Eric T. Nalisnick

Artificial Intelligence · with Caruana · 2015–2016

A researcher in the Science corpus: 5 papers; trusts 135 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #10,244 of 35,345 by trust here, #15,236 by citations
• Natural Language Processing — 33 citations into it; #3,941 of 5,688 by trust here, #4,539 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Information Retrieval & Search — 13 citations into it; #313 of 1,022 by trust here, #471 by citations
• Generative Models — 11 citations into it
• Information Systems — 8 citations into it; #5,190 of 8,771 by trust here, #5,501 by citations

Whom Eric T. Nalisnick trusts overall

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

1. Diederik P. Kingma
2. Michael I. Jordan
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Tomas Mikolov
6. Ilya Sutskever
7. Danilo Jimenez Rezende
8. Ruslan Salakhutdinov
9. Alex Graves
10. Jimmy Ba

Who trusts Eric T. Nalisnick overall

1. Bhaskar Mitra
2. Fernando Diaz
3. Hamed Zamani
4. Nick Craswell
5. Jimmy J. Lin
6. J. Tomczak
7. Matthias Bauer
8. Jaspreet Singh
9. Tolga Bolukbasi
10. Danilo Jimenez Rezende

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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A Dual Embedding Space Model for Document Ranking (2016) — 140 citations
• Deep Generative Models with Stick-Breaking Priors (2016) — 21 citations
• Improving Document Ranking with Dual Word Embeddings (2016) — 174 citations
• Approximate Inference for Deep Latent Gaussian Mixtures (2016) — 100 citations
• Under review as a conference paper at ICLR 2016 (2015)