Willie Neiswanger
Artificial Intelligence · 2012–2024
A researcher in the Science corpus: 13 papers; trusts 704 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 242 citations into it; #6,672 of 35,345 by trust here, #6,103 by citations
- Computer Vision and Pattern Recognition — 107 citations into it; #5,521 of 21,575 by trust here, #5,620 by citations
- Probabilistic & Bayesian Methods — 61 citations into it; #879 of 1,525 by trust here, #435 by citations
- Optimization & Training — 55 citations into it
- Neural Architecture Search & AutoML — 49 citations into it; #67 of 283 by trust here, #54 by citations
- Evaluation & Benchmarks — 41 citations into it
- Statistics and Probability — 32 citations into it; #11,368 of 12,932 by trust here, #7,914 by citations
- Computational Theory and Mathematics — 31 citations into it; #7,747 of 12,182 by trust here, #4,850 by citations
Whom Willie Neiswanger trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Kaiming He
- Quoc V. Le
- Yann LeCun
- Karen Simonyan
- Radford M. Neal
- Ilya Sutskever
Who trusts Willie Neiswanger overall
- Colin White
- Yash Savani
- Sam Nolen
- Michael I. Jordan
- Da Wang
- Peter J. Green
- Kevin Hsieh
- Maruan Al-Shedivat
- N. Foti
- Joel C. Zinn
Papers from Willie Neiswanger's perspective
Ordered by how strongly the researchers Willie Neiswanger trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018) — 666 citations
- Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures (2012) — 3 citations
- Fast Distribution To Real Regression (2013) — 43 citations
- Fast Function to Function Regression (2014) — 31 citations
- A Study on Encodings for Neural Architecture Search (2020) — 85 citations
- Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms (2014) — 44 citations
- ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization (2019) — 18 citations
- Methods for comparing uncertainty quantifications for material property predictions (2019) — 208 citations
- LiveBench: A Challenging, Contamination-Free LLM Benchmark (2024) — 317 citations
- Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling (2020) — 80 citations
- BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (2019) — 413 citations
- ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language (2019) — 10 citations
- Asymptotically Exact, Embarrassingly Parallel MCMC (2013) — 341 citations