Keith D. Levin

Artificial Intelligence · 2013–2018

A researcher in the Science corpus: 7 papers; trusts 23 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #6,565 of 35,345 by trust here, #7,858 by citations
• Signal Processing — 31 citations into it; #1,013 of 7,572 by trust here, #1,504 by citations
• Speech & Audio — 24 citations into it; #280 of 1,076 by trust here, #361 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #1,530 of 3,145 by trust here, #2,094 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Computational Theory and Mathematics — 8 citations into it
• Statistical Learning Theory — 7 citations into it

Whom Keith D. Levin trusts overall

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

1. Aren Jansen
2. Geoffrey E. Hinton
3. Mikhail Belkin
4. Sariel Har-Peled
5. Sam T. Roweis
6. James R. Glass
7. Diederik P. Kingma
8. Partha Niyogi
9. Piotr Indyk
10. Brian McFee

Who trusts Keith D. Levin overall

1. Herman Kamper
2. Karen Livescu
3. Shane Settle
4. Aren Jansen
5. Yu-An Chung
6. Wanjia He
7. Sharon Goldwater
8. Weiran Wang
9. Hung-yi Lee
10. Nils Holzenberger

Papers from Keith D. Levin's perspective

Ordered by how strongly the researchers Keith D. Levin 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition (2013) — 113 citations
• Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings (2017) — 91 citations
• Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings (2013) — 136 citations
• Segmental acoustic indexing for zero resource keyword search (2015) — 77 citations
• Out-of-sample extension of graph adjacency spectral embedding (2018)
• Statistical Inference on Random Dot Product Graphs: a Survey (2017) — 290 citations
• A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference (2017)