Kee Siong Ng

Optimization & Training · 2011–2019

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 10 citations into it

Whom Kee Siong Ng trusts overall

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

1. Mark Herbster
2. Ian H. Witten

Who trusts Kee Siong Ng overall

1. Marc G. Bellemare
2. Joel Veness
3. Romain Laroche
4. Georg Ostrovski
5. Erik Talvitie

Papers from Kee Siong Ng's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Et al
4. Large Scale Distributed Deep Networks
5. A Stochastic Approximation Method
6. Regression Shrinkage and Selection via the Lasso
7. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
8. Wasserstein GAN
9. Wasserstein Generative Adversarial Networks
10. Deep Learning with Differential Privacy

Their papers

• Context Tree Switching (2011) — 51 citations
• Towards Fair and Privacy-Preserving Federated Deep Models (2019) — 244 citations
• The MADlib Analytics Library or MAD Skills, the SQL (2012) — 449 citations