{"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/119","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":119,"pages_in_order":196,"rows_per_page":100,"rows":[11801,11900],"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/118","next":"/method/convolution/papers/120","papers":[{"paper":null,"slug":"symbol-shift-equivariant-neural-networks","title":"Symbol-Shift Equivariant Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/synthesising-realistic-calcium-imaging-data","slug":"synthesising-realistic-calcium-imaging-data","title":"Synthesising Realistic Calcium Imaging Data of Neuronal Populations Using GAN","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"teleport-graph-convolutional-networks","title":"Teleport Graph Convolutional Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-3tconv-an-intrinsic-approach-to","title":"The 3TConv: An Intrinsic Approach to Explainable 3D CNNs","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-card-shuffling-hypotheses-building-a-time","title":"The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unified-analytic-forms-for-convolutional","title":"Unified analytic forms for Convolutional Neural Networks and Wavelet Filter Banks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-real-world-super-resolution-a","title":"Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"variance-based-sample-weighting-for","title":"Variance Based Sample Weighting for Supervised Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-transformers-where-do-transformers","title":"Visual Transformers: Where Do Transformers Really Belong in Vision Models?","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"wb-detr-transformer-based-detector-without","title":"WB-DETR: Transformer-Based Detector Without Backbone","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"weighted-bellman-backups-for-improved-signal","title":"Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"weighted-line-graph-convolutional-networks","title":"Weighted Line Graph Convolutional Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-cnn-approach-to-simultaneously-count-plants","title":"A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery","date":"2020-12-31","arxiv_id":"2012.15827","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-ode-based-neural-networks-on-low","title":"Accelerating ODE-Based Neural Networks on Low-Cost FPGAs","date":"2020-12-31","arxiv_id":"2012.15465","n_code_links":0,"syntology":null},{"paper":null,"slug":"asynchronous-advantage-actor-critic-non-1","title":"Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup","date":"2020-12-31","arxiv_id":"2012.15511","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-neural-network-and-rule-based","slug":"convolutional-neural-network-and-rule-based","title":"Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs","date":"2020-12-31","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/efficientnet-absolute-zero-for-continuous","slug":"efficientnet-absolute-zero-for-continuous","title":"EfficientNet-Absolute Zero for Continuous Speech Keyword Spotting","date":"2020-12-31","arxiv_id":"2012.15695","n_code_links":2,"syntology":null},{"paper":null,"slug":"frea-unet-frequency-aware-u-net-for-modality","title":"FREA-Unet: Frequency-aware U-net for Modality Transfer","date":"2020-12-31","arxiv_id":"2012.15397","n_code_links":0,"syntology":null},{"paper":null,"slug":"i-o-lower-bounds-for-auto-tuning-of","title":"I/O Lower Bounds for Auto-tuning of Convolutions in CNNs","date":"2020-12-31","arxiv_id":"2012.15667","n_code_links":0,"syntology":null},{"paper":"/paper/rethinking-semantic-segmentation-from-a","slug":"rethinking-semantic-segmentation-from-a","title":"Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers","date":"2020-12-31","arxiv_id":"2012.15840","n_code_links":5,"syntology":null},{"paper":"/paper/automatic-polyp-segmentation-using-u-net","slug":"automatic-polyp-segmentation-using-u-net","title":"Automatic Polyp Segmentation using U-Net-ResNet50","date":"2020-12-30","arxiv_id":"2012.15247","n_code_links":0,"syntology":null},{"paper":null,"slug":"damaged-fingerprint-recognition-by","title":"Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes","date":"2020-12-30","arxiv_id":"2012.15041","n_code_links":0,"syntology":null},{"paper":"/paper/deepsphere-a-graph-based-spherical-cnn-1","slug":"deepsphere-a-graph-based-spherical-cnn-1","title":"DeepSphere: a graph-based spherical CNN","date":"2020-12-30","arxiv_id":"2012.15000","n_code_links":8,"syntology":{"ran":9,"of":13,"n_ran_checked":7,"n_instrument":2,"unverified":4,"pointer_only":4,"phrase":"9 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["deepsphere/deepsphere-tf1"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/exploring-large-context-for-cerebral-aneurysm","slug":"exploring-large-context-for-cerebral-aneurysm","title":"Exploring Large Context for Cerebral Aneurysm Segmentation","date":"2020-12-30","arxiv_id":"2012.15136","n_code_links":1,"syntology":null},{"paper":"/paper/towards-unsupervised-deep-image-enhancement","slug":"towards-unsupervised-deep-image-enhancement","title":"Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network","date":"2020-12-30","arxiv_id":"2012.15020","n_code_links":1,"syntology":null},{"paper":null,"slug":"transformer-for-image-quality-assessment","title":"Transformer for Image Quality Assessment","date":"2020-12-30","arxiv_id":"2101.01097","n_code_links":0,"syntology":null},{"paper":"/paper/2d-or-not-2d-adaptive-3d-convolution","slug":"2d-or-not-2d-adaptive-3d-convolution","title":"2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition","date":"2020-12-29","arxiv_id":"2012.14950","n_code_links":0,"syntology":null},{"paper":null,"slug":"cascaded-framework-for-automatic-evaluation","title":"Cascaded Framework for Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI","date":"2020-12-29","arxiv_id":"2012.14556","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensembled-resunet-for-anatomical-brain","title":"Ensembled ResUnet for Anatomical Brain Barriers Segmentation","date":"2020-12-29","arxiv_id":"2012.14567","n_code_links":0,"syntology":null},{"paper":"/paper/hybrid-micro-macro-level-convolution-for","slug":"hybrid-micro-macro-level-convolution-for","title":"Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning","date":"2020-12-29","arxiv_id":"2012.14722","n_code_links":1,"syntology":null},{"paper":"/paper/kaleidoscope-an-efficient-learnable-1","slug":"kaleidoscope-an-efficient-learnable-1","title":"Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps","date":"2020-12-29","arxiv_id":"2012.14966","n_code_links":2,"syntology":{"ran":16,"of":23,"n_ran_checked":9,"n_instrument":7,"unverified":7,"pointer_only":0,"phrase":"16 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 7 unverified","official":{"repos":["HazyResearch/butterfly","HazyResearch/learning-circuits"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":"/paper/layoutlmv2-multi-modal-pre-training-for","slug":"layoutlmv2-multi-modal-pre-training-for","title":"LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding","date":"2020-12-29","arxiv_id":"2012.14740","n_code_links":9,"syntology":null},{"paper":null,"slug":"object-sorting-using-faster-r-cnn","title":"Object sorting using faster R-CNN","date":"2020-12-29","arxiv_id":"2012.14840","n_code_links":0,"syntology":null},{"paper":"/paper/reinforcement-learning-for-control-of-valves","slug":"reinforcement-learning-for-control-of-valves","title":"Reinforcement Learning for Control of Valves","date":"2020-12-29","arxiv_id":"2012.14668","n_code_links":2,"syntology":null},{"paper":null,"slug":"3d-axial-attention-for-lung-nodule","title":"3D Axial-Attention for Lung Nodule Classification","date":"2020-12-28","arxiv_id":"2012.14117","n_code_links":0,"syntology":null},{"paper":null,"slug":"action-recognition-with-kernel-based-graph","title":"Action Recognition with Kernel-based Graph Convolutional Networks","date":"2020-12-28","arxiv_id":"2012.14186","n_code_links":0,"syntology":null},{"paper":"/paper/aerial-imagery-pile-burn-detection-using-deep","slug":"aerial-imagery-pile-burn-detection-using-deep","title":"Aerial Imagery Pile burn detection using Deep Learning: the FLAME dataset","date":"2020-12-28","arxiv_id":"2012.14036","n_code_links":2,"syntology":null},{"paper":null,"slug":"cascaded-convolutional-neural-network-for","title":"Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI","date":"2020-12-28","arxiv_id":"2012.14128","n_code_links":0,"syntology":null},{"paper":null,"slug":"combining-cnn-and-hybrid-active-contours-for","title":"Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images","date":"2020-12-28","arxiv_id":"2012.14207","n_code_links":0,"syntology":null},{"paper":null,"slug":"disentangled-planning-and-control-in-vision","title":"Disentangled Planning and Control in Vision Based Robotics via Reward Machines","date":"2020-12-28","arxiv_id":"2012.14464","n_code_links":0,"syntology":null},{"paper":null,"slug":"lip-reading-with-hierarchical-pyramidal","title":"Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention","date":"2020-12-28","arxiv_id":"2012.14360","n_code_links":0,"syntology":null},{"paper":null,"slug":"lookhops-light-multi-order-convolution-and","title":"LookHops: light multi-order convolution and pooling for graph classification","date":"2020-12-28","arxiv_id":"2012.15741","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-convolution-for-semantic-role","title":"Adaptive Convolution for Semantic Role Labeling","date":"2020-12-27","arxiv_id":"2012.13939","n_code_links":0,"syntology":null},{"paper":null,"slug":"ellipse-regression-with-predicted","title":"Ellipse Regression with Predicted Uncertainties for Accurate Multi-View 3D Object Estimation","date":"2020-12-27","arxiv_id":"2101.05212","n_code_links":0,"syntology":null},{"paper":"/paper/multi-channel-auto-calibration-for-the","slug":"multi-channel-auto-calibration-for-the","title":"Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning","date":"2020-12-27","arxiv_id":"2012.14023","n_code_links":1,"syntology":null},{"paper":null,"slug":"structure-aware-layer-decomposition-learning","title":"Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning","date":"2020-12-27","arxiv_id":"2012.13894","n_code_links":0,"syntology":null},{"paper":null,"slug":"direct-quantization-for-training-highly","title":"Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks","date":"2020-12-26","arxiv_id":"2012.13762","n_code_links":0,"syntology":null},{"paper":null,"slug":"explainable-multi-class-classification-of","title":"Explainable Multi-class Classification of Medical Data","date":"2020-12-26","arxiv_id":"2012.13796","n_code_links":0,"syntology":null},{"paper":null,"slug":"linking-attention-based-multiscale-cnn-with","title":"Linking Attention-Based Multiscale CNN With Dynamical GCN for Driving Fatigue Detection","date":"2020-12-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/occipital-and-left-temporal-instantaneous","slug":"occipital-and-left-temporal-instantaneous","title":"Occipital and left temporal instantaneous amplitude and frequency oscillations correlated with access and phenomenal consciousness","date":"2020-12-26","arxiv_id":"2101.10056","n_code_links":1,"syntology":null},{"paper":"/paper/sparse-adversarial-attack-to-object-detection","slug":"sparse-adversarial-attack-to-object-detection","title":"Sparse Adversarial Attack to Object Detection","date":"2020-12-26","arxiv_id":"2012.13692","n_code_links":1,"syntology":null},{"paper":null,"slug":"implicit-feature-pyramid-network-for-object","title":"Implicit Feature Pyramid Network for Object Detection","date":"2020-12-25","arxiv_id":"2012.13563","n_code_links":0,"syntology":null},{"paper":"/paper/inception-convolution-with-efficient-dilation","slug":"inception-convolution-with-efficient-dilation","title":"Inception Convolution with Efficient Dilation Search","date":"2020-12-25","arxiv_id":"2012.13587","n_code_links":1,"syntology":null},{"paper":null,"slug":"revisiting-edge-detection-in-convolutional","title":"Revisiting Edge Detection in Convolutional Neural Networks","date":"2020-12-25","arxiv_id":"2012.13576","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-state-representation-dueling-network-for","title":"A State Representation Dueling Network for Deep Reinforcement Learning","date":"2020-12-24","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"appearance-invariant-6-dof-visual","title":"Appearance-Invariant 6-DoF Visual Localization using Generative Adversarial Networks","date":"2020-12-24","arxiv_id":"2012.13191","n_code_links":0,"syntology":null},{"paper":"/paper/edn-salient-object-detection-via-extremely","slug":"edn-salient-object-detection-via-extremely","title":"EDN: Salient Object Detection via Extremely-Downsampled Network","date":"2020-12-24","arxiv_id":"2012.13093","n_code_links":1,"syntology":null},{"paper":null,"slug":"granet-global-relation-aware-attentional","title":"GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification","date":"2020-12-24","arxiv_id":"2012.13466","n_code_links":0,"syntology":null},{"paper":null,"slug":"hausdorff-point-convolution-with-geometric","title":"Hausdorff Point Convolution with Geometric Priors","date":"2020-12-24","arxiv_id":"2012.13118","n_code_links":0,"syntology":null},{"paper":null,"slug":"seed-phenotyping-on-neural-networks-using","title":"Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning","date":"2020-12-24","arxiv_id":"2012.13259","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-deep-clustering-and","title":"Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets","date":"2020-12-24","arxiv_id":"2012.13321","n_code_links":0,"syntology":null},{"paper":"/paper/cholecseg8k-a-semantic-segmentation-dataset","slug":"cholecseg8k-a-semantic-segmentation-dataset","title":"CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80","date":"2020-12-23","arxiv_id":"2012.12453","n_code_links":2,"syntology":null},{"paper":null,"slug":"diabetic-retinopathy-grading-system-based-on","title":"Diabetic Retinopathy Grading System Based on Transfer Learning","date":"2020-12-23","arxiv_id":"2012.12515","n_code_links":0,"syntology":null},{"paper":"/paper/focal-frequency-loss-for-generative-models","slug":"focal-frequency-loss-for-generative-models","title":"Focal Frequency Loss for Image Reconstruction and Synthesis","date":"2020-12-23","arxiv_id":"2012.12821","n_code_links":1,"syntology":null},{"paper":null,"slug":"icmsc-intra-and-cross-modality-semantic","title":"ICMSC: Intra- and Cross-modality Semantic Consistency for Unsupervised Domain Adaptation on Hip Joint Bone Segmentation","date":"2020-12-23","arxiv_id":"2012.12570","n_code_links":0,"syntology":null},{"paper":null,"slug":"mg-sagc-a-multiscale-graph-and-its-self","title":"MG-SAGC: A multiscale graph and its self-adaptive graph convolution network for 3D point clouds","date":"2020-12-23","arxiv_id":"2012.12445","n_code_links":0,"syntology":null},{"paper":"/paper/swa-object-detection","slug":"swa-object-detection","title":"SWA Object Detection","date":"2020-12-23","arxiv_id":"2012.12645","n_code_links":2,"syntology":null},{"paper":"/paper/synet-an-ensemble-network-for-object","slug":"synet-an-ensemble-network-for-object","title":"SyNet: An Ensemble Network for Object Detection in UAV Images","date":"2020-12-23","arxiv_id":"2012.12991","n_code_links":1,"syntology":null},{"paper":null,"slug":"applying-wav2vec2-0-to-speech-recognition-in","title":"Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages","date":"2020-12-22","arxiv_id":"2012.12121","n_code_links":0,"syntology":null},{"paper":"/paper/cloud-removal-in-remote-sensing-images-using","slug":"cloud-removal-in-remote-sensing-images-using","title":"Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation","date":"2020-12-22","arxiv_id":"2012.12180","n_code_links":3,"syntology":null},{"paper":"/paper/efficient-and-visualizable-convolutional","slug":"efficient-and-visualizable-convolutional","title":"Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT","date":"2020-12-22","arxiv_id":"2012.11860","n_code_links":1,"syntology":null},{"paper":"/paper/fast-fluid-simulations-in-3d-with-physics","slug":"fast-fluid-simulations-in-3d-with-physics","title":"Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D","date":"2020-12-22","arxiv_id":"2012.11893","n_code_links":3,"syntology":null},{"paper":"/paper/fcanet-frequency-channel-attention-networks","slug":"fcanet-frequency-channel-attention-networks","title":"FcaNet: Frequency Channel Attention Networks","date":"2020-12-22","arxiv_id":"2012.11879","n_code_links":7,"syntology":null},{"paper":"/paper/fracbnn-accurate-and-fpga-efficient-binary","slug":"fracbnn-accurate-and-fpga-efficient-binary","title":"FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations","date":"2020-12-22","arxiv_id":"2012.12206","n_code_links":2,"syntology":null},{"paper":null,"slug":"geometric-robust-descriptor-for-3d-point","title":"Robust Kernel-based Feature Representation for 3D Point Cloud Analysis via Circular Convolutional Network","date":"2020-12-22","arxiv_id":"2012.12215","n_code_links":0,"syntology":null},{"paper":null,"slug":"guidedstyle-attribute-knowledge-guided-style","title":"GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing","date":"2020-12-22","arxiv_id":"2012.11856","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-joint-2d-3d-representations-for-1","title":"Learning Joint 2D-3D Representations for Depth Completion","date":"2020-12-22","arxiv_id":"2012.12402","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-instance-segmentation-in-brachial","title":"Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet","date":"2020-12-22","arxiv_id":"2012.12012","n_code_links":0,"syntology":null},{"paper":null,"slug":"prediction-of-chronic-kidney-disease-using","title":"Prediction of Chronic Kidney Disease Using Deep Neural Network","date":"2020-12-22","arxiv_id":"2012.12089","n_code_links":0,"syntology":null},{"paper":"/paper/time-travel-rephotography","slug":"time-travel-rephotography","title":"Time-Travel Rephotography","date":"2020-12-22","arxiv_id":"2012.12261","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Time-Travel-Rephotography/Time-Travel-Rephotography.github.io"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"universal-approximation-properties-for-odenet","title":"Universal Approximation Properties for an ODENet and a ResNet: Mathematical Analysis and Numerical Experiments","date":"2020-12-22","arxiv_id":"2101.10229","n_code_links":0,"syntology":null},{"paper":null,"slug":"2012-11643","title":"myGym: Modular Toolkit for Visuomotor Robotic Tasks","date":"2020-12-21","arxiv_id":"2012.11643","n_code_links":0,"syntology":null},{"paper":null,"slug":"contraband-materials-detection-within","title":"Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery","date":"2020-12-21","arxiv_id":"2012.11753","n_code_links":0,"syntology":null},{"paper":"/paper/hyperseg-patch-wise-hypernetwork-for-real","slug":"hyperseg-patch-wise-hypernetwork-for-real","title":"HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation","date":"2020-12-21","arxiv_id":"2012.11582","n_code_links":1,"syntology":null},{"paper":null,"slug":"infrared-image-pedestrian-target-detection","title":"Infrared image pedestrian target detection based on Yolov3 and migration learning","date":"2020-12-21","arxiv_id":"2012.11185","n_code_links":0,"syntology":null},{"paper":"/paper/knowledge-transfer-based-fine-grained-visual","slug":"knowledge-transfer-based-fine-grained-visual","title":"Knowledge Transfer Based Fine-grained Visual Classification","date":"2020-12-21","arxiv_id":"2012.11389","n_code_links":1,"syntology":null},{"paper":"/paper/online-bag-of-visual-words-generation-for","slug":"online-bag-of-visual-words-generation-for","title":"OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning","date":"2020-12-21","arxiv_id":"2012.11552","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"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":["valeoai/obow"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"small-footprint-wake-up-word-recognition-in","title":"Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature","date":"2020-12-21","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"techtexc-classification-of-technical-texts","title":"TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network","date":"2020-12-21","arxiv_id":"2012.11420","n_code_links":0,"syntology":null},{"paper":null,"slug":"adnfm-an-attentive-densenet-based","title":"AdnFM: An Attentive DenseNet based Factorization Machine for CTR Prediction","date":"2020-12-20","arxiv_id":"2012.10820","n_code_links":0,"syntology":null},{"paper":null,"slug":"anchor-based-spatial-temporal-attention","title":"Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences","date":"2020-12-20","arxiv_id":"2012.10860","n_code_links":0,"syntology":null},{"paper":"/paper/color-channel-perturbation-attacks-for","slug":"color-channel-perturbation-attacks-for","title":"Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks","date":"2020-12-20","arxiv_id":"2012.14456","n_code_links":1,"syntology":null},{"paper":null,"slug":"computer-vision-based-accident-detection-for","title":"Computer Vision based Accident Detection for Autonomous Vehicles","date":"2020-12-20","arxiv_id":"2012.10870","n_code_links":0,"syntology":null},{"paper":null,"slug":"computer-vision-based-animal-collision","title":"Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles","date":"2020-12-20","arxiv_id":"2012.10878","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-hyperspectral-image","title":"Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks","date":"2020-12-20","arxiv_id":"2012.10932","n_code_links":0,"syntology":null},{"paper":null,"slug":"dccrgan-deep-complex-convolution-recurrent","title":"DCCRGAN: Deep Complex Convolution Recurrent Generator Adversarial Network for Speech Enhancement","date":"2020-12-19","arxiv_id":"2012.10732","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-optical-convolutional-neural-network","title":"Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing","date":"2020-12-19","arxiv_id":"2012.10812","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-holistically-guided-decoder-for-deep","title":"A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection","date":"2020-12-18","arxiv_id":"2012.10162","n_code_links":0,"syntology":null},{"paper":"/paper/a-surrogate-lagrangian-relaxation-based-model","slug":"a-surrogate-lagrangian-relaxation-based-model","title":"Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation","date":"2020-12-18","arxiv_id":"2012.10079","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-the-ground-level-ozone","title":"Investigating Ground-level Ozone Formation: A Case Study in Taiwan","date":"2020-12-18","arxiv_id":"2012.10058","n_code_links":0,"syntology":null}],"record_sha256":"d7b9408a7eaf8bd9534c110cbb04c2e2c83c69abfd2b635fdfb82ebf5887faed","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}