David S. Bindel
Artificial Intelligence · 2000–2018
A researcher in the Science corpus: 5 papers; trusts 49 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 29 citations into it; #20,929 of 35,345 by trust here, #16,471 by citations
- Probabilistic & Bayesian Methods — 22 citations into it
- Computational Theory and Mathematics — 16 citations into it
- Electrical and Electronic Engineering — 14 citations into it; #5,123 of 16,539 by trust here, #8,176 by citations
- Computer Vision and Pattern Recognition — 13 citations into it
- Optimization & Training — 11 citations into it
- Statistics and Probability — 11 citations into it
- Numerical Methods & Scientific Computing — 10 citations into it
Whom David S. Bindel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- R. Astley
- Timothy A. Davis
- Andrew Gordon Wilson
- U. Basu
- R. Lehoucq
- Diederik P. Kingma
- Geoffrey E. Hinton
- L. Eon Bottou
- Kaiming He
- Alex Krizhevsky
Who trusts David S. Bindel overall
- Brandon Harrington
- Amy E. Duwel
- Reza Abdolvand
- Beverly Yang
- U. Basu
- Wesley J. Maddox
- Andrew Gordon Wilson
- Rob N. Candler
- Cheng Huang
- Hector Garcia-Molina
Papers from David S. Bindel's perspective
Ordered by how strongly the researchers David S. Bindel trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration (2018) — 1,460 citations
- Scalable Log Determinants for Gaussian Process Kernel Learning (2017) — 109 citations
- IEEE Standard for Floating-Point Arithmetic (2008) — 2,319 citations
- Elastic PMLs for resonator anchor loss simulation (2005) — 194 citations
- OceanStore: an architecture for global-scale persistent storage (2000) — 2,615 citations