Chen-Yu Ho

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #34,688 of 35,345 by trust here, #19,873 by citations
• Optimization & Training — 70 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• ML Systems & Hardware — 27 citations into it
• Computational Mechanics — 24 citations into it
• Computer Networks and Communications — 20 citations into it
• Electrical and Electronic Engineering — 15 citations into it
• Hardware and Architecture — 13 citations into it

Whom Chen-Yu Ho trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Dan Alistarh
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Sebastian U. Stich
7. Yoshua Bengio
8. Peter Richtárik
9. Sébastien Bubeck
10. Yurii E. Nesterov

Who trusts Chen-Yu Ho overall

1. Samuel Horváth

Papers from Chen-Yu Ho's perspective

Ordered by how strongly the researchers Chen-Yu Ho 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
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

• Natural Compression for Distributed Deep Learning (2019) — 183 citations
• Efficient sparse collective communication and its application to accelerate distributed deep learning (2020) — 142 citations
• Scaling Distributed Machine Learning with In-Network Aggregation (2019) — 553 citations