Robert Nishihara

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 8 papers; trusts 91 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #8,309 of 35,345 by trust here, #5,638 by citations
• Reinforcement Learning — 59 citations into it; #2,221 of 2,242 by trust here, #1,663 by citations
• Optimization & Training — 58 citations into it; #1,141 of 2,601 by trust here, #1,744 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #12,692 of 21,575 by trust here, #7,421 by citations
• Computer Networks and Communications — 31 citations into it; #2,393 of 9,886 by trust here, #4,405 by citations
• ML Systems & Hardware — 31 citations into it; #238 of 575 by trust here, #137 by citations
• Information Systems — 23 citations into it
• Hardware and Architecture — 19 citations into it

Whom Robert Nishihara trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. L. Eon Bottou
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Kaiming He
9. Ronald J. Williams
10. Richard S. Sutton

Who trusts Robert Nishihara overall

1. Jakub Konecný
2. Aryan Mokhtari
3. David Lopez-Paz
4. Mark Eisen
5. Tal Ben-Nun
6. Jianyu Wang
7. Alejandro Newell
8. Eric Liang
9. Horia Mania
10. Zheng Xu

Papers from Robert Nishihara's perspective

Ordered by how strongly the researchers Robert Nishihara trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Ray: A Distributed Framework for Emerging AI Applications (2017) — 1,702 citations
• Discovering Causal Signals in Images (2016) — 256 citations
• SparkNet: Training Deep Networks in Spark (2015) — 178 citations
• Tune: A Research Platform for Distributed Model Selection and Training (2018) — 1,129 citations
• Ray RLLib: A Composable and Scalable Reinforcement Learning Library (2017) — 181 citations
• A Linearly-Convergent Stochastic L-BFGS Algorithm (2015) — 262 citations
• RLlib: Abstractions for Distributed Reinforcement Learning (2017) — 1,043 citations
• A General Analysis of the Convergence of ADMM (2015) — 339 citations