Ngoc-Quan Pham

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 455 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #9,814 of 35,345 by trust here, #7,945 by citations
• Natural Language Processing — 112 citations into it; #2,710 of 5,688 by trust here, #3,332 by citations
• Computer Vision and Pattern Recognition — 77 citations into it; #9,225 of 21,575 by trust here, #8,296 by citations
• Model Architectures — 36 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #1,547 of 3,145 by trust here, #1,638 by citations
• Evaluation & Benchmarks — 17 citations into it
• Multimodal Learning — 12 citations into it; #665 of 1,211 by trust here, #821 by citations
• Optimization & Training — 12 citations into it

Whom Ngoc-Quan Pham trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Tomas Mikolov
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Kaiming He
8. Richard Socher
9. Karen Simonyan
10. Marco Baroni

Who trusts Ngoc-Quan Pham overall

1. John Wieting
2. Zhijian Ou
3. Kevin Gimpel
4. Alexandros Komninos
5. Rohit Prabhavalkar
6. Dan Otter
7. Raj Dabre
8. Matteo Pagliardini
9. Yuge Shi
10. Angela Fan

Papers from Ngoc-Quan Pham's perspective

Ordered by how strongly the researchers Ngoc-Quan Pham 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Combining Language and Vision with a Multimodal Skip-gram Model (2015) — 290 citations
• Towards Multi-Agent Communication-Based Language Learning (2016) — 26 citations
• Improving Zero-shot Translation with Language-Independent Constraints (2019) — 65 citations
• Convolutional Neural Network Language Models (2016) — 40 citations
• Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model (2015) — 48 citations
• Very Deep Self-Attention Networks for End-to-End Speech Recognition (2019) — 172 citations
• The red one!: On learning to refer to things based on discriminative properties (2016) — 16 citations
• Open Source Toolkit for Speech to Text Translation (2018) — 14 citations