David Rolnick

Artificial Intelligence · with Belongie · 2017–2022

A researcher in the Science corpus: 3 papers; trusts 146 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #6,406 of 35,345 by trust here, #8,439 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #4,923 of 21,575 by trust here, #10,010 by citations
• Evaluation & Benchmarks — 26 citations into it
• Optimization & Training — 16 citations into it; #680 of 2,601 by trust here, #1,365 by citations
• Model Architectures — 12 citations into it
• Scaling & Learning Theory — 9 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Safety, Robustness & Interpretability — 8 citations into it

Whom David Rolnick trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Yann LeCun
4. Geoffrey E. Hinton
5. Matthew D. Zeiler
6. Yoshua Bengio
7. Jia Deng
8. Ilya Sutskever
9. X. Zhang
10. Christian Szegedy

Who trusts David Rolnick overall

1. Greg Yang
2. Boris Hanin
3. Soham De
4. Alexander Turner
5. Jianfeng Feng
6. Kuang-Huei Lee
7. Samuel L. Smith
8. Xiaobo Xia
9. Tao Yu
10. Haoqi Fan

Papers from David Rolnick's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• How to Start Training: The Effect of Initialization and Architecture (2018) — 282 citations
• Deep Learning is Robust to Massive Label Noise (2017) — 639 citations
• Bugs in the Data: How ImageNet Misrepresents Biodiversity (2022) — 52 citations