Hung Viet Pham

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 4 papers; trusts 25 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 77 citations into it
• Information Systems — 61 citations into it; #7,736 of 8,771 by trust here, #4,885 by citations
• Software — 46 citations into it; #1,054 of 1,227 by trust here, #572 by citations
• Safety, Robustness & Interpretability — 37 citations into it
• Natural Language Processing — 33 citations into it
• Computer Networks and Communications — 15 citations into it
• Program Analysis & Verification — 14 citations into it; #256 of 301 by trust here, #166 by citations
• Code Generation & Program Synthesis — 11 citations into it; #185 of 220 by trust here, #140 by citations

Whom Hung Viet Pham trusts overall

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

1. Miltiadis Allamanis
2. Timon Gehr
3. Yoshua Bengio
4. Premkumar T. Devanbu
5. Geoffrey E. Hinton
6. James Bergstra
7. Alex Krizhevsky
8. Ilya Sutskever
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Hung Viet Pham overall

1. Chun Xia
2. Andrew Brock

Papers from Hung Viet Pham's perspective

Ordered by how strongly the researchers Hung Viet Pham trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries (2019) — 214 citations
• Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training (2021) — 49 citations
• CoCoNuT: combining context-aware neural translation models using ensemble for program repair (2020) — 436 citations
• Learning API Usages from Bytecode: A Statistical Approach (2015) — 80 citations