Lu Hou

Artificial Intelligence · with Jiang · 2016–2022

A researcher in the Science corpus: 10 papers; trusts 491 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 253 citations into it; #9,983 of 35,345 by trust here, #5,788 by citations
• Computer Vision and Pattern Recognition — 195 citations into it; #7,060 of 21,575 by trust here, #4,584 by citations
• Efficiency & Compression — 90 citations into it; #572 of 1,264 by trust here, #478 by citations
• Large Language Models — 60 citations into it; #331 of 799 by trust here, #468 by citations
• Natural Language Processing — 51 citations into it
• Model Architectures — 48 citations into it; #1,051 of 2,278 by trust here, #1,749 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Optimization & Training — 34 citations into it; #1,383 of 2,601 by trust here, #1,804 by citations

Whom Lu Hou trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Sepp Hochreiter
9. Jimmy Ba
10. Alex Graves

Who trusts Lu Hou overall

1. Wei Zhang
2. Yu Cheng
3. Chaofan Tao
4. Zi-Mei Yao
5. Xiaoxia Wu
6. Haoli Bai
7. Sehoon Kim
8. Bohan Zhuang
9. Xinyin Ma
10. Shangyu Chen

Papers from Lu Hou's perspective

Ordered by how strongly the researchers Lu Hou 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• FILIP: Fine-grained Interactive Language-Image Pre-Training (2021) — 865 citations
• Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation (2022) — 113 citations
• DynaBERT: Dynamic BERT with Adaptive Width and Depth (2020) — 390 citations
• Loss-aware Binarization of Deep Networks (2016) — 229 citations
• BinaryBERT: Pushing the Limit of BERT Quantization (2020) — 276 citations
• TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020) — 238 citations
• Compression of Generative Pre-trained Language Models via Quantization (2022) — 119 citations
• Improved OOD Generalization via Adversarial Training and Pre-training (2021) — 101 citations
• Analysis of Quantized Models (2019) — 38 citations
• Power Law in Sparsified Deep Neural Networks (2018) — 3 citations