February 11, 2015 preprint Open access Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift arXiv (Cornell University) DOI: 10.48550/arxiv.1502.03167 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 61 stored of 24,084 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Multi-Step ahead Predictive Model for Blood Glucose Concentrations of Type-1 Diabetic Patients 2021 Research Square preprint Medicine Diabetes Management and Research Open access 1 citations Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks 2021 arXiv (Cornell University) preprint Computer Science Advanced Neural Network Applications Open access 1 citations Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling 2021 Crystals article Materials Science Machine Learning in Materials Science Open access cited by 3 patents 40 citations STEP: Segmenting and Tracking Every Pixel 2021 arXiv (Cornell University) preprint Computer Science Advanced Neural Network Applications Open access 9 citations Indoor environment data time-series reconstruction using autoencoder neural networks 2021 Building and Environment article Engineering Building Energy and Comfort Optimization Open access 43 citations Norm Loss: An efficient yet effective regularization method for deep neural networks 2021 Proceedings - 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