Hao Liu

Artificial Intelligence · 8 papers · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 406 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 187 citations into it; #32,136 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 115 citations into it
• Reinforcement Learning — 80 citations into it
• Generative Models — 45 citations into it
• Probabilistic & Bayesian Methods — 30 citations into it
• Optimization & Training — 25 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Safety, Robustness & Interpretability — 20 citations into it

Whom Hao Liu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Alex Graves
9. Christian Szegedy
10. Max Welling

Who trusts Hao Liu overall

1. Alexander R. Trott
2. Ching-An Cheng
3. Yaqian Zhang
4. Youngmin Oh
5. Avishek Joey Bose
6. Víctor Campos
7. George Tucker
8. Sriram Srinivasan
9. Caiming Xiong
10. Xinyan Yan

Papers from Hao Liu's perspective

Ordered by how strongly the researchers Hao Liu 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. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The False Promise of Imitating Proprietary LLMs (2023) — 280 citations
• Aligning Text-to-Image Models using Human Feedback (2023) — 471 citations
• Competitive Experience Replay (2019) — 60 citations
• DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models (2023) — 460 citations
• Hybrid Discriminative-Generative Training via Contrastive Learning (2020) — 43 citations
• Variational Inference with Tail-adaptive f-Divergence (2018) — 61 citations
• Action-dependent Control Variates for Policy Optimization via Stein Identity (2018) — 52 citations
• Action-depedent Control Variates for Policy Optimization via Stein's Identity (2017) — 57 citations