Eric T. Nalisnick

Artificial Intelligence · with Lakshminarayanan · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 219 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #11,924 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 90 citations into it; #5,253 of 21,575 by trust here, #3,525 by citations
• Generative Models — 82 citations into it; #523 of 1,956 by trust here, #404 by citations
• Probabilistic & Bayesian Methods — 63 citations into it; #848 of 1,525 by trust here, #409 by citations
• Statistics and Probability — 39 citations into it
• Statistical and Nonlinear Physics — 33 citations into it
• Signal Processing — 29 citations into it
• Model Architectures — 21 citations into it

Whom Eric T. Nalisnick trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Danilo Jimenez Rezende
6. Aäron van den Oord
7. Laurent Dinh
8. Michael I. Jordan
9. Alex Graves
10. Radford M. Neal

Who trusts Eric T. Nalisnick overall

1. Erik A. Daxberger
2. Joost R. van Amersfoort
3. Yilun Du
4. Jie Jessie Ren
5. Pavel Izmailov
6. Stanislav Fort
7. Yee Whye Teh
8. Polina Kirichenko
9. José Miguel Hernández-Lobato
10. Lewis Smith

Papers from Eric T. Nalisnick's perspective

Ordered by how strongly the researchers Eric T. Nalisnick trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Do Deep Generative Models Know What They Don't Know? (2018) — 882 citations
• Normalizing Flows for Probabilistic Modeling and Inference (2019) — 2,415 citations
• Hybrid Models with Deep and Invertible Features (2019) — 105 citations
• Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality (2019) — 85 citations
• Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality (2019) — 170 citations