{"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":"/task/deep-learning/papers/67","list_of":"/task/deep-learning","task":"Deep Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":67,"pages_in_order":95,"rows_per_page":100,"rows":[6601,6700],"of":9423,"counts":{"archive_papers_tagged":9423,"with_a_code_link":2693,"where_syntology_ran_a_sample":410,"not_listed_spam_title":0,"listed":9423,"listed_where_code_ran":410,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":356,"every_run_a_failure_of_syntologys_instrument":54,"listed_with_a_run_with_no_instrument_failure":356,"listed_every_run_a_failure_of_syntologys_instrument":54,"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":"/task/deep-learning","prev":"/task/deep-learning/papers/66","next":"/task/deep-learning/papers/68","papers":[{"url":null,"slug":"data-driven-rogue-waves-and-parameter","title":"Data-driven rogue waves and parameter discovery in the defocusing NLS equation with a potential using the PINN deep learning","date":"2020-12-18","arxiv_id":"2012.09984","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-high-harmonic-generation","title":"Deep learning and high harmonic generation","date":"2020-12-18","arxiv_id":"2012.10328","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-techniques-for-super-resolution","title":"Deep Learning Techniques for Super-Resolution in Video Games","date":"2020-12-17","arxiv_id":"2012.09810","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-deep-object-tracking-with-circular","title":"End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box","date":"2020-12-17","arxiv_id":"2012.09771","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-ensemble-knowledge","title":"Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning","date":"2020-12-17","arxiv_id":"2012.09816","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-deep-learning-based-myocardial","title":"Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software","date":"2020-12-16","arxiv_id":"2012.09070","repositories_listed":0,"syntology":null},{"url":null,"slug":"physical-deep-learning-based-on-optimal","title":"Physical deep learning based on optimal control of dynamical systems","date":"2020-12-16","arxiv_id":"2012.08761","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-generalization-in-deep-learning-1","title":"Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task","date":"2020-12-16","arxiv_id":"2012.09117","repositories_listed":0,"syntology":null},{"url":null,"slug":"temimagenet-and-atomsegnet-deep-learning","title":"TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images","date":"2020-12-16","arxiv_id":"2012.09093","repositories_listed":0,"syntology":null},{"url":null,"slug":"ubam-unsupervised-behavior-analysis-and","title":"Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning","date":"2020-12-16","arxiv_id":"2012.09237","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-classification-of","title":"Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning","date":"2020-12-15","arxiv_id":"2012.08406","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-deep-learning","title":"Model-Based Deep Learning","date":"2020-12-15","arxiv_id":"2012.08405","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-estimation-with-deep-learning-for","title":"Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling","date":"2020-12-15","arxiv_id":"2012.14295","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomechanical-modelling-of-brain-atrophy","title":"Biomechanical modelling of brain atrophy through deep learning","date":"2020-12-14","arxiv_id":"2012.07596","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-material-recognition-most","title":"Deep Learning for Material recognition: most recent advances and open challenges","date":"2020-12-14","arxiv_id":"2012.07495","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphs-for-deep-learning-representations","title":"Graphs for deep learning representations","date":"2020-12-14","arxiv_id":"2012.07439","repositories_listed":0,"syntology":null},{"url":null,"slug":"neurips-2020-competition-predicting","title":"NeurIPS 2020 Competition: Predicting Generalization in Deep Learning","date":"2020-12-14","arxiv_id":"2012.07976","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-generalization-in-deep-learning","title":"Predicting Generalization in Deep Learning via Local Measures of Distortion","date":"2020-12-13","arxiv_id":"2012.06969","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-filter-size-effect-in-deep","title":"Analysis of Filter Size Effect In Deep Learning","date":"2020-12-12","arxiv_id":"2101.01115","repositories_listed":0,"syntology":null},{"url":null,"slug":"filtering-ddos-attacks-from-unlabeled-network","title":"Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning","date":"2020-12-12","arxiv_id":"2012.06805","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-deep-learning-models-to-predict","title":"Building Deep Learning Models to Predict Mortality in ICU Patients","date":"2020-12-11","arxiv_id":"2012.07585","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-approach-for-matrix-completion","title":"Deep Learning Approach for Matrix Completion Using Manifold Learning","date":"2020-12-11","arxiv_id":"2012.06063","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-kinematic-reconstruction","title":"Deep-Learning-Based Kinematic Reconstruction for DUNE","date":"2020-12-11","arxiv_id":"2012.06181","repositories_listed":0,"syntology":null},{"url":null,"slug":"parallelized-rate-distortion-optimized","title":"Parallelized Rate-Distortion Optimized Quantization Using Deep Learning","date":"2020-12-11","arxiv_id":"2012.06380","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-deep-learning-for-individualized","title":"Unsupervised deep learning for individualized brain functional network identification","date":"2020-12-11","arxiv_id":"2012.06494","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-generation-of-interpretable-lung","title":"Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images","date":"2020-12-10","arxiv_id":"2012.05447","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-methods-for-sar-image","title":"Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives","date":"2020-12-10","arxiv_id":"2012.05508","repositories_listed":0,"syntology":null},{"url":null,"slug":"effect-of-the-regularization-hyperparameter","title":"Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI","date":"2020-12-10","arxiv_id":"2012.05661","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-deep-neural-networks-and-transfer","title":"Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets","date":"2020-12-10","arxiv_id":"2012.06025","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-deep-learning-techniques-for","title":"Generative Deep Learning Techniques for Password Generation","date":"2020-12-10","arxiv_id":"2012.05685","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-and-time-reversible-cellular","title":"Commutative Evolution Laws in Holographic Cellular Automata: AdS/CFT, Near-Extremal D3-Branes, and a Deep Learning Approach","date":"2020-12-10","arxiv_id":"2012.06441","repositories_listed":0,"syntology":null},{"url":null,"slug":"notes-on-deep-learning-theory","title":"Notes on Deep Learning Theory","date":"2020-12-10","arxiv_id":"2012.05760","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-based-budget-active-learning-for-deep","title":"Cost-Based Budget Active Learning for Deep Learning","date":"2020-12-09","arxiv_id":"2012.05196","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-climate-model-output","title":"Deep Learning for Climate Model Output Statistics","date":"2020-12-09","arxiv_id":"2012.10394","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-deep-learning-models-for-1","title":"Evaluation of Deep Learning Models for Kannada Handwritten Digit Recognition","date":"2020-12-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-consistent-deep-learning-for","title":"Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization","date":"2020-12-09","arxiv_id":"2012.05359","repositories_listed":0,"syntology":null},{"url":null,"slug":"artefact-removal-in-ground-truth-deficient","title":"Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning","date":"2020-12-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-clustering-of-continuous-1","title":"Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates","date":"2020-12-08","arxiv_id":"2012.04381","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-learning-regression-for","title":"Interpretable deep learning regression for breast density estimation on MRI","date":"2020-12-08","arxiv_id":"2012.04336","repositories_listed":0,"syntology":null},{"url":null,"slug":"knn-enhanced-deep-learning-against-noisy","title":"KNN-enhanced Deep Learning Against Noisy Labels","date":"2020-12-08","arxiv_id":"2012.04224","repositories_listed":0,"syntology":null},{"url":null,"slug":"cycleqsm-unsupervised-qsm-deep-learning-using","title":"CycleQSM: Unsupervised QSM Deep Learning using Physics-Informed CycleGAN","date":"2020-12-07","arxiv_id":"2012.03842","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-methods-for-credit-card-fraud","title":"Deep Learning Methods for Credit Card Fraud Detection","date":"2020-12-07","arxiv_id":"2012.03754","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformable-gabor-feature-networks-for","title":"Deformable Gabor Feature Networks for Biomedical Image Classification","date":"2020-12-07","arxiv_id":"2012.04109","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-bounds-for-deep-learning","title":"Generalization bounds for deep learning","date":"2020-12-07","arxiv_id":"2012.04115","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-based-adaptive-deep-learning-for-entity","title":"Adaptive Deep Learning for Entity Resolution by Risk Analysis","date":"2020-12-07","arxiv_id":"2012.03513","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotation-invariant-point-convolution-with","title":"Rotation-Invariant Point Convolution With Multiple Equivariant Alignments","date":"2020-12-07","arxiv_id":"2012.04048","repositories_listed":0,"syntology":null},{"url":null,"slug":"benefit-of-deep-learning-with-non-convex-1","title":"Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods","date":"2020-12-06","arxiv_id":"2012.03224","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-deep-learning-with-noisy-labels","title":"A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?","date":"2020-12-05","arxiv_id":"2012.03061","repositories_listed":0,"syntology":null},{"url":null,"slug":"fixed-priority-global-scheduling-from-a-deep","title":"Fixed Priority Global Scheduling from a Deep Learning Perspective","date":"2020-12-05","arxiv_id":"2012.03002","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-the-global-workspace-theory","title":"Deep Learning and the Global Workspace Theory","date":"2020-12-04","arxiv_id":"2012.10390","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-medical-anomaly-detection-a","title":"Deep Learning for Medical Anomaly Detection -- A Survey","date":"2020-12-04","arxiv_id":"2012.02364","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-deep-learning-methods-for","title":"Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems","date":"2020-12-04","arxiv_id":"2012.02891","repositories_listed":0,"syntology":null},{"url":null,"slug":"covid-clnet-covid-19-detection-with","title":"COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches","date":"2020-12-03","arxiv_id":"2012.02234","repositories_listed":0,"syntology":null},{"url":null,"slug":"creativity-of-deep-learning-conceptualization","title":"Creativity of Deep Learning: Conceptualization and Assessment","date":"2020-12-03","arxiv_id":"2012.02282","repositories_listed":0,"syntology":null},{"url":null,"slug":"remix-calibrated-resampling-for-class","title":"ReMix: Calibrated Resampling for Class Imbalance in Deep learning","date":"2020-12-03","arxiv_id":"2012.02312","repositories_listed":0,"syntology":null},{"url":null,"slug":"resperfnet-deep-residual-learning-for-1","title":"ResPerfNet: Deep Residual Learning for Regressional Performance Modeling of Deep Neural Networks","date":"2020-12-03","arxiv_id":"2012.01671","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-attention-based-deep-learning","title":"Comparison of Attention-based Deep Learning Models for EEG Classification","date":"2020-12-02","arxiv_id":"2012.01074","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-uncertainty-from-deep-learning-for","title":"Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows","date":"2020-12-02","arxiv_id":"2012.01478","repositories_listed":0,"syntology":null},{"url":null,"slug":"second-order-guarantees-in-federated-learning","title":"Second-Order Guarantees in Federated Learning","date":"2020-12-02","arxiv_id":"2012.01474","repositories_listed":0,"syntology":null},{"url":null,"slug":"speaker-recognition-based-on-deep-learning-an","title":"Speaker Recognition Based on Deep Learning: An Overview","date":"2020-12-02","arxiv_id":"2012.00931","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-similarity-for-deep-learning","title":"A graph similarity for deep learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/autosync-learning-to-synchronize-for-data","slug":"autosync-learning-to-synchronize-for-data","title":"AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cyut-team-chinese-grammatical-error-diagnosis","title":"CYUT Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2020 CGED Shared Task","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-ad-hoc-beamforming-based-on-speaker","title":"Deep Ad-hoc Beamforming Based on Speaker Extraction for Target-Dependent Speech Separation","date":"2020-12-01","arxiv_id":"2012.00403","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-arrhythmia-detection","title":"Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms","date":"2020-12-01","arxiv_id":"2012.00348","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-deep-learning-for-neural-implants","title":"Edge Deep Learning for Neural Implants","date":"2020-12-01","arxiv_id":"2012.00307","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-deep-learning-models-for-linking","title":"Geometric Deep Learning Models for Linking Character Names in Novels","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hate-speech-detection-in-saudi-twittersphere","title":"Hate Speech Detection in Saudi Twittersphere: A Deep Learning Approach","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-fine-tune-deep-neural-networks-in-few","title":"How to fine-tune deep neural networks in few-shot learning?","date":"2020-12-01","arxiv_id":"2012.00204","repositories_listed":0,"syntology":null},{"url":null,"slug":"meg-source-localization-via-deep-learning","title":"MEG Source Localization via Deep Learning","date":"2020-12-01","arxiv_id":"2012.00588","repositories_listed":0,"syntology":null},{"url":null,"slug":"modality-enriched-neural-network-for-metaphor","title":"Modality Enriched Neural Network for Metaphor Detection","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multifaceted-uncertainty-estimation-for-label","title":"Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"normalizing-kalman-filters-for-multivariate","title":"Normalizing Kalman Filters for Multivariate Time Series Analysis","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-universality-of-deep-learning","title":"On the universality of deep learning","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-graph-convolutional-networks-for","title":"Regularized Graph Convolutional Networks for Short Text Classification","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-based-collocation-method-for","title":"A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data","date":"2020-11-30","arxiv_id":"2011.14965","repositories_listed":0,"syntology":null},{"url":null,"slug":"depression-status-estimation-by-deep-learning","title":"Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model","date":"2020-11-30","arxiv_id":"2011.14966","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-self-supervised-fully-convolutional","title":"Fast, Self Supervised, Fully Convolutional Color Normalization of H&E Stained Images","date":"2020-11-30","arxiv_id":"2011.15000","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-via-rate-reduction","title":"Incremental Learning via Rate Reduction","date":"2020-11-30","arxiv_id":"2011.14593","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-biases-for-deep-learning-of-higher","title":"Inductive Biases for Deep Learning of Higher-Level Cognition","date":"2020-11-30","arxiv_id":"2011.15091","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-covariant-and-scale-invariant-gaussian","title":"Scale-covariant and scale-invariant Gaussian derivative networks","date":"2020-11-30","arxiv_id":"2011.14759","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-view-spectral-ct-reconstruction-using","title":"Sparse-View Spectral CT Reconstruction Using Deep Learning","date":"2020-11-30","arxiv_id":"2011.14842","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-auditability-for-fairness-in-deep","title":"Towards Auditability for Fairness in Deep Learning","date":"2020-11-30","arxiv_id":"2012.00106","repositories_listed":0,"syntology":null},{"url":null,"slug":"value-function-based-performance-optimization","title":"Value Function Based Performance Optimization of Deep Learning Workloads","date":"2020-11-30","arxiv_id":"2011.14486","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraining-volume-change-in-learned-image","title":"CNN-based Lung CT Registration with Multiple Anatomical Constraints","date":"2020-11-29","arxiv_id":"2011.14372","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-regularization-prediction","title":"Deep Learning for Regularization Prediction in Diffeomorphic Image Registration","date":"2020-11-28","arxiv_id":"2011.14229","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-cardiovascular-risk-from-national","title":"Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach","date":"2020-11-28","arxiv_id":"2011.14032","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-deep-learning-accelerated-topology","title":"Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials","date":"2020-11-28","arxiv_id":"2011.14177","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-deep-learning-1","title":"A Backward SDE Method for Uncertainty Quantification in Deep Learning","date":"2020-11-28","arxiv_id":"2011.14145","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-classical-mechanics","title":"AdS/Deep-Learning made easy: simple examples","date":"2020-11-27","arxiv_id":"2011.13726","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-descent-for-deep-matrix","title":"Gradient Descent for Deep Matrix Factorization: Dynamics and Implicit Bias towards Low Rank","date":"2020-11-27","arxiv_id":"2011.13772","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-attacks-on-deep-learning-face","title":"Robust Attacks on Deep Learning Face Recognition in the Physical World","date":"2020-11-27","arxiv_id":"2011.13526","repositories_listed":0,"syntology":null},{"url":null,"slug":"trends-in-deep-learning-for-medical","title":"Trends in deep learning for medical hyperspectral image analysis","date":"2020-11-27","arxiv_id":"2011.13974","repositories_listed":0,"syntology":null},{"url":null,"slug":"bringing-ai-to-edge-from-deep-learning-s","title":"Bringing AI To Edge: From Deep Learning's Perspective","date":"2020-11-25","arxiv_id":"2011.14808","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-resource-allocation-for-1","title":"Deep Learning-based Resource Allocation For Device-to-Device Communication","date":"2020-11-25","arxiv_id":"2011.12757","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-based-ode-solver-for-chemical","title":"A deep learning-based ODE solver for chemical kinetics","date":"2020-11-24","arxiv_id":"2012.12654","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-inference-performance-of-deep","title":"Benchmarking Inference Performance of Deep Learning Models on Analog Devices","date":"2020-11-24","arxiv_id":"2011.11840","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-deblurring-for-microscopic-pathology","title":"Blind deblurring for microscopic pathology images using deep learning networks","date":"2020-11-24","arxiv_id":"2011.11879","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-automated-mitral-inflow-doppler","title":"Fully Automated Mitral Inflow Doppler Analysis Using Deep Learning","date":"2020-11-24","arxiv_id":"2011.12429","repositories_listed":0,"syntology":null},{"url":null,"slug":"kshapenet-riemannian-network-on-kendall-shape","title":"KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition","date":"2020-11-24","arxiv_id":"2011.12004","repositories_listed":0,"syntology":null}],"record_sha256":"9d447c69b2b4539dc521517fb55e649ae5ee700787d231ce40e7a1e0f52307d1","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}