Joseph Keshet

Artificial Intelligence · 2003–2020

A researcher in the Science corpus: 11 papers; trusts 367 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #15,509 of 35,345 by trust here, #8,283 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #16,432 of 21,575 by trust here, #10,677 by citations
• Natural Language Processing — 29 citations into it; #2,424 of 5,688 by trust here, #3,826 by citations
• Model Architectures — 26 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Signal Processing — 20 citations into it; #5,649 of 7,572 by trust here, #2,276 by citations
• Optimization & Training — 19 citations into it; #601 of 2,601 by trust here, #622 by citations
• Statistical Learning Theory — 18 citations into it; #274 of 1,038 by trust here, #175 by citations

Whom Joseph Keshet trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. B. Taskar
8. Ilya Sutskever
9. David A. McAllester
10. Ross B. Girshick

Who trusts Joseph Keshet overall

1. Yang Song
2. Mark Dredze
3. Steven C. H. Hoi
4. André F. T. Martins
5. Shai Shalev-Shwartz
6. Ross B. Girshick
7. Samy Bengio
8. Peilin Zhao
9. Sham M. Kakade
10. Alexander Gerhard Schwing

Papers from Joseph Keshet's perspective

Ordered by how strongly the researchers Joseph Keshet 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring (2018) — 872 citations
• Houdini: Fooling Deep Structured Prediction Models (2017) — 280 citations
• Out-of-Distribution Detection using Multiple Semantic Label Representations (2018) — 93 citations
• Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation (2020) — 91 citations
• Predicting glottal closure insufficiency using fundamental frequency contour analysis (2019)
• Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions (2013) — 22 citations
• Generalization bounds and consistency for latent-structural probit and ramp loss (2011) — 66 citations
• Explicit Approximations of the Gaussian Kernel (2011) — 68 citations
• Direct Loss Minimization for Structured Prediction (2010) — 154 citations
• The projectron: a bounded kernel-based Perceptron (2008) — 127 citations
• Online Passive-Aggressive Algorithms (2003) — 2,132 citations