Hung Le

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 4 papers; trusts 316 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it
• Natural Language Processing — 79 citations into it
• Computer Vision and Pattern Recognition — 59 citations into it
• Information Systems — 52 citations into it
• Large Language Models — 31 citations into it
• Software — 27 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Multimodal Learning — 17 citations into it

Whom Hung Le trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Ilya Sutskever
4. Alex Krizhevsky
5. Yoshua Bengio
6. Kaiming He
7. Jimmy Ba
8. Yann LeCun
9. Richard S. Sutton
10. Oriol Vinyals

Who trusts Hung Le overall

1. Patrick M. Haluptzok
2. Jinjie Ni
3. Xudong Lin
4. Theo X. Olausson
5. Shun Zhang
6. Jiate Liu
7. Gedas Bertasius
8. Sean Welleck
9. Guanzhi Wang
10. Matthew Bowers

Papers from Hung Le's perspective

Ordered by how strongly the researchers Hung Le 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems (2019) — 123 citations
• CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023) — 742 citations
• CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (2022) — 502 citations
• URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection (2018) — 325 citations