{"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/84","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":84,"pages_in_order":95,"rows_per_page":100,"rows":[8301,8400],"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/83","next":"/task/deep-learning/papers/85","papers":[{"url":null,"slug":"fadlfederated-autonomous-deep-learning-for","title":"FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record","date":"2018-11-28","arxiv_id":"1811.11400","repositories_listed":0,"syntology":null},{"url":null,"slug":"algae-detection-using-computer-vision-and","title":"Algae Detection Using Computer Vision and Deep Learning","date":"2018-11-27","arxiv_id":"1811.10847","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-image-stylization-using-deep-fully","title":"Automatic Image Stylization Using Deep Fully Convolutional Networks","date":"2018-11-27","arxiv_id":"1811.10872","repositories_listed":0,"syntology":null},{"url":null,"slug":"mobiface-a-lightweight-deep-learning-face","title":"MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices","date":"2018-11-27","arxiv_id":"1811.11080","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-uncertainty-estimate-for-antibiotic","title":"Reliable uncertainty estimate for antibiotic resistance classification with Stochastic Gradient Langevin Dynamics","date":"2018-11-27","arxiv_id":"1811.11145","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-inference-in-facebook-data","title":"Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications","date":"2018-11-24","arxiv_id":"1811.09886","repositories_listed":0,"syntology":null},{"url":null,"slug":"polar-decoding-on-sparse-graphs-with-deep","title":"Polar Decoding on Sparse Graphs with Deep Learning","date":"2018-11-24","arxiv_id":"1811.09801","repositories_listed":0,"syntology":null},{"url":null,"slug":"black-box-autoregressive-density-estimation","title":"Black-Box Autoregressive Density Estimation for State-Space Models","date":"2018-11-20","arxiv_id":"1811.08337","repositories_listed":0,"syntology":null},{"url":null,"slug":"variance-suppression-balanced-training","title":"Variance Suppression: Balanced Training Process in Deep Learning","date":"2018-11-20","arxiv_id":"1811.08163","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-far-from-automatically-interpreting-deep","title":"How far from automatically interpreting deep learning","date":"2018-11-19","arxiv_id":"1811.07747","repositories_listed":0,"syntology":null},{"url":null,"slug":"past-present-and-future-approaches-using","title":"Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data","date":"2018-11-19","arxiv_id":"1811.07749","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-referenced-deep-learning","title":"Self-Referenced Deep Learning","date":"2018-11-19","arxiv_id":"1811.07598","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-pedestrian-detection-at","title":"Deep Learning based Pedestrian Detection at Distance in Smart Cities","date":"2018-11-18","arxiv_id":"1812.00876","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-with-inaccurate-training-data","title":"Deep Learning with Inaccurate Training Data for Image Restoration","date":"2018-11-18","arxiv_id":"1811.07268","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifiers-based-on-deep-sparse-coding","title":"Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples","date":"2018-11-17","arxiv_id":"1811.07211","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modality-deep-learning-brings-bright","title":"Cross-modality deep learning brings bright-field microscopy contrast to holography","date":"2018-11-17","arxiv_id":"1811.07103","repositories_listed":0,"syntology":null},{"url":null,"slug":"anomaly-detection-using-deep-learning-based","title":"Anomaly Detection using Deep Learning based Image Completion","date":"2018-11-16","arxiv_id":"1811.06861","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-at-supercomputer-scale","title":"Image Classification at Supercomputer Scale","date":"2018-11-16","arxiv_id":"1811.06992","repositories_listed":0,"syntology":null},{"url":null,"slug":"concept-oriented-deep-learning-generative","title":"Concept-Oriented Deep Learning: Generative Concept Representations","date":"2018-11-15","arxiv_id":"1811.06622","repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-lombard-reflex-on-the-performance","title":"Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems","date":"2018-11-15","arxiv_id":"1811.06250","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-training-targets-and-objective-functions","title":"On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement","date":"2018-11-15","arxiv_id":"1811.06234","repositories_listed":0,"syntology":null},{"url":null,"slug":"stable-tensor-neural-networks-for-rapid-deep","title":"Stable Tensor Neural Networks for Rapid Deep Learning","date":"2018-11-15","arxiv_id":"1811.06569","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explainable-deep-learning-for-credit","title":"Towards Explainable Deep Learning for Credit Lending: A Case Study","date":"2018-11-15","arxiv_id":"1811.06471","repositories_listed":0,"syntology":null},{"url":null,"slug":"bandana-using-non-volatile-memory-for-storing","title":"Bandana: Using Non-volatile Memory for Storing Deep Learning Models","date":"2018-11-14","arxiv_id":"1811.05922","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-learning-for-guided","title":"Interpretable deep learning for guided structure-property explorations in photovoltaics","date":"2018-11-14","arxiv_id":"1811.06067","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-orchestrated-empirical-study-on-deep","title":"An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms","date":"2018-11-13","arxiv_id":"1811.05187","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-short-text-matching-with-deep","title":"Cross-lingual Short-text Matching with Deep Learning","date":"2018-11-13","arxiv_id":"1811.05569","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-secure-are-deep-learning-algorithms-from","title":"How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?","date":"2018-11-13","arxiv_id":"1811.05259","repositories_listed":0,"syntology":null},{"url":"/paper/vehicle-re-identification-using-quadruple","slug":"vehicle-re-identification-using-quadruple","title":"Vehicle Re-identification Using Quadruple Directional Deep Learning Features","date":"2018-11-13","arxiv_id":"1811.05163","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-transmitter","title":"Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity","date":"2018-11-12","arxiv_id":"1811.04521","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-versus-classical-regression-for","title":"Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction","date":"2018-11-12","arxiv_id":"1811.04907","repositories_listed":0,"syntology":null},{"url":null,"slug":"focusing-on-the-big-picture-insights-into-a","title":"Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery","date":"2018-11-12","arxiv_id":"1811.04893","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-deep-learning-models-using-causal","title":"Explaining Deep Learning Models using Causal Inference","date":"2018-11-11","arxiv_id":"1811.04376","repositories_listed":0,"syntology":null},{"url":null,"slug":"pedestrian-collision-avoidance-system-pecas-a","title":"Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework","date":"2018-11-11","arxiv_id":"1811.04453","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-approach-for-building-detection","title":"Deep Learning Approach for Building Detection in Satellite Multispectral Imagery","date":"2018-11-10","arxiv_id":"1811.04247","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convergence-theory-for-deep-learning-via","title":"A Convergence Theory for Deep Learning via Over-Parameterization","date":"2018-11-09","arxiv_id":"1811.03962","repositories_listed":0,"syntology":null},{"url":null,"slug":"skeptical-deep-learning-with-distribution","title":"Skeptical Deep Learning with Distribution Correction","date":"2018-11-09","arxiv_id":"1811.03821","repositories_listed":0,"syntology":null},{"url":null,"slug":"activation-functions-comparison-of-trends-in","title":"Activation Functions: Comparison of trends in Practice and Research for Deep Learning","date":"2018-11-08","arxiv_id":"1811.03378","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-deep-learning-for-exoplanet","title":"Bayesian Deep Learning for Exoplanet Atmospheric Retrieval","date":"2018-11-08","arxiv_id":"1811.03390","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-predicts-hip-fracture-using","title":"Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables","date":"2018-11-08","arxiv_id":"1811.03695","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-dense-stereo-matching-for-digital","title":"Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery","date":"2018-11-08","arxiv_id":"1811.03535","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-state-estimation-for-unobservable","title":"Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning","date":"2018-11-07","arxiv_id":"1811.02756","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-deep-learning-models-a-bayesian","title":"Explaining Deep Learning Models - A Bayesian Non-parametric Approach","date":"2018-11-07","arxiv_id":"1811.03422","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-amplitudes-perturbation-data-augmentation","title":"An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding","date":"2018-11-06","arxiv_id":"1811.02353","repositories_listed":0,"syntology":null},{"url":null,"slug":"double-adaptive-stochastic-gradient","title":"Double Adaptive Stochastic Gradient Optimization","date":"2018-11-06","arxiv_id":"1811.02525","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-the-wild-facial-expression-recognition-in","title":"In-the-wild Facial Expression Recognition in Extreme Poses","date":"2018-11-06","arxiv_id":"1811.02194","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-and-predicting-popularity-dynamics","title":"Modeling and Predicting Popularity Dynamics via Deep Learning Attention Mechanism","date":"2018-11-06","arxiv_id":"1811.02117","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalization-bounds-for-neural-networks","title":"Sample Compression, Support Vectors, and Generalization in Deep Learning","date":"2018-11-05","arxiv_id":"1811.02067","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-sensor-fusion-with-deep-learning","title":"Multi-Level Sensor Fusion with Deep Learning","date":"2018-11-05","arxiv_id":"1811.02447","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-ml-deep-learning-for-rashi-scripts-ocr","title":"Auto-ML Deep Learning for Rashi Scripts OCR","date":"2018-11-03","arxiv_id":"1811.01290","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-computer-aided-diagnosis","title":"Deep Learning based Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A Survey","date":"2018-11-03","arxiv_id":"1811.01238","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-bit-ofdm-receivers-via-deep-learning","title":"One-Bit OFDM Receivers via Deep Learning","date":"2018-11-02","arxiv_id":"1811.00971","repositories_listed":0,"syntology":null},{"url":null,"slug":"topological-approaches-to-deep-learning","title":"Topological Approaches to Deep Learning","date":"2018-11-02","arxiv_id":"1811.01122","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-the-fundamental-concepts-of-mathematics","title":"Connections between physics, mathematics and deep learning","date":"2018-11-01","arxiv_id":"1811.00576","repositories_listed":0,"syntology":null},{"url":null,"slug":"macquarie-university-at-bioasq-6b-deep","title":"Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based summarisation","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"democratizing-production-scale-distributed","title":"Democratizing Production-Scale Distributed Deep Learning","date":"2018-10-31","arxiv_id":"1811.00143","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-diagnosis-of-lymphoma-with-digital","title":"Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning","date":"2018-10-30","arxiv_id":"1811.02668","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-as-feature-encoding-for-emotion","title":"Deep Learning as Feature Encoding for Emotion Recognition","date":"2018-10-30","arxiv_id":"1810.12613","repositories_listed":0,"syntology":null},{"url":null,"slug":"weak-supervision-for-deep-representation","title":"Weak-supervision for Deep Representation Learning under Class Imbalance","date":"2018-10-30","arxiv_id":"1810.12513","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-cnn-resbilstm-ctc-an-end-to-end","title":"Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition","date":"2018-10-29","arxiv_id":"1810.12001","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-multi-task-deep-learning-for","title":"Multi-label Multi-task Deep Learning for Behavioral Coding","date":"2018-10-29","arxiv_id":"1810.12349","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hitchhikers-guide-on-distributed-training","title":"A Hitchhiker's Guide On Distributed Training of Deep Neural Networks","date":"2018-10-28","arxiv_id":"1810.11787","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-generation-of-low-precision-deep","title":"Automating Generation of Low Precision Deep Learning Operators","date":"2018-10-25","arxiv_id":"1810.11066","repositories_listed":0,"syntology":null},{"url":null,"slug":"har-netfusing-deep-representation-and-hand","title":"HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition","date":"2018-10-25","arxiv_id":"1810.10929","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-strategy-for-vehicular","title":"A Deep Learning Strategy for Vehicular Floating Content Management","date":"2018-10-24","arxiv_id":"1811.11249","repositories_listed":0,"syntology":null},{"url":"/paper/forecasting-individualized-disease","slug":"forecasting-individualized-disease","title":"Forecasting Individualized Disease Trajectories using Interpretable Deep Learning","date":"2018-10-24","arxiv_id":"1810.10489","repositories_listed":0,"syntology":null},{"url":null,"slug":"precipitation-nowcasting-leveraging","title":"Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN","date":"2018-10-24","arxiv_id":"1810.10485","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-poissons-equation-using-deep-learning","title":"Solving Poisson's Equation using Deep Learning in Particle Simulation of PN Junction","date":"2018-10-24","arxiv_id":"1810.10192","repositories_listed":0,"syntology":null},{"url":null,"slug":"nestdnn-resource-aware-multi-tenant-on-device","title":"NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision","date":"2018-10-23","arxiv_id":"1810.10090","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-fruit-detection-and","title":"A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards","date":"2018-10-22","arxiv_id":"1810.09499","repositories_listed":0,"syntology":null},{"url":null,"slug":"lamvi-2-a-visual-tool-for-comparing-and","title":"LAMVI-2: A Visual Tool for Comparing and Tuning Word Embedding Models","date":"2018-10-22","arxiv_id":"1810.11367","repositories_listed":0,"syntology":null},{"url":null,"slug":"ruuh-a-deep-learning-based-conversational","title":"Ruuh: A Deep Learning Based Conversational Social Agent","date":"2018-10-22","arxiv_id":"1810.12097","repositories_listed":0,"syntology":null},{"url":null,"slug":"analog-to-digital-conversion-revolutionized","title":"Analog-to-digital conversion revolutionized by deep learning","date":"2018-10-21","arxiv_id":"1810.08906","repositories_listed":0,"syntology":null},{"url":null,"slug":"to-compress-or-not-to-compress-characterizing","title":"To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference","date":"2018-10-21","arxiv_id":"1810.08899","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-machine-to-machine-an-oct-trained-deep","title":"From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs","date":"2018-10-20","arxiv_id":"1810.10343","repositories_listed":0,"syntology":null},{"url":null,"slug":"positnn-tapered-precision-deep-learning","title":"PositNN: Tapered Precision Deep Learning Inference for the Edge","date":"2018-10-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-registration-tools","title":"A Comparative Analysis of Registration Tools: Traditional vs Deep Learning Approach on High Resolution Tissue Cleared Data","date":"2018-10-19","arxiv_id":"1810.08315","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-deep-learning-for-face-detection-and","title":"Embedded Deep Learning for Face Detection and Emotion Recognition with Intel© Movidius (TM) Neural Compute Stick","date":"2018-10-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sequenced-replacement-sampling-for-deep","title":"Sequenced-Replacement Sampling for Deep Learning","date":"2018-10-19","arxiv_id":"1810.08322","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-transparent-neural-network","title":"Towards Transparent Neural Network Acceleration","date":"2018-10-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptivity-of-deep-relu-network-for-learning","title":"Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality","date":"2018-10-18","arxiv_id":"1810.08033","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-verification-for-autonomous","title":"Compositional Verification for Autonomous Systems with Deep Learning Components","date":"2018-10-18","arxiv_id":"1810.08303","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-vs-human-graders-for","title":"Deep Learning vs. Human Graders for Classifying Severity Levels of Diabetic Retinopathy in a Real-World Nationwide Screening Program","date":"2018-10-18","arxiv_id":"1810.08290","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-deep-learning-across-multiple","title":"Collaborative Deep Learning Across Multiple Data Centers","date":"2018-10-16","arxiv_id":"1810.06877","repositories_listed":0,"syntology":null},{"url":null,"slug":"dn-resnet-efficient-deep-residual-network-for","title":"DN-ResNet: Efficient Deep Residual Network for Image Denoising","date":"2018-10-16","arxiv_id":"1810.06766","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-data-quality-through-deep-learning","title":"Improving Data Quality through Deep Learning and Statistical Models","date":"2018-10-16","arxiv_id":"1810.07132","repositories_listed":0,"syntology":null},{"url":null,"slug":"projecting-trouble-light-based-adversarial","title":"Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers","date":"2018-10-16","arxiv_id":"1810.10337","repositories_listed":0,"syntology":null},{"url":null,"slug":"security-matters-a-survey-on-adversarial","title":"Security Matters: A Survey on Adversarial Machine Learning","date":"2018-10-16","arxiv_id":"1810.07339","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-newton-scheme-for-deep-learning","title":"The Newton Scheme for Deep Learning","date":"2018-10-16","arxiv_id":"1810.07550","repositories_listed":0,"syntology":null},{"url":null,"slug":"composing-rnns-and-fsts-for-small-data","title":"Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text","date":"2018-10-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-super-resolution-in","title":"Deep learning-based super-resolution in coherent imaging systems","date":"2018-10-15","arxiv_id":"1810.06611","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-segment-corneal-tissue-interfaces","title":"Learning to Segment Corneal Tissue Interfaces in OCT Images","date":"2018-10-15","arxiv_id":"1810.06612","repositories_listed":0,"syntology":null},{"url":null,"slug":"stop-illegal-comments-a-multi-task-deep","title":"Stop Illegal Comments: A Multi-Task Deep Learning Approach","date":"2018-10-15","arxiv_id":"1810.06665","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-detection-algorithm-for-remote-sensing","title":"Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks","date":"2018-10-13","arxiv_id":"1810.05782","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-deep-learning-in-ophthalmology","title":"Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter","date":"2018-10-13","arxiv_id":"1810.05874","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-deep-learning-for-robust-object","title":"Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments","date":"2018-10-13","arxiv_id":"1810.10323","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-gentle-introduction-to-deep-learning-in","title":"A Gentle Introduction to Deep Learning in Medical Image Processing","date":"2018-10-12","arxiv_id":"1810.05401","repositories_listed":0,"syntology":null},{"url":null,"slug":"isa-mapper-a-compute-and-hardware-agnostic","title":"ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler","date":"2018-10-12","arxiv_id":"1810.09958","repositories_listed":0,"syntology":null},{"url":null,"slug":"protein-family-classification-using-deep","title":"Protein Family Classification using Deep Learning","date":"2018-10-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-blended-deep-learning-approach-for","title":"A Blended Deep Learning Approach for Predicting User Intended Actions","date":"2018-10-11","arxiv_id":"1810.04824","repositories_listed":0,"syntology":null}],"record_sha256":"b73e8f4f10dc33a1e30d8cce2ed51b21bbfcadf139f3f93e32e7fc8cb4f2fec8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}