Benjamin S. Glicksberg
Artificial Intelligence · 2015–2023
A researcher in the Science corpus: 10 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 147 citations into it
- Federated & Distributed Learning — 46 citations into it
- Computer Vision and Pattern Recognition — 28 citations into it
- Optimization & Training — 18 citations into it
- Endocrinology, Diabetes and Metabolism — 13 citations into it
- Computational Theory and Mathematics — 5 citations into it
Whom Benjamin S. Glicksberg trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- H. Brendan McMahan
- Peter Richtárik
- Jakub Konecný
- Geoffrey E. Hinton
- Otkrist Gupta
- Robert Tibshirani
- Alex Krizhevsky
- Virginia Smith
- Reza Shokri
- Yoshua Bengio
Who trusts Benjamin S. Glicksberg overall
Papers from Benjamin S. Glicksberg's perspective
Ordered by how strongly the researchers Benjamin S. Glicksberg trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Distilling the Knowledge in a Neural Network (arXiv.org)
- Going deeper with convolutions
Their papers
- Deep learning predicts hip fracture using confounding patient and healthcare variables (2018) — 286 citations
- Identification of type 2 diabetes subgroups through topological analysis of patient similarity (2015) — 544 citations
- Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments (2023) — 298 citations
- Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) — 736 citations
- Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection (2020) — 668 citations
- AKI in Hospitalized Patients with COVID-19. (2020) — 606 citations
- Association of Treatment Dose Anticoagulation With In-Hospital Survival Among Hospitalized Patients With COVID-19 (2020) — 875 citations
- Federated Learning for Healthcare Informatics (2019) — 1,462 citations
- Artificial Intelligence in Cardiology. (2018) — 769 citations
- Machine learning in cardiovascular medicine: are we there yet? (2018) — 457 citations