Andrej Risteski

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 11 papers; trusts 177 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 137 citations into it; #11,206 of 35,345 by trust here, #9,870 by citations
• Representation & Self-Supervised Learning — 81 citations into it; #1,877 of 3,145 by trust here, #2,012 by citations
• Natural Language Processing — 63 citations into it; #3,122 of 5,688 by trust here, #3,369 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #15,285 of 21,575 by trust here, #20,756 by citations
• Computational Mechanics — 30 citations into it
• Optimization & Training — 22 citations into it; #1,852 of 2,601 by trust here, #2,259 by citations
• Signal Processing — 17 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it; #1,133 of 1,525 by trust here, #1,274 by citations

Whom Andrej Risteski trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Radford M. Neal
9. Kaiming He
10. Michael I. Jordan

Who trusts Andrej Risteski overall

1. Yuanzhi Li
2. Zeyuan Allen-Zhu
3. Garrett Wilson
4. Eitan Richardson
5. Diane Joyce Cook
6. Liangzu Peng
7. Sanjeev Arora
8. Zehao Dou
9. Ryan Webster

Papers from Andrej Risteski's perspective

Ordered by how strongly the researchers Andrej Risteski 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Linear Algebraic Structure of Word Senses, with Applications to Polysemy (2016) — 346 citations
• The Risks of Invariant Risk Minimization (2020) — 369 citations
• A Latent Variable Model Approach to PMI-based Word Embeddings (2015) — 400 citations
• Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions (2022) — 16 citations
• An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization (2021) — 40 citations
• Do GANs learn the distribution? Some Theory and Empirics (2018) — 171 citations
• On the Ability of Neural Nets to Express Distributions (2017) — 89 citations
• Recovery guarantee of weighted low-rank approximation via alternating minimization (2016) — 52 citations
• Provable learning of noisy-OR networks (2016) — 27 citations
• Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates (2016) — 30 citations
• RAND-WALK: A Latent Variable Model Approach to Word Embeddings (2015) — 67 citations