{"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/78","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":78,"pages_in_order":95,"rows_per_page":100,"rows":[7701,7800],"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/77","next":"/task/deep-learning/papers/79","papers":[{"url":null,"slug":"deep-learning-at-scale-for-subgrid-modeling","title":"Deep learning at scale for subgrid modeling in turbulent flows","date":"2019-10-01","arxiv_id":"1910.00928","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-light-field-saliency","title":"Deep Learning for Light Field Saliency Detection","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-seeing-through-window-with","title":"Deep Learning for Seeing Through Window With Raindrops","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ngemm-optimizing-gemm-for-deep-learning-via","title":"NGEMM: Optimizing GEMM for Deep Learning via Compiler-based Techniques","date":"2019-10-01","arxiv_id":"1910.00178","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-error-analysis-for-the-training-of-deep","title":"Full error analysis for the training of deep neural networks","date":"2019-09-30","arxiv_id":"1910.00121","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-deep-learning-based-prediction","title":"Evaluation of Deep Learning-based prediction models in Microgrids","date":"2019-09-29","arxiv_id":"1910.00500","repositories_listed":0,"syntology":null},{"url":null,"slug":"unfolding-the-structure-of-a-document-using","title":"Unfolding the Structure of a Document using Deep Learning","date":"2019-09-29","arxiv_id":"1910.03678","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-k-svd-denoising","title":"Deep K-SVD Denoising","date":"2019-09-28","arxiv_id":"1909.13164","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-states-for-atari-agents-via","title":"Counterfactual States for Atari Agents via Generative Deep Learning","date":"2019-09-27","arxiv_id":"1909.12969","repositories_listed":0,"syntology":null},{"url":null,"slug":"membership-encoding-for-deep-learning","title":"Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning","date":"2019-09-27","arxiv_id":"1909.12982","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-deep-learning-architecture-for-2","title":"A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification","date":"2019-09-26","arxiv_id":"1909.11866","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-and-random-forest-based","title":"Deep Learning and Random Forest-Based Augmentation of sRNA Expression Profiles","date":"2019-09-26","arxiv_id":"1909.11943","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-polar-code-design","title":"Deep Learning-based Polar Code Design","date":"2019-09-26","arxiv_id":"1909.12035","repositories_listed":0,"syntology":null},{"url":null,"slug":"exascale-deep-learning-to-accelerate-cancer","title":"Exascale Deep Learning to Accelerate Cancer Research","date":"2019-09-26","arxiv_id":"1909.12291","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-deep-learning-for-augmentation-of","title":"Explainable Deep Learning for Augmentation of sRNA Expression Profiles","date":"2019-09-26","arxiv_id":"1909.11956","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dynamic-approach-to-accelerate-deep","title":"A Dynamic Approach to Accelerate Deep Learning Training","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-kolmogorov-complexity-approach-to","title":"A Kolmogorov Complexity Approach to Generalization in Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mechanism-of-implicit-regularization-in","title":"A Mechanism of Implicit Regularization in Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-perspective-in-understanding-of-adam","title":"A new perspective in understanding of Adam-Type algorithms and beyond","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"amortized-nesterov-s-momentum-robust-and","title":"Amortized Nesterov's Momentum: Robust and Lightweight Momentum for Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-optimization-principle-of-deep-learning","title":"An Optimization Principle Of Deep Learning?","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"credible-sample-elicitation-by-deep-learning-1","title":"Credible Sample Elicitation by Deep Learning, for Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-breast-ct-for-radiation","title":"Deep-learning-based Breast CT for Radiation Dose Reduction","date":"2019-09-25","arxiv_id":"1909.11721","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-deepfakes-creation-and","title":"Deep Learning for Deepfakes Creation and Detection: A Survey","date":"2019-09-25","arxiv_id":"1909.11573","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-symbolic-regression","title":"Deep symbolic regression","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deepagrel-biologically-plausible-deep","title":"DeepAGREL: Biologically plausible deep learning via direct reinforcement","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangling-trainability-and-generalization","title":"Disentangling Trainability and Generalization in Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evidence-aware-entropy-decomposition-for","title":"Evidence-Aware Entropy Decomposition For Active Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-deep-learning-dynamics-with","title":"Forecasting Deep Learning Dynamics with Applications to Hyperparameter Tuning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-underlying-physical-properties-from","title":"Learning Underlying Physical Properties From Observations For Trajectory Prediction","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"near-zero-cost-differentially-private-deep","title":"Near-Zero-Cost Differentially Private Deep Learning with Teacher Ensembles","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-tunability-of-optimizers-in-deep-1","title":"On the Tunability of Optimizers in Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sgd-with-hardness-weighted-sampling-for","title":"SGD with Hardness Weighted Sampling for Distributionally Robust Deep Learning","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"software-engineering-meets-deep-learning-a","title":"Software Engineering Meets Deep Learning: A Mapping Study","date":"2019-09-25","arxiv_id":"1909.11436","repositories_listed":0,"syntology":null},{"url":null,"slug":"stock-prices-prediction-using-deep-learning","title":"Stock Prices Prediction using Deep Learning Models","date":"2019-09-25","arxiv_id":"1909.12227","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-for-deep-learning","title":"Synthetic Data for Deep Learning","date":"2019-09-25","arxiv_id":"1909.11512","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-vs-real-deep-learning-on-controlled","title":"Synthetic vs Real: Deep Learning on Controlled Noise","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-the-pathology-lab-an-intelligent","title":"Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World","date":"2019-09-24","arxiv_id":"1909.11212","repositories_listed":0,"syntology":null},{"url":null,"slug":"exascale-deep-learning-for-scientific-inverse","title":"Exascale Deep Learning for Scientific Inverse Problems","date":"2019-09-24","arxiv_id":"1909.11150","repositories_listed":0,"syntology":null},{"url":null,"slug":"pista-sense-resnet-for-parallel-mri","title":"pISTA-SENSE-ResNet for Parallel MRI Reconstruction","date":"2019-09-24","arxiv_id":"1910.00650","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910416","title":"Biomedical Mention Disambiguation using a Deep Learning Approach","date":"2019-09-23","arxiv_id":"1909.10416","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910473","title":"Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and \"transfer learning\" technique","date":"2019-09-23","arxiv_id":"1909.10473","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-ultrasound-beamforming-using-deep","title":"Adaptive Ultrasound Beamforming using Deep Learning","date":"2019-09-23","arxiv_id":"1909.10342","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-matrix-a-deep-learning-benchmark-for","title":"AI Matrix: A Deep Learning Benchmark for Alibaba Data Centers","date":"2019-09-23","arxiv_id":"1909.10562","repositories_listed":0,"syntology":null},{"url":null,"slug":"compiler-level-matrix-multiplication","title":"Compiler-Level Matrix Multiplication Optimization for Deep Learning","date":"2019-09-23","arxiv_id":"1909.10616","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910084","title":"mlVIRNET: Multilevel Variational Image Registration Network","date":"2019-09-22","arxiv_id":"1909.10084","repositories_listed":0,"syntology":null},{"url":null,"slug":"190909837","title":"Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks","date":"2019-09-21","arxiv_id":"1909.09837","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-lightweight-deep-learning-model-for-human","title":"A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices","date":"2019-09-20","arxiv_id":"1909.12917","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-architectures-for-automated","title":"Deep learning architectures for automated image segmentation","date":"2019-09-19","arxiv_id":"1909.10333","repositories_listed":0,"syntology":null},{"url":null,"slug":"slices-of-attention-in-asynchronous-video-job","title":"Slices of Attention in Asynchronous Video Job Interviews","date":"2019-09-19","arxiv_id":"1909.08845","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantitative-impact-of-label-noise-on-the","title":"Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans","date":"2019-09-18","arxiv_id":"1909.08959","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-deep-learning-approach-for-diagnosis","title":"A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease","date":"2019-09-17","arxiv_id":"1909.07587","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-precoding-for-the-mimo","title":"Deep Learning based Precoding for the MIMO Gaussian Wiretap Channel","date":"2019-09-17","arxiv_id":"1909.07963","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-tanh-hardware-efficient-activations-for","title":"K-TanH: Efficient TanH For Deep Learning","date":"2019-09-17","arxiv_id":"1909.07729","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-estimate-labels-uncertainty-for","title":"Learn to Estimate Labels Uncertainty for Quality Assurance","date":"2019-09-17","arxiv_id":"1909.08058","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-sbl-a-deep-learning-architecture-for","title":"Learned-SBL: A Deep Learning Architecture for Sparse Signal Recovery","date":"2019-09-17","arxiv_id":"1909.08185","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathdl-mathematical-deep-learning-for-d3r","title":"MathDL: Mathematical deep learning for D3R Grand Challenge 4","date":"2019-09-17","arxiv_id":"1909.07784","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-pediatric-vascular-anomalies-with","title":"Identifying Pediatric Vascular Anomalies With Deep Learning","date":"2019-09-16","arxiv_id":"1909.07046","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-discovery-in-nanophotonics-using","title":"Knowledge Discovery In Nanophotonics Using Geometric Deep Learning","date":"2019-09-16","arxiv_id":"1909.07330","repositories_listed":0,"syntology":null},{"url":null,"slug":"unaligned-sequence-similarity-search-using","title":"Unaligned Sequence Similarity Search Using Deep Learning","date":"2019-09-16","arxiv_id":"1909.06929","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-and-power-evaluation-of-ai","title":"Benchmarking the Performance and Energy Efficiency of AI Accelerators for AI Training","date":"2019-09-15","arxiv_id":"1909.06842","repositories_listed":0,"syntology":null},{"url":null,"slug":"ffdl-a-flexible-multi-tenant-deep-learning","title":"FfDL : A Flexible Multi-tenant Deep Learning Platform","date":"2019-09-14","arxiv_id":"1909.06526","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-superpixel-driven-deep-learning-approach","title":"A superpixel-driven deep learning approach for the analysis of dermatological wounds","date":"2019-09-13","arxiv_id":"1909.06264","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-cooperative-radio-signal","title":"Deep Learning for Cooperative Radio Signal Classification","date":"2019-09-13","arxiv_id":"1909.06031","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancement-of-seismic-imaging-an-innovative","title":"Enhancement of seismic imaging: An innovative deep learning approach","date":"2019-09-13","arxiv_id":"1909.06016","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-forecasting-of-heat-waves","title":"High Resolution Forecasting of Heat Waves impacts on Leaf Area Index by Multiscale Multitemporal Deep Learning","date":"2019-09-13","arxiv_id":"1909.07786","repositories_listed":0,"syntology":null},{"url":null,"slug":"horizontal-flows-and-manifold-stochastics-in","title":"Horizontal Flows and Manifold Stochastics in Geometric Deep Learning","date":"2019-09-13","arxiv_id":"1909.06397","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-aware-planning-by-confidence-estimation","title":"Risk-Aware Planning by Confidence Estimation using Deep Learning-Based Perception","date":"2019-09-13","arxiv_id":"1910.00101","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectrum-sensing-based-on-deep-learning","title":"Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios","date":"2019-09-13","arxiv_id":"1909.06020","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-multilingual-user-feedback-using","title":"Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning","date":"2019-09-12","arxiv_id":"1909.05504","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognition-of-handwritten-digit-using","title":"Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers","date":"2019-09-12","arxiv_id":"1909.08490","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-system-for-differential","title":"A deep learning system for differential diagnosis of skin diseases","date":"2019-09-11","arxiv_id":"1909.05382","repositories_listed":0,"syntology":null},{"url":null,"slug":"scienet-deep-learning-with-spike-assisted","title":"ScieNet: Deep Learning with Spike-assisted Contextual Information Extraction","date":"2019-09-11","arxiv_id":"1909.05314","repositories_listed":0,"syntology":null},{"url":null,"slug":"structural-robustness-for-deep-learning","title":"Structural Robustness for Deep Learning Architectures","date":"2019-09-11","arxiv_id":"1909.05095","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-deep-learning-for-network-traffic","title":"A Study of Deep Learning for Network Traffic Data Forecasting","date":"2019-09-10","arxiv_id":"1909.04501","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-hip-fracture-identification-and","title":"Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning","date":"2019-09-10","arxiv_id":"1909.06326","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-automated-classification","title":"Deep Learning for Automated Classification and Characterization of Amorphous Materials","date":"2019-09-10","arxiv_id":"1909.04648","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-regression-of-vlsi-plasma-etch","title":"Deep Learning Regression of VLSI Plasma Etch Metrology","date":"2019-09-10","arxiv_id":"1910.10067","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-and-video-games","title":"Reinforcement Learning and Video Games","date":"2019-09-10","arxiv_id":"1909.04751","repositories_listed":0,"syntology":null},{"url":null,"slug":"swapped-face-detection-using-deep-learning","title":"Swapped Face Detection using Deep Learning and Subjective Assessment","date":"2019-09-10","arxiv_id":"1909.04217","repositories_listed":0,"syntology":null},{"url":null,"slug":"techniques-all-classifiers-can-learn-from","title":"A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization","date":"2019-09-10","arxiv_id":"1909.04791","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-deep-learning-with-event","title":"Distributed Deep Learning with Event-Triggered Communication","date":"2019-09-08","arxiv_id":"1909.05020","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-bidirectional-transformer","title":"Multi-Task Bidirectional Transformer Representations for Irony Detection","date":"2019-09-08","arxiv_id":"1909.03526","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-deep-learning-for-video","title":"Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations","date":"2019-09-07","arxiv_id":"1909.05667","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-sanity-check-for-deep-learning-systems","title":"Data Sanity Check for Deep Learning Systems via Learnt Assertions","date":"2019-09-06","arxiv_id":"1909.03835","repositories_listed":0,"syntology":null},{"url":null,"slug":"mass-personalization-of-deep-learning","title":"Personalization of Deep Learning","date":"2019-09-06","arxiv_id":"1909.02803","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-surveillance-of-highway-traffic-events","title":"Video Surveillance of Highway Traffic Events by Deep Learning Architectures","date":"2019-09-06","arxiv_id":"1909.12235","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversely-stale-parameters-for-efficient","title":"On the Acceleration of Deep Learning Model Parallelism with Staleness","date":"2019-09-05","arxiv_id":"1909.02625","repositories_listed":0,"syntology":null},{"url":null,"slug":"alime-autoencoder-based-approach-for-local","title":"ALIME: Autoencoder Based Approach for Local Interpretability","date":"2019-09-04","arxiv_id":"1909.02437","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-aided-tabu-search-detection-for","title":"Deep Learning-Aided Tabu Search Detection for Large MIMO Systems","date":"2019-09-04","arxiv_id":"1909.01683","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-asset-exchange-path-to-ubiquitous-deep","title":"Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment","date":"2019-09-04","arxiv_id":"1909.01606","repositories_listed":0,"syntology":null},{"url":null,"slug":"quasi-newton-optimization-methods-for-deep","title":"Quasi-Newton Optimization Methods For Deep Learning Applications","date":"2019-09-04","arxiv_id":"1909.01994","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-loss-functions-for-supervised-monaural","title":"On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement","date":"2019-09-03","arxiv_id":"1909.01019","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-network-component-for-knowledge","title":"A Neural Network Component for Knowledge-Based Semantic Representations of Text","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-evaluation-of-neural-machine","title":"Human Evaluation of Neural Machine Translation: The Case of Deep Learning","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-complex-word-identification","title":"Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-reinforcement-learning-based-neural","title":"Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research","date":"2019-09-01","arxiv_id":"1909.00311","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-scenario-reduction-for-power-systems-by","title":"Fast Scenario Reduction for Power Systems by Deep Learning","date":"2019-08-30","arxiv_id":"1908.11486","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-consumer-default-a-deep-learning","title":"Predicting Consumer Default: A Deep Learning Approach","date":"2019-08-30","arxiv_id":"1908.11498","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-image-watermarking-system-based-on","title":"A Robust Image Watermarking System Based on Deep Neural Networks","date":"2019-08-29","arxiv_id":"1908.11331","repositories_listed":0,"syntology":null}],"record_sha256":"825224cdb6558f06bf5624ebbe05d6182674ba2b4c980fd30b27703619005e9d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}