{"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/kaiming-initialization/papers/17","list_of":"/method/kaiming-initialization","method":"Kaiming Initialization","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":17,"pages_in_order":30,"rows_per_page":100,"rows":[1601,1700],"of":2931,"counts":{"archive_papers_tagged":2931,"with_a_code_link":1332,"where_syntology_ran_a_sample":379,"not_listed_spam_title":0,"listed":2931,"listed_where_code_ran":379,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":319,"every_run_a_failure_of_syntologys_instrument":60,"listed_with_a_run_with_no_instrument_failure":319,"listed_every_run_a_failure_of_syntologys_instrument":60,"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/kaiming-initialization","prev":"/method/kaiming-initialization/papers/16","next":"/method/kaiming-initialization/papers/18","papers":[{"paper":null,"slug":"lottery-ticket-implies-accuracy-degradation","title":"Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?","date":"2021-02-19","arxiv_id":"2102.11068","n_code_links":0,"syntology":null},{"paper":"/paper/training-cascaded-networks-for-speeded","slug":"training-cascaded-networks-for-speeded","title":"Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss","date":"2021-02-19","arxiv_id":"2102.09808","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-mathematical-principle-of-deep-learning","title":"A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space","date":"2021-02-18","arxiv_id":"2102.09235","n_code_links":0,"syntology":null},{"paper":"/paper/recurrent-rational-networks","slug":"recurrent-rational-networks","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","date":"2021-02-18","arxiv_id":"2102.09407","n_code_links":4,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":3,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ml-research/rational_activations","ml-research/rational_rl","ml-research/rational_sl","k4ntz/activation-functions"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-dataset-and-benchmark-for-malaria-life","title":"A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images","date":"2021-02-17","arxiv_id":"2102.08708","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-transfer-learning-of-elastography","title":"Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images","date":"2021-02-17","arxiv_id":"2102.08567","n_code_links":0,"syntology":null},{"paper":"/paper/lambdanetworks-modeling-long-range-1","slug":"lambdanetworks-modeling-long-range-1","title":"LambdaNetworks: Modeling Long-Range Interactions Without Attention","date":"2021-02-17","arxiv_id":"2102.08602","n_code_links":7,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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":null}},{"paper":"/paper/an-automl-based-approach-to-multimodal-image","slug":"an-automl-based-approach-to-multimodal-image","title":"An AutoML-based Approach to Multimodal Image Sentiment Analysis","date":"2021-02-16","arxiv_id":"2102.08092","n_code_links":0,"syntology":null},{"paper":"/paper/improving-deep-learning-based-semi-supervised","slug":"improving-deep-learning-based-semi-supervised","title":"Comparison of semi-supervised deep learning algorithms for audio classification","date":"2021-02-16","arxiv_id":"2102.08183","n_code_links":1,"syntology":null},{"paper":null,"slug":"training-larger-networks-for-deep","title":"Training Larger Networks for Deep Reinforcement Learning","date":"2021-02-16","arxiv_id":"2102.07920","n_code_links":0,"syntology":null},{"paper":null,"slug":"colored-kimia-path24-dataset-configurations","title":"Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings","date":"2021-02-15","arxiv_id":"2102.07611","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-severity-classification-of","title":"Detection and severity classification of COVID-19 in CT images using deep learning","date":"2021-02-15","arxiv_id":"2102.07726","n_code_links":0,"syntology":null},{"paper":"/paper/momentum-residual-neural-networks","slug":"momentum-residual-neural-networks","title":"Momentum Residual Neural Networks","date":"2021-02-15","arxiv_id":"2102.07870","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-deepsentinel-an-extensible-corpus-of","title":"Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model","date":"2021-02-11","arxiv_id":"2102.06260","n_code_links":0,"syntology":null},{"paper":null,"slug":"application-of-yolo-on-mask-detection-task","title":"Application of Yolo on Mask Detection Task","date":"2021-02-10","arxiv_id":"2102.05402","n_code_links":0,"syntology":null},{"paper":"/paper/brecq-pushing-the-limit-of-post-training-1","slug":"brecq-pushing-the-limit-of-post-training-1","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","date":"2021-02-10","arxiv_id":"2102.05426","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":2,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yhhhli/BRECQ"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/regional-attention-with-architecture-rebuilt","slug":"regional-attention-with-architecture-rebuilt","title":"Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition","date":"2021-02-10","arxiv_id":"2102.05348","n_code_links":1,"syntology":null},{"paper":null,"slug":"distribution-adaptive-int8-quantization-for","title":"Distribution Adaptive INT8 Quantization for Training CNNs","date":"2021-02-09","arxiv_id":"2102.04782","n_code_links":0,"syntology":null},{"paper":"/paper/mali-a-memory-efficient-and-reverse-accurate-1","slug":"mali-a-memory-efficient-and-reverse-accurate-1","title":"MALI: A memory efficient and reverse accurate integrator for Neural ODEs","date":"2021-02-09","arxiv_id":"2102.04668","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["juntang-zhuang/TorchDiffEqPack"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"train-a-one-million-way-instance-classifier","title":"Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning","date":"2021-02-09","arxiv_id":"2102.04848","n_code_links":0,"syntology":null},{"paper":"/paper/spike-based-residual-blocks","slug":"spike-based-residual-blocks","title":"Deep Residual Learning in Spiking Neural Networks","date":"2021-02-08","arxiv_id":"2102.04159","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":3,"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) · 1 unverified","official":{"repos":["fangwei123456/Spike-Element-Wise-ResNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]}}},{"paper":"/paper/self-supervised-driven-consistency-training","slug":"self-supervised-driven-consistency-training","title":"Self-supervised driven consistency training for annotation efficient histopathology image analysis","date":"2021-02-07","arxiv_id":"2102.03897","n_code_links":2,"syntology":null},{"paper":null,"slug":"convolutional-neural-network-interpretability","title":"Convolutional Neural Network Interpretability with General Pattern Theory","date":"2021-02-05","arxiv_id":"2102.04247","n_code_links":0,"syntology":null},{"paper":"/paper/gnn-rl-compression-topology-aware-network","slug":"gnn-rl-compression-topology-aware-network","title":"Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning","date":"2021-02-05","arxiv_id":"2102.03214","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yusx-swapp/gnn-rl-model-compression"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/on-the-connection-of-benford-s-law-and-neural","slug":"on-the-connection-of-benford-s-law-and-neural","title":"Rethinking Neural Networks With Benford's Law","date":"2021-02-05","arxiv_id":"2102.03313","n_code_links":1,"syntology":null},{"paper":"/paper/ml-doctor-holistic-risk-assessment-of","slug":"ml-doctor-holistic-risk-assessment-of","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","date":"2021-02-04","arxiv_id":"2102.02551","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["liuyugeng/ml-doctor"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"deep-cnns-for-large-scale-species","title":"Deep CNNs for large scale species classification","date":"2021-02-03","arxiv_id":"2102.01863","n_code_links":0,"syntology":null},{"paper":"/paper/anomalous-event-recognition-in-videos-based","slug":"anomalous-event-recognition-in-videos-based","title":"Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures","date":"2021-02-02","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"face-recognition-using-sf-3-cnn-with-higher","title":"Face Recognition Using $Sf_{3}CNN$ With Higher Feature Discrimination","date":"2021-02-02","arxiv_id":"2102.01404","n_code_links":0,"syntology":null},{"paper":"/paper/classification-of-fracture-and-normal","slug":"classification-of-fracture-and-normal","title":"Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models","date":"2021-01-31","arxiv_id":"2102.00515","n_code_links":0,"syntology":null},{"paper":"/paper/re-reproducibility-report-of-interpretable","slug":"re-reproducibility-report-of-interpretable","title":"[Re] Reproducibility report of \"Interpretable Complex-Valued Neural Networks for Privacy Protection\"","date":"2021-01-31","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-model-compression-based-on-the-training","title":"Deep Model Compression based on the Training History","date":"2021-01-30","arxiv_id":"2102.00160","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-deep-learning-analysis-of","title":"Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease","date":"2021-01-29","arxiv_id":"2101.12505","n_code_links":0,"syntology":null},{"paper":"/paper/capsnet-regularization-and-its-conjugation","slug":"capsnet-regularization-and-its-conjugation","title":"CapsNet Regularization and its Conjugation with ResNet for Signature Identification","date":"2021-01-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"wavelet-denoised-resnet-cnn-and-lightgbm","title":"Wavelet Denoised-ResNet CNN and LightGBM Method to Predict Forex Rate of Change","date":"2021-01-29","arxiv_id":"2102.04861","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-cross-image-pixel-contrast-for","slug":"exploring-cross-image-pixel-contrast-for","title":"Exploring Cross-Image Pixel Contrast for Semantic Segmentation","date":"2021-01-28","arxiv_id":"2101.11939","n_code_links":5,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["tfzhou/ContrastiveSeg"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/malware-detection-using-frequency-domain","slug":"malware-detection-using-frequency-domain","title":"Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning","date":"2021-01-26","arxiv_id":"2101.10578","n_code_links":1,"syntology":null},{"paper":null,"slug":"3d-u-net-for-segmentation-of-covid-19","title":"3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware","date":"2021-01-25","arxiv_id":"2101.09976","n_code_links":0,"syntology":null},{"paper":"/paper/densenet-for-breast-tumor-classification-in","slug":"densenet-for-breast-tumor-classification-in","title":"DenseNet for Breast Tumor Classification in Mammographic Images","date":"2021-01-24","arxiv_id":"2101.09637","n_code_links":2,"syntology":null},{"paper":null,"slug":"expression-recognition-analysis-in-the-wild","title":"Expression Recognition Analysis in the Wild","date":"2021-01-22","arxiv_id":"2101.09231","n_code_links":0,"syntology":null},{"paper":"/paper/daf-re-a-challenging-crowd-sourced-large","slug":"daf-re-a-challenging-crowd-sourced-large","title":"DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition","date":"2021-01-21","arxiv_id":"2101.08674","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["arkel23/animesion"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"variance-based-samples-weighting-for","title":"Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning","date":"2021-01-19","arxiv_id":"2101.07561","n_code_links":0,"syntology":null},{"paper":null,"slug":"tlu-net-a-deep-learning-approach-for","title":"TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection","date":"2021-01-18","arxiv_id":"2101.06915","n_code_links":0,"syntology":null},{"paper":null,"slug":"cost-efficient-online-hyperparameter","title":"Cost-Efficient Online Hyperparameter Optimization","date":"2021-01-17","arxiv_id":"2101.06590","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-representation-learning-from-3","title":"Self-Supervised Representation Learning from Flow Equivariance","date":"2021-01-16","arxiv_id":"2101.06553","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multiple-classifier-approach-for","title":"A Multiple Classifier Approach for Concatenate-Designed Neural Networks","date":"2021-01-14","arxiv_id":"2101.05457","n_code_links":0,"syntology":null},{"paper":"/paper/fabricnet-a-fiber-recognition-architecture","slug":"fabricnet-a-fiber-recognition-architecture","title":"FabricNet: A Fiber Recognition Architecture Using Ensemble ConvNets","date":"2021-01-14","arxiv_id":"2101.05564","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-prediction-of-alzheimer-s","title":"Deep learning based prediction of Alzheimer's disease from magnetic resonance images","date":"2021-01-13","arxiv_id":"2101.04961","n_code_links":0,"syntology":null},{"paper":"/paper/neural-sequence-to-grid-module-for-learning","slug":"neural-sequence-to-grid-module-for-learning","title":"Neural Sequence-to-grid Module for Learning Symbolic Rules","date":"2021-01-13","arxiv_id":"2101.04921","n_code_links":1,"syntology":null},{"paper":null,"slug":"accuracy-and-architecture-studies-of-residual","title":"Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations","date":"2021-01-10","arxiv_id":"2101.03583","n_code_links":0,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-with-function","slug":"deep-reinforcement-learning-with-function","title":"Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies","date":"2021-01-09","arxiv_id":"2101.03418","n_code_links":1,"syntology":null},{"paper":null,"slug":"residual-networks-classify-inputs-based-on-1","title":"Residual networks classify inputs based on their neural transient dynamics","date":"2021-01-08","arxiv_id":"2101.03009","n_code_links":0,"syntology":null},{"paper":"/paper/end-2-end-covid-19-detection-from-breath","slug":"end-2-end-covid-19-detection-from-breath","title":"End-2-End COVID-19 Detection from Breath & Cough Audio","date":"2021-01-07","arxiv_id":"2102.08359","n_code_links":2,"syntology":null},{"paper":"/paper/facial-expression-recognition-in-the-wild-via","slug":"facial-expression-recognition-in-the-wild-via","title":"Facial Expression Recognition in the Wild via Deep Attentive Center Loss","date":"2021-01-07","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"cap-context-aware-pruning-for-semantic","title":"CAP-Context-Aware-Pruning-for-Semantic-Segmentation","date":"2021-01-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cap-context-aware-pruning-for-semantic-1","title":"CAP: Context-Aware Pruning for Semantic-Segmentation","date":"2021-01-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/industrial-image-anomaly-localization-based","slug":"industrial-image-anomaly-localization-based","title":"Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature","date":"2021-01-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"isetauto-detecting-vehicles-with-depth-and","title":"ISETAuto: Detecting vehicles with depth and radiance information","date":"2021-01-06","arxiv_id":"2101.01843","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-automatic-system-to-monitor-the-physical","title":"An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic","date":"2021-01-05","arxiv_id":"2101.01373","n_code_links":0,"syntology":null},{"paper":"/paper/one-shot-model-for-the-prediction-of-covid-19","slug":"one-shot-model-for-the-prediction-of-covid-19","title":"One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features","date":"2021-01-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/few-shot-image-classification-just-use-a","slug":"few-shot-image-classification-just-use-a","title":"Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier","date":"2021-01-03","arxiv_id":"2101.00562","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["arjish/PreTrainedFullLibrary_FewShot"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/regnet-self-regulated-network-for-image","slug":"regnet-self-regulated-network-for-image","title":"RegNet: Self-Regulated Network for Image Classification","date":"2021-01-03","arxiv_id":"2101.00590","n_code_links":14,"syntology":null},{"paper":null,"slug":"a-block-minifloat-representation-for-training","title":"A Block Minifloat Representation for Training Deep Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"aggregation-with-feature-detection","title":"Aggregation With Feature Detection","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-artificial-intelligence-system-for-1","title":"An Artificial Intelligence System for Combined Fruit Detection and Georeferencing, Using RTK-Based Perspective Projection in Drone Imagery","date":"2021-01-01","arxiv_id":"2101.00339","n_code_links":0,"syntology":null},{"paper":null,"slug":"beyond-covid-19-diagnosis-prognosis-with","title":"Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"consistent-instance-classification-for","title":"Consistent Instance Classification for Unsupervised Representation Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"constructing-multiple-high-quality-deep","title":"Constructing Multiple High-Quality Deep Neural Networks: A TRUST-TECH Based Approach","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"diet-snn-a-low-latency-spiking-neural-network","title":"DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"differentiable-dynamic-wirings-for-neural","title":"Differentiable Dynamic Wirings for Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"evidence-against-implicitly-recurrent","title":"Evidence against implicitly recurrent computations in residual neural networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-training-of-contrastive-learning-with","title":"Fast Training of Contrastive Learning with Intermediate Contrastive Loss","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hw-nas-bench-hardware-aware-neural","title":"HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"inhibition-augmented-convnets","title":"Inhibition-augmented ConvNets","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"making-coherence-out-of-nothing-at-all-1","title":"Making Coherence Out of Nothing At All: Measuring Evolution of Gradient Alignment","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/model-free-energy-distance-for-pruning-dnns","slug":"model-free-energy-distance-for-pruning-dnns","title":"Model-Free Energy Distance for Pruning DNNs","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-representation-ensemble-in-few-shot","title":"Multi-Representation Ensemble in Few-Shot Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-networks-preserve-invertibility-across","title":"Neural Networks Preserve Invertibility Across Iterations: A Possible Source of Implicit Data Augmentation","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"phew-paths-with-higher-edge-weights-give","title":"PHEW: Paths with Higher Edge-Weights give ''winning tickets'' without training data","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"practical-locally-private-federated-learning","title":"Practical Locally Private Federated Learning with Communication Efficiency","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"recycling-sub-optimial-hyperparameter","title":"Recycling sub-optimial Hyperparameter Optimization models to generate efficient Ensemble Deep Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/sedona-search-for-decoupled-neural-networks","slug":"sedona-search-for-decoupled-neural-networks","title":"SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning","date":"2021-01-01","arxiv_id":null,"n_code_links":1,"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":"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":"/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":"/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":"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":"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":"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/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":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":"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":"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":"/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":"object-detection-based-on-ocsafpn-in-aerial","title":"Object Detection based on OcSaFPN in Aerial Images with Noise","date":"2020-12-18","arxiv_id":"2012.09859","n_code_links":0,"syntology":null},{"paper":"/paper/sceneformer-indoor-scene-generation-with","slug":"sceneformer-indoor-scene-generation-with","title":"SceneFormer: Indoor Scene Generation with Transformers","date":"2020-12-17","arxiv_id":"2012.09793","n_code_links":2,"syntology":null},{"paper":null,"slug":"objective-based-hierarchical-clustering-of","title":"Objective-Based Hierarchical Clustering of Deep Embedding Vectors","date":"2020-12-15","arxiv_id":"2012.08466","n_code_links":0,"syntology":null},{"paper":null,"slug":"user-friendly-automatic-transcription-of-low","title":"User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis","date":"2020-12-15","arxiv_id":"2101.03027","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantizing-data-for-distributed-learning","title":"Quantizing data for distributed learning","date":"2020-12-14","arxiv_id":"2012.07913","n_code_links":0,"syntology":null}],"record_sha256":"f1c399c546b8c03255ce1b9e4361c80f17e95b3eb93f53c1b3562dd3c2ae23d9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}