Yuri Burda
Artificial Intelligence · 2014–2022
A researcher in the Science corpus: 8 papers; trusts 261 researchers and is trusted by 1,254 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 149 citations into it; #571 of 35,345 by trust here, #1,473 by citations
- Reinforcement Learning — 108 citations into it; #516 of 2,242 by trust here, #345 by citations
- Computer Vision and Pattern Recognition — 63 citations into it; #1,351 of 21,575 by trust here, #2,347 by citations
- Probabilistic & Bayesian Methods — 44 citations into it; #116 of 1,525 by trust here, #153 by citations
- Generative Models — 35 citations into it; #107 of 1,956 by trust here, #273 by citations
- Management Science and Operations Research — 29 citations into it
- Optimization & Training — 28 citations into it
- Model Architectures — 22 citations into it
Whom Yuri Burda trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Radford M. Neal
- J. Urgen Schmidhuber
- Geoffrey E. Hinton
- Ruslan Salakhutdinov
- Yann LeCun
- Richard S. Sutton
- Ronald J. Williams
- Alex Krizhevsky
Who trusts Yuri Burda overall
- Arash Vahdat
- Yilun Du
- Lars Maaløe
- Sergey Levine
- Ruslan Salakhutdinov
- Yoshua Bengio
- Danilo Jimenez Rezende
- Andriy Mnih
- Matthew D. Hoffman
- Roger Baker Grosse
Papers from Yuri Burda's perspective
Ordered by how strongly the researchers Yuri Burda trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Importance Weighted Autoencoders (2015) — 1,359 citations
- On the Quantitative Analysis of Decoder-Based Generative Models (2016) — 226 citations
- Exploration by Random Network Distillation (2018) — 1,706 citations
- Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments (2017) — 376 citations
- Large-Scale Study of Curiosity-Driven Learning (2018) — 776 citations
- Accurate and conservative estimates of MRF log-likelihood using reverse annealing (2014) — 67 citations
- Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets (2022) — 696 citations
- Learning Policy Representations in Multiagent Systems (2018) — 137 citations