","date":"2024-12-12","arxiv_id":"2412.09386","n_code_links":0,"syntology":null},{"paper":null,"slug":"stochastic-learning-of-non-conjugate","title":"Stochastic Learning of Non-Conjugate Variational Posterior for Image Classification","date":"2024-12-12","arxiv_id":"2412.08951","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-gravitational-wave-parameter","title":"Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach","date":"2024-12-11","arxiv_id":"2412.08672","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multimodal-ensemble-approach-for-clear-cell","title":"A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction","date":"2024-12-10","arxiv_id":"2412.07136","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-epsilon-adversarial-training-for","title":"Adaptive Epsilon Adversarial Training for Robust Gravitational Wave Parameter Estimation Using Normalizing Flows","date":"2024-12-10","arxiv_id":"2412.07559","n_code_links":0,"syntology":null},{"paper":"/paper/amclr-unified-augmented-learning-for-cross","slug":"amclr-unified-augmented-learning-for-cross","title":"AmCLR: Unified Augmented Learning for Cross-Modal Representations","date":"2024-12-10","arxiv_id":"2412.07979","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparative-analysis-of-deep-learning-4","title":"Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG","date":"2024-12-10","arxiv_id":"2412.07878","n_code_links":0,"syntology":null},{"paper":null,"slug":"dense-cross-connected-ensemble-convolutional","title":"Dense Cross-Connected Ensemble Convolutional Neural Networks for Enhanced Model Robustness","date":"2024-12-09","arxiv_id":"2412.07022","n_code_links":0,"syntology":null},{"paper":"/paper/hybrid-attention-network-an-efficient","slug":"hybrid-attention-network-an-efficient","title":"HYATT-Net is Grand: A Hybrid Attention Network for Performant Anatomical Landmark Detection","date":"2024-12-09","arxiv_id":"2412.06499","n_code_links":1,"syntology":null},{"paper":null,"slug":"impact-of-privacy-parameters-on-deep-learning","title":"Impact of Privacy Parameters on Deep Learning Models for Image Classification","date":"2024-12-09","arxiv_id":"2412.06689","n_code_links":0,"syntology":null},{"paper":null,"slug":"policy-agnostic-rl-offline-rl-and-online-rl","title":"Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone","date":"2024-12-09","arxiv_id":"2412.06685","n_code_links":0,"syntology":null},{"paper":null,"slug":"thermal-image-based-fault-diagnosis-in","title":"Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks","date":"2024-12-08","arxiv_id":"2412.05901","n_code_links":0,"syntology":null},{"paper":"/paper/gaf-fusionnet-multimodal-ecg-analysis-via","slug":"gaf-fusionnet-multimodal-ecg-analysis-via","title":"GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split Attention","date":"2024-12-07","arxiv_id":"2501.01960","n_code_links":1,"syntology":null},{"paper":null,"slug":"integrating-yolo11-and-convolution-block","title":"Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards","date":"2024-12-07","arxiv_id":"2412.05728","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-boxes-mask-guided-spatio-temporal","title":"Beyond Boxes: Mask-Guided Spatio-Temporal Feature Aggregation for Video Object Detection","date":"2024-12-06","arxiv_id":"2412.04915","n_code_links":0,"syntology":null},{"paper":"/paper/colonnet-a-hybrid-of-densenet121-and-u-net","slug":"colonnet-a-hybrid-of-densenet121-and-u-net","title":"ColonNet: A Hybrid Of DenseNet121 And U-NET Model For Detection And Segmentation Of GI Bleeding","date":"2024-12-06","arxiv_id":"2412.05216","n_code_links":1,"syntology":null},{"paper":"/paper/machine-learning-based-mmwave-mimo-beam","slug":"machine-learning-based-mmwave-mimo-beam","title":"Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: Algorithms and Datasets","date":"2024-12-06","arxiv_id":"2412.05427","n_code_links":1,"syntology":null},{"paper":null,"slug":"mitigating-instance-dependent-label-noise","title":"Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement","date":"2024-12-06","arxiv_id":"2412.04898","n_code_links":0,"syntology":null},{"paper":null,"slug":"multimodal-sentiment-analysis-based-on-bert","title":"Multimodal Sentiment Analysis Based on BERT and ResNet","date":"2024-12-04","arxiv_id":"2412.03625","n_code_links":0,"syntology":null},{"paper":"/paper/tight-pac-bayesian-risk-certificates-for","slug":"tight-pac-bayesian-risk-certificates-for","title":"Tight PAC-Bayesian Risk Certificates for Contrastive Learning","date":"2024-12-04","arxiv_id":"2412.03486","n_code_links":1,"syntology":null},{"paper":null,"slug":"performance-comparison-of-deep-learning-1","title":"Performance Comparison of Deep Learning Techniques in Naira Classification","date":"2024-12-03","arxiv_id":"2412.02072","n_code_links":0,"syntology":null},{"paper":"/paper/vision-transformers-for-weakly-supervised","slug":"vision-transformers-for-weakly-supervised","title":"Vision Transformers for Weakly-Supervised Microorganism Enumeration","date":"2024-12-03","arxiv_id":"2412.02250","n_code_links":2,"syntology":null}],"record_sha256":"60c47068d942126ed27ebf421e7407a554e5dd1ce15eea50cdb47bbb3ae379e3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}