Duc Le
Artificial Intelligence · 2013–2024
A researcher in the Science corpus: 4 papers; trusts 32 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 77 citations into it; #20,269 of 35,345 by trust here, #14,718 by citations
- Signal Processing — 53 citations into it; #4,165 of 7,572 by trust here, #2,276 by citations
- Natural Language Processing — 35 citations into it
- Speech & Audio — 35 citations into it; #1,005 of 1,076 by trust here, #642 by citations
- Model Architectures — 21 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
- Optimization & Training — 8 citations into it
Whom Duc Le trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Graves
- Abdel-rahman Mohamed
- Diederik P. Kingma
- Yoshua Bengio
- Daniel Povey
- Alex Krizhevsky
- George E. Dahl
- Yann LeCun
- Hasim Sak
Who trusts Duc Le overall
- Wei-Ning Hsu
- Qi-rong Mao
- Alex Xiao
- Yongqiang Wang
- Abdel-rahman Mohamed
- Frank Zhang
- Ming Dong
- Zhengwei Huang
- Vineel Pratap
- Qiantong Xu
Papers from Duc Le's perspective
Ordered by how strongly the researchers Duc Le trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Transformer-Based Acoustic Modeling for Hybrid Speech Recognition (2019) — 266 citations
- From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition (2019) — 65 citations
- The Llama 3 Herd of Models (2024) — 17,085 citations
- Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks (2013) — 107 citations