Ka-Ho Chow

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 46 citations into it; #31,566 of 35,345 by trust here, #24,417 by citations
• Safety, Robustness & Interpretability — 14 citations into it
• Optimization & Training — 12 citations into it

Whom Ka-Ho Chow trusts overall

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

1. Reza Shokri
2. Stacey Truex
3. Diederik P. Kingma
4. Alex Krizhevsky

Who trusts Ka-Ho Chow overall

1. Lichao Sun

Papers from Ka-Ho Chow's perspective

Ordered by how strongly the researchers Ka-Ho Chow trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Learning Multiple Layers of Features from Tiny Images
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. ImageNet classification with deep convolutional neural networks
6. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Long Short-Term Memory
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• A Framework for Evaluating Gradient Leakage Attacks in Federated Learning (2020) — 172 citations
• LDP-Fed: federated learning with local differential privacy (2020) — 534 citations