Atsushi Nitanda

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 87 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it
• Statistical Learning Theory — 36 citations into it
• Optimization & Training — 34 citations into it; #1,993 of 2,601 by trust here, #2,484 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Model Architectures — 20 citations into it
• Scaling & Learning Theory — 18 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Statistics and Probability — 15 citations into it

Whom Atsushi Nitanda trusts overall

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

1. Peter L. Bartlett
2. Jerome H. Friedman
3. Tong Zhang
4. Vladimir Koltchinskii
5. Lénaïc Chizat
6. Kaiming He
7. Yoshua Bengio
8. Simon Shaolei Du
9. Francis R. Bach
10. Geoffrey E. Hinton

Who trusts Atsushi Nitanda overall

1. Simone Scardapane
2. Michele Scarpiniti
3. Enzo Baccarelli

Papers from Atsushi Nitanda's perspective

Ordered by how strongly the researchers Atsushi Nitanda 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. Learning Multiple Layers of Features from Tiny Images
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

• Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems (2019) — 43 citations
• Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems (2019) — 16 citations
• Functional Gradient Boosting based on Residual Network Perception (2018) — 30 citations