{"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/convolution/papers/41","list_of":"/method/convolution","method":"Convolution","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":41,"pages_in_order":196,"rows_per_page":100,"rows":[4001,4100],"of":19586,"counts":{"archive_papers_tagged":19586,"with_a_code_link":8064,"where_syntology_ran_a_sample":1837,"not_listed_spam_title":0,"listed":19586,"listed_where_code_ran":1837,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1557,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1557,"listed_every_run_a_failure_of_syntologys_instrument":280,"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/convolution","prev":"/method/convolution/papers/40","next":"/method/convolution/papers/42","papers":[{"paper":null,"slug":"multiclass-segmentation-using-teeth-attention","title":"Multiclass Segmentation using Teeth Attention Modules for Dental X-ray Images","date":"2023-11-07","arxiv_id":"2311.03749","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-convolutional-neural-network","title":"Unsupervised convolutional neural network fusion approach for change detection in remote sensing images","date":"2023-11-07","arxiv_id":"2311.03679","n_code_links":0,"syntology":null},{"paper":"/paper/a-two-stage-generative-model-with-cyclegan","slug":"a-two-stage-generative-model-with-cyclegan","title":"A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection","date":"2023-11-06","arxiv_id":"2311.03074","n_code_links":1,"syntology":null},{"paper":"/paper/an-efficient-self-supervised-cross-view","slug":"an-efficient-self-supervised-cross-view","title":"An Efficient Self-Supervised Cross-View Training For Sentence Embedding","date":"2023-11-06","arxiv_id":"2311.03228","n_code_links":1,"syntology":null},{"paper":"/paper/brain-networks-and-intelligence-a-graph","slug":"brain-networks-and-intelligence-a-graph","title":"Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data","date":"2023-11-06","arxiv_id":"2311.03520","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"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) · 1 unverified","official":{"repos":["bishalth01/BrainRGIN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/deep-image-semantic-communication-model-for","slug":"deep-image-semantic-communication-model-for","title":"Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things","date":"2023-11-06","arxiv_id":"2311.02926","n_code_links":2,"syntology":null},{"paper":null,"slug":"multi-loss-based-feature-fusion-and-top-two","title":"Multi Loss-based Feature Fusion and Top Two Voting Ensemble Decision Strategy for Facial Expression Recognition in the Wild","date":"2023-11-06","arxiv_id":"2311.03478","n_code_links":0,"syntology":null},{"paper":"/paper/multispans-a-multi-range-spatial-temporal","slug":"multispans-a-multi-range-spatial-temporal","title":"MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization","date":"2023-11-06","arxiv_id":"2311.02880","n_code_links":1,"syntology":null},{"paper":"/paper/orthonets-orthogonal-channel-attention","slug":"orthonets-orthogonal-channel-attention","title":"OrthoNets: Orthogonal Channel Attention Networks","date":"2023-11-06","arxiv_id":"2311.03071","n_code_links":1,"syntology":null},{"paper":null,"slug":"predicting-age-from-white-matter-diffusivity","title":"Predicting Age from White Matter Diffusivity with Residual Learning","date":"2023-11-06","arxiv_id":"2311.03500","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-shift-multi-objective-loss-function","title":"Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection","date":"2023-11-06","arxiv_id":"2311.02863","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-region-growing-network-for","title":"Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence","date":"2023-11-06","arxiv_id":"2311.03572","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-or-convolution-transformer-encoders","title":"Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency","date":"2023-11-05","arxiv_id":"2311.02772","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-sparse-3d-convolution-network-with-vdb","title":"Fast Sparse 3D Convolution Network with VDB","date":"2023-11-05","arxiv_id":"2311.02762","n_code_links":0,"syntology":null},{"paper":"/paper/counting-manatee-aggregations-using-deep","slug":"counting-manatee-aggregations-using-deep","title":"Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel","date":"2023-11-04","arxiv_id":"2311.02315","n_code_links":1,"syntology":null},{"paper":null,"slug":"forecasting-post-wildfire-vegetation-recovery","title":"Forecasting Post-Wildfire Vegetation Recovery in California using a Convolutional Long Short-Term Memory Tensor Regression Network","date":"2023-11-04","arxiv_id":"2311.02492","n_code_links":0,"syntology":null},{"paper":null,"slug":"hybrid-quantum-image-classification-and","title":"Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis","date":"2023-11-04","arxiv_id":"2311.02402","n_code_links":0,"syntology":null},{"paper":null,"slug":"p-age-pexels-dataset-for-robust-spatio","title":"P-Age: Pexels Dataset for Robust Spatio-Temporal Apparent Age Classification","date":"2023-11-04","arxiv_id":"2311.02432","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-systematic-review-of-deep-graph-neural","title":"A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics","date":"2023-11-03","arxiv_id":"2311.02127","n_code_links":0,"syntology":null},{"paper":null,"slug":"after-stroke-arm-paresis-detection-using","title":"After-Stroke Arm Paresis Detection using Kinematic Data","date":"2023-11-03","arxiv_id":"2311.16138","n_code_links":0,"syntology":null},{"paper":null,"slug":"capturing-local-and-global-features-in","title":"Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer","date":"2023-11-03","arxiv_id":"2311.01731","n_code_links":0,"syntology":null},{"paper":null,"slug":"chex-nomaly-segmenting-lung-abnormalities","title":"CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning","date":"2023-11-03","arxiv_id":"2311.01777","n_code_links":0,"syntology":null},{"paper":null,"slug":"depth-guided-free-space-segmentation-for-a","title":"Depth-guided Free-space Segmentation for a Mobile Robot","date":"2023-11-03","arxiv_id":"2311.01966","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-keratoconus-diseases-using-deep","title":"Detection of keratoconus Diseases using deep Learning","date":"2023-11-03","arxiv_id":"2311.01996","n_code_links":0,"syntology":null},{"paper":null,"slug":"epidemic-decision-making-system-based","title":"Epidemic Decision-making System Based Federated Reinforcement Learning","date":"2023-11-03","arxiv_id":"2311.01749","n_code_links":0,"syntology":null},{"paper":"/paper/minesegsat-an-automated-system-to-evaluate","slug":"minesegsat-an-automated-system-to-evaluate","title":"MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery","date":"2023-11-03","arxiv_id":"2311.01676","n_code_links":1,"syntology":null},{"paper":null,"slug":"nahid-ai-based-algorithm-for-operating-fully","title":"Nahid: AI-based Algorithm for operating fully-automatic surgery","date":"2023-11-03","arxiv_id":"2401.08584","n_code_links":0,"syntology":null},{"paper":null,"slug":"smart-traffic-management-of-vehicles-using","title":"Smart Traffic Management of Vehicles using Faster R-CNN based Deep Learning Method","date":"2023-11-03","arxiv_id":"2311.10099","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-potential-of-wearable-sensors-for","title":"The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)","date":"2023-11-03","arxiv_id":"2311.02251","n_code_links":0,"syntology":null},{"paper":"/paper/bi-preference-learning-heterogeneous","slug":"bi-preference-learning-heterogeneous","title":"Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation","date":"2023-11-02","arxiv_id":"2311.01125","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-vision-transformer-for-accurate","title":"Efficient Vision Transformer for Accurate Traffic Sign Detection","date":"2023-11-02","arxiv_id":"2311.01429","n_code_links":0,"syntology":null},{"paper":null,"slug":"map-assisted-tdoa-localization-enhancement","title":"Map-assisted TDOA Localization Enhancement Based On CNN","date":"2023-11-02","arxiv_id":"2311.01291","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-view-synthesis-from-a-single-rgbd-image","title":"Novel View Synthesis from a Single RGBD Image for Indoor Scenes","date":"2023-11-02","arxiv_id":"2311.01065","n_code_links":0,"syntology":null},{"paper":"/paper/adaptmvsnet-efficient-multi-view-stereo-with","slug":"adaptmvsnet-efficient-multi-view-stereo-with","title":"AdaptMVSNet: Efficient Multi-View Stereo with adaptive convolution and attention fusion","date":"2023-11-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"entity-alignment-method-of-science-and","title":"Entity Alignment Method of Science and Technology Patent based on Graph Convolution Network and Information Fusion","date":"2023-11-01","arxiv_id":"2311.00300","n_code_links":0,"syntology":null},{"paper":"/paper/feature-oriented-deep-learning-framework-for","slug":"feature-oriented-deep-learning-framework-for","title":"Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss","date":"2023-11-01","arxiv_id":"2311.00412","n_code_links":1,"syntology":null},{"paper":null,"slug":"formal-translation-from-reversing-petri-nets","title":"Formal Translation from Reversing Petri Nets to Coloured Petri Nets","date":"2023-11-01","arxiv_id":"2311.00629","n_code_links":0,"syntology":null},{"paper":"/paper/generating-hsr-bogie-vibration-signals-via","slug":"generating-hsr-bogie-vibration-signals-via","title":"Generating HSR Bogie Vibration Signals via Pulse Voltage-Guided Conditional Diffusion Model","date":"2023-11-01","arxiv_id":"2311.00496","n_code_links":1,"syntology":null},{"paper":null,"slug":"kronecker-factored-approximate-curvature-for","title":"Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures","date":"2023-11-01","arxiv_id":"2311.00636","n_code_links":0,"syntology":null},{"paper":"/paper/latent-space-translation-via-semantic-1","slug":"latent-space-translation-via-semantic-1","title":"Latent Space Translation via Semantic Alignment","date":"2023-11-01","arxiv_id":"2311.00664","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-field-dynamics-model-for-granular","title":"Neural Field Dynamics Model for Granular Object Piles Manipulation","date":"2023-11-01","arxiv_id":"2311.00802","n_code_links":0,"syntology":null},{"paper":null,"slug":"progressive-recurrent-network-for-shadow","title":"Progressive Recurrent Network for Shadow Removal","date":"2023-11-01","arxiv_id":"2311.00455","n_code_links":0,"syntology":null},{"paper":"/paper/re-scoring-using-image-language-similarity","slug":"re-scoring-using-image-language-similarity","title":"Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection","date":"2023-11-01","arxiv_id":"2311.00278","n_code_links":1,"syntology":null},{"paper":null,"slug":"revealing-cnn-architectures-via-side-channel","title":"Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based Inference Accelerators","date":"2023-11-01","arxiv_id":"2311.00579","n_code_links":0,"syntology":null},{"paper":"/paper/selectively-sharing-experiences-improves","slug":"selectively-sharing-experiences-improves","title":"Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning","date":"2023-11-01","arxiv_id":"2311.00865","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":["mgerstgrasser/super"],"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":"tipping-points-of-evolving-epidemiological","title":"Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling","date":"2023-11-01","arxiv_id":"2311.00797","n_code_links":0,"syntology":null},{"paper":"/paper/winnet-time-series-forecasting-with-a-window","slug":"winnet-time-series-forecasting-with-a-window","title":"WinNet: Make Only One Convolutional Layer Effective for Time Series Forecasting","date":"2023-11-01","arxiv_id":"2311.00214","n_code_links":1,"syntology":null},{"paper":null,"slug":"ddc-pim-efficient-algorithm-architecture-co","title":"DDC-PIM: Efficient Algorithm/Architecture Co-design for Doubling Data Capacity of SRAM-based Processing-In-Memory","date":"2023-10-31","arxiv_id":"2310.20424","n_code_links":0,"syntology":null},{"paper":null,"slug":"density-matrix-emulation-of-quantum-recurrent","title":"Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Prediction","date":"2023-10-31","arxiv_id":"2310.20671","n_code_links":0,"syntology":null},{"paper":"/paper/from-denoising-training-to-test-time","slug":"from-denoising-training-to-test-time","title":"From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation","date":"2023-10-31","arxiv_id":"2310.20271","n_code_links":1,"syntology":null},{"paper":"/paper/iars-segnet-interpretable-attention-residual","slug":"iars-segnet-interpretable-attention-residual","title":"IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation","date":"2023-10-31","arxiv_id":"2310.20292","n_code_links":0,"syntology":null},{"paper":null,"slug":"importance-estimation-with-random-gradient","title":"Importance Estimation with Random Gradient for Neural Network Pruning","date":"2023-10-31","arxiv_id":"2310.20203","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-dose-ct-image-enhancement-using-deep","title":"Low-Dose CT Image Enhancement Using Deep Learning","date":"2023-10-31","arxiv_id":"2310.20265","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-refinement-of-in-situ-images","title":"Machine learning refinement of in situ images acquired by low electron dose LC-TEM","date":"2023-10-31","arxiv_id":"2310.20279","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-task-deep-convolutional-network-to","title":"Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity","date":"2023-10-31","arxiv_id":"2311.00167","n_code_links":0,"syntology":null},{"paper":null,"slug":"rir-sf-room-impulse-response-based-spatial","title":"RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios","date":"2023-10-31","arxiv_id":"2311.00146","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-serotonergic-psychedelic-n-n","title":"The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in cortical neural circuits","date":"2023-10-31","arxiv_id":"2310.20582","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-federated-learning-framework-for-stenosis","title":"A Federated Learning Framework for Stenosis Detection","date":"2023-10-30","arxiv_id":"2310.19445","n_code_links":0,"syntology":null},{"paper":null,"slug":"can-input-reconstruction-be-used-to-directly","title":"Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model? -- Application to proton therapy dose prediction for head and neck cancer patients","date":"2023-10-30","arxiv_id":"2310.19686","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-state-space-models-for-long","slug":"convolutional-state-space-models-for-long","title":"Convolutional State Space Models for Long-Range Spatiotemporal Modeling","date":"2023-10-30","arxiv_id":"2310.19694","n_code_links":1,"syntology":null},{"paper":null,"slug":"decoupled-actor-critic","title":"On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics","date":"2023-10-30","arxiv_id":"2310.19527","n_code_links":0,"syntology":null},{"paper":null,"slug":"intelligent-breast-cancer-diagnosis-with","title":"Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images","date":"2023-10-30","arxiv_id":"2310.19411","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-measuring-fairness-in-generative-models","title":"On Measuring Fairness in Generative Models","date":"2023-10-30","arxiv_id":"2310.19297","n_code_links":0,"syntology":null},{"paper":"/paper/resource-constrained-semantic-segmentation","slug":"resource-constrained-semantic-segmentation","title":"Resource Constrained Semantic Segmentation for Waste Sorting","date":"2023-10-30","arxiv_id":"2310.19407","n_code_links":1,"syntology":null},{"paper":"/paper/towards-few-annotation-learning-for-object","slug":"towards-few-annotation-learning-for-object","title":"Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?","date":"2023-10-30","arxiv_id":"2310.19936","n_code_links":1,"syntology":null},{"paper":null,"slug":"automaton-distillation-neuro-symbolic","title":"Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning","date":"2023-10-29","arxiv_id":"2310.19137","n_code_links":0,"syntology":null},{"paper":null,"slug":"customize-stylegan-with-one-hand-sketch","title":"Customize StyleGAN with One Hand Sketch","date":"2023-10-29","arxiv_id":"2310.18949","n_code_links":0,"syntology":null},{"paper":null,"slug":"diffspectralnet-unveiling-the-potential-of","title":"DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification","date":"2023-10-29","arxiv_id":"2312.12441","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-task-and-weight-prioritization","slug":"dynamic-task-and-weight-prioritization","title":"Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery","date":"2023-10-29","arxiv_id":"2310.19109","n_code_links":1,"syntology":null},{"paper":null,"slug":"feature-aggregation-in-joint-sound","title":"Feature Aggregation in Joint Sound Classification and Localization Neural Networks","date":"2023-10-29","arxiv_id":"2310.19063","n_code_links":0,"syntology":null},{"paper":"/paper/benchmark-generation-framework-with","slug":"benchmark-generation-framework-with","title":"Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness","date":"2023-10-28","arxiv_id":"2310.18626","n_code_links":1,"syntology":null},{"paper":null,"slug":"emotion-oriented-behavior-model-using-deep","title":"Emotion-Oriented Behavior Model Using Deep Learning","date":"2023-10-28","arxiv_id":"2311.14674","n_code_links":0,"syntology":null},{"paper":"/paper/laughing-hyena-distillery-extracting-compact","slug":"laughing-hyena-distillery-extracting-compact","title":"Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions","date":"2023-10-28","arxiv_id":"2310.18780","n_code_links":1,"syntology":null},{"paper":"/paper/weakly-coupled-deep-q-networks","slug":"weakly-coupled-deep-q-networks","title":"Weakly Coupled Deep Q-Networks","date":"2023-10-28","arxiv_id":"2310.18803","n_code_links":0,"syntology":{"ran":5,"of":10,"n_ran_checked":3,"n_instrument":2,"unverified":5,"pointer_only":10,"phrase":"5 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":"/paper/edge-ai-based-vein-detector-for-efficient","slug":"edge-ai-based-vein-detector-for-efficient","title":"Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa","date":"2023-10-27","arxiv_id":"2310.18234","n_code_links":1,"syntology":null},{"paper":"/paper/siamese-detr-for-generic-multi-object","slug":"siamese-detr-for-generic-multi-object","title":"Siamese-DETR for Generic Multi-Object Tracking","date":"2023-10-27","arxiv_id":"2310.17875","n_code_links":1,"syntology":null},{"paper":null,"slug":"understanding-parameter-saliency-via-extreme","title":"Understanding Parameter Saliency via Extreme Value Theory","date":"2023-10-27","arxiv_id":"2310.17951","n_code_links":0,"syntology":null},{"paper":null,"slug":"universality-for-the-global-spectrum-of","title":"Universality for the global spectrum of random inner-product kernel matrices in the polynomial regime","date":"2023-10-27","arxiv_id":"2310.18280","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-resource-management-for-edge-network","title":"Adaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning","date":"2023-10-26","arxiv_id":"2310.17523","n_code_links":0,"syntology":null},{"paper":null,"slug":"cosmosdsr-a-methodology-for-automated","title":"CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter","date":"2023-10-26","arxiv_id":"2310.17158","n_code_links":0,"syntology":null},{"paper":null,"slug":"dsac-c-constrained-maximum-entropy-for-robust","title":"DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic","date":"2023-10-26","arxiv_id":"2310.17173","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-sea-ice-segmentation-in-sentinel-1","slug":"enhancing-sea-ice-segmentation-in-sentinel-1","title":"Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions","date":"2023-10-26","arxiv_id":"2310.17122","n_code_links":1,"syntology":null},{"paper":null,"slug":"feature-extraction-and-classification-from","title":"Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning","date":"2023-10-26","arxiv_id":"2310.17681","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-neonatal-chest-sound-separation","title":"Real-time Neonatal Chest Sound Separation using Deep Learning","date":"2023-10-26","arxiv_id":"2310.17116","n_code_links":0,"syntology":null},{"paper":null,"slug":"single-channel-speech-enhancement-by-colored","title":"Single channel speech enhancement by colored spectrograms","date":"2023-10-26","arxiv_id":"2310.17142","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-plant-identification-and","slug":"deep-learning-for-plant-identification-and","title":"Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches","date":"2023-10-25","arxiv_id":"2310.16273","n_code_links":1,"syntology":null},{"paper":null,"slug":"deepfake-detection-leveraging-the-power-of-2d","title":"Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles","date":"2023-10-25","arxiv_id":"2310.16388","n_code_links":0,"syntology":null},{"paper":"/paper/free-form-flows-make-any-architecture-a","slug":"free-form-flows-make-any-architecture-a","title":"Free-form Flows: Make Any Architecture a Normalizing Flow","date":"2023-10-25","arxiv_id":"2310.16624","n_code_links":1,"syntology":null},{"paper":null,"slug":"improvement-in-alzheimer-s-disease-mri-images","title":"Improvement in Alzheimer's Disease MRI Images Analysis by Convolutional Neural Networks Via Topological Optimization","date":"2023-10-25","arxiv_id":"2310.16857","n_code_links":0,"syntology":null},{"paper":null,"slug":"instance-wise-linearization-of-neural-network","title":"Instance-wise Linearization of Neural Network for Model Interpretation","date":"2023-10-25","arxiv_id":"2310.16295","n_code_links":0,"syntology":null},{"paper":"/paper/torchsparse-efficient-training-and-inference","slug":"torchsparse-efficient-training-and-inference","title":"TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs","date":"2023-10-25","arxiv_id":"2311.12862","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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mit-han-lab/torchsparse"],"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":"transpose-6d-object-pose-estimation-with","title":"TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer","date":"2023-10-25","arxiv_id":"2310.16279","n_code_links":0,"syntology":null},{"paper":"/paper/automatic-aorta-segmentation-with-heavily","slug":"automatic-aorta-segmentation-with-heavily","title":"Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge","date":"2023-10-24","arxiv_id":"2310.15827","n_code_links":1,"syntology":null},{"paper":null,"slug":"convbki-real-time-probabilistic-semantic","title":"ConvBKI: Real-Time Probabilistic Semantic Mapping Network with Quantifiable Uncertainty","date":"2023-10-24","arxiv_id":"2310.16020","n_code_links":0,"syntology":null},{"paper":null,"slug":"decoupled-detr-spatially-disentangling-1","title":"Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection","date":"2023-10-24","arxiv_id":"2310.15955","n_code_links":0,"syntology":null},{"paper":"/paper/g-cascade-efficient-cascaded-graph","slug":"g-cascade-efficient-cascaded-graph","title":"G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation","date":"2023-10-24","arxiv_id":"2310.16175","n_code_links":1,"syntology":{"ran":6,"of":9,"n_ran_checked":1,"n_instrument":5,"unverified":3,"pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["SLDGroup/G-CASCADE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"grid-frequency-forecasting-in-university","title":"Grid Frequency Forecasting in University Campuses using Convolutional LSTM","date":"2023-10-24","arxiv_id":"2310.16071","n_code_links":0,"syntology":null},{"paper":null,"slug":"kirchhoffnet-a-circuit-bridging-message","title":"KirchhoffNet: A Scalable Ultra Fast Analog Neural Network","date":"2023-10-24","arxiv_id":"2310.15872","n_code_links":0,"syntology":null},{"paper":null,"slug":"off-policy-evaluation-for-large-action-spaces-2","title":"Off-Policy Evaluation for Large Action Spaces via Policy Convolution","date":"2023-10-24","arxiv_id":"2310.15433","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-convergence-and-sample-complexity","title":"On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $ε$-Greedy Exploration","date":"2023-10-24","arxiv_id":"2310.16173","n_code_links":0,"syntology":null}],"record_sha256":"8d7e70c65c5ef3223b7ae1acb205bf39a797a4d3b7a77a597385b9fe756b27f6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}