Xuechen Li
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 10 papers; trusts 310 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 298 citations into it; #10,512 of 35,345 by trust here, #1,441 by citations
- Computer Vision and Pattern Recognition — 128 citations into it; #7,498 of 21,575 by trust here, #7,340 by citations
- Natural Language Processing — 90 citations into it
- Large Language Models — 74 citations into it
- Generative Models — 51 citations into it; #370 of 1,956 by trust here, #465 by citations
- Representation & Self-Supervised Learning — 47 citations into it; #1,035 of 3,145 by trust here, #1,277 by citations
- Evaluation & Benchmarks — 43 citations into it
- Probabilistic & Bayesian Methods — 35 citations into it
Whom Xuechen Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Radford M. Neal
- Alex Krizhevsky
- Yann LeCun
- Ilya Sutskever
- Kaiming He
- Jimmy Ba
- Ruslan Salakhutdinov
Who trusts Xuechen Li overall
- Francesco Locatello
- Fei Ye
- Yang Song
- Bernhard Schölkopf
- Qinsheng Zhang
- Chin-Wei Huang
- Stefan Bauer
- Emilien Dupont
- Danilo Jimenez Rezende
- Jie Huang
Papers from Xuechen Li's perspective
Ordered by how strongly the researchers Xuechen Li 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Isolating Sources of Disentanglement in VAEs (2018) — 451 citations
- Holistic Evaluation of Language Models (2023) — 1,863 citations
- Inference Suboptimality in Variational Autoencoders (2018) — 312 citations
- Large Language Models Can Be Strong Differentially Private Learners (2021) — 533 citations
- AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback (2023) — 921 citations
- Foundation Models and Fair Use (2023) — 188 citations
- Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks (2023) — 385 citations
- Scalable Gradients for Stochastic Differential Equations (2020) — 434 citations
- Isolating Sources of Disentanglement in Variational Autoencoders (2018) — 1,044 citations