Oren Rippel
Computer Vision and Pattern Recognition · 2013–2018
A researcher in the Science corpus: 8 papers; trusts 416 researchers and is trusted by 667 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 128 citations into it; #1,674 of 21,575 by trust here, #4,001 by citations
- Artificial Intelligence — 65 citations into it; #2,651 of 35,345 by trust here, #3,362 by citations
- Representation & Self-Supervised Learning — 41 citations into it; #1,211 of 3,145 by trust here, #1,885 by citations
- Optimization & Training — 33 citations into it; #1,409 of 2,601 by trust here, #505 by citations
- Signal Processing — 33 citations into it
- Model Architectures — 26 citations into it; #1,149 of 2,278 by trust here, #1,620 by citations
- Probabilistic & Bayesian Methods — 24 citations into it; #405 of 1,525 by trust here, #214 by citations
- Generative Models — 22 citations into it
Whom Oren Rippel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Ruslan Salakhutdinov
- Radford M. Neal
- Yann LeCun
- Yoshua Bengio
- Neil D. Lawrence
- Ilya Sutskever
- Kaiming He
Who trusts Oren Rippel overall
- Eirikur Agustsson
- Laurent Dinh
- Garrett Wilson
- Michael Tschannen
- David C. Minnen
- Fabian Mentzer
- Bharath Hariharan
- Waseem Rawat
- Eli Schwartz
- Jascha Narain Sohl-Dickstein
Papers from Oren Rippel's perspective
Ordered by how strongly the researchers Oren Rippel 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
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Scalable Bayesian Optimization Using Deep Neural Networks (2015) — 1,144 citations
- Metric Learning with Adaptive Density Discrimination (2015) — 224 citations
- Real-Time Adaptive Image Compression (2017) — 595 citations
- High-Dimensional Probability Estimation with Deep Density Models (2013) — 128 citations
- Spectral Representations for Convolutional Neural Networks (2015) — 404 citations
- Learning Ordered Representations with Nested Dropout (2014) — 129 citations
- Avoiding pathologies in very deep networks (2014) — 163 citations
- Learned Video Compression (2018) — 261 citations