Ethan Fetaya

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 14 papers; trusts 961 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 297 citations into it; #7,717 of 35,345 by trust here, #6,330 by citations
• Computer Vision and Pattern Recognition — 221 citations into it; #5,229 of 21,575 by trust here, #5,082 by citations
• Model Architectures — 81 citations into it
• Representation & Self-Supervised Learning — 59 citations into it
• Optimization & Training — 54 citations into it
• Generative Models — 47 citations into it
• Graph Learning — 39 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it

Whom Ethan Fetaya trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Christian Szegedy
9. Karen Simonyan
10. Ross B. Girshick

Who trusts Ethan Fetaya overall

1. Han-Jia Ye
2. Haggai Maron
3. Dushyant Mehta
4. Will Grathwohl
5. Sercan Ö. Arik
6. Ke Sun
7. Diogo Carbonera Luvizon
8. Adrian Bulat
9. Hadar Serviansky
10. Mengye Ren

Papers from Ethan Fetaya's perspective

Ordered by how strongly the researchers Ethan Fetaya 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Incremental Few-Shot Learning with Attention Attractor Networks (2018) — 189 citations
• Human Pose Estimation Using Deep Consensus Voting (2016) — 169 citations
• Inference in Probabilistic Graphical Models by Graph Neural Networks (2018) — 110 citations
• Atoms of recognition in human and computer vision (2016) — 169 citations
• Reviving and Improving Recurrent Back-Propagation (2018) — 140 citations
• On Learning Sets of Symmetric Elements (2020) — 152 citations
• Understanding the Limitations of Conditional Generative Models (2019) — 65 citations
• On the Universality of Invariant Networks (2019) — 275 citations
• Personalized Federated Learning using Hypernetworks (2021) — 473 citations
• GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning (2021) — 42 citations
• Conditional Generative Models are not Robust (2019) — 16 citations
• UvA-DARE (Digital Academic Repository) Neural Relational Inference for Interacting Systems Neural Relational Inference for Interacting Systems (2018)
• Neural Guided Constraint Logic Programming for Program Synthesis (2018) — 35 citations
• Learning Discrete Weights Using the Local Reparameterization Trick (2017) — 92 citations