Jonathan Niles-Weed

Artificial Intelligence · with Fernandez‐Granda · 2020

A researcher in the Science corpus: 1 paper; trusts 79 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Optimization & Training — 16 citations into it
• Safety, Robustness & Interpretability — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Jonathan Niles-Weed trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Shahar Mendelson
10. X. Zhang

Who trusts Jonathan Niles-Weed overall

1. Hung-Yu Tseng

Papers from Jonathan Niles-Weed's perspective

Ordered by how strongly the researchers Jonathan Niles-Weed 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Early-Learning Regularization Prevents Memorization of Noisy Labels (2020) — 772 citations