Ross Goroshin

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 9 papers; trusts 417 researchers and is trusted by 2,088 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #873 of 21,575 by trust here, #1,171 by citations
• Artificial Intelligence — 93 citations into it; #5,215 of 35,345 by trust here, #2,517 by citations
• Representation & Self-Supervised Learning — 66 citations into it; #1,442 of 3,145 by trust here, #1,543 by citations
• Cognitive Neuroscience — 38 citations into it
• Optimization & Training — 17 citations into it
• Media Technology — 13 citations into it
• Aerospace Engineering — 10 citations into it
• Signal Processing — 10 citations into it; #3,958 of 7,572 by trust here, #1,538 by citations

Whom Ross Goroshin trusts overall

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

1. Yann LeCun
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Marc'Aurelio Ranzato
6. Koray Kavukcuoglu
7. L. Eon Bottou
8. Ilya Sutskever
9. Diederik P. Kingma
10. Kaiming He

Who trusts Ross Goroshin overall

1. Dinesh Jayaraman
2. Kristen Grauman
3. Ishan Misra
4. Chelsea Finn
5. Sergey Levine
6. Trevor Darrell
7. Christoph Feichtenhofer
8. Francesco Locatello
9. X. Wang
10. Wanli Ouyang

Papers from Ross Goroshin's perspective

Ordered by how strongly the researchers Ross Goroshin 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning to Navigate in Complex Environments (2016) — 958 citations
• Efficient object localization using Convolutional Networks (2014) — 1,451 citations
• Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (2019) — 701 citations
• Unsupervised Learning of Spatiotemporally Coherent Metrics (2014) — 166 citations
• Stacked What-Where Auto-encoders (2015) — 263 citations
• Learning to Linearize Under Uncertainty (2015) — 145 citations
• Unsupervised Feature Learning from Temporal Data (2015) — 35 citations
• Vector-based navigation using grid-like representations in artificial agents (2018) — 716 citations
• An Effective Anti-Aliasing Approach for Residual Networks (2020) — 36 citations