Jiri Hron
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 8 papers; trusts 117 researchers and is trusted by 62 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 125 citations into it; #4,377 of 35,345 by trust here, #5,359 by citations
- Computer Vision and Pattern Recognition — 65 citations into it; #14,766 of 21,575 by trust here, #13,269 by citations
- Probabilistic & Bayesian Methods — 61 citations into it; #381 of 1,525 by trust here, #332 by citations
- Optimization & Training — 40 citations into it
- Model Architectures — 36 citations into it; #1,612 of 2,278 by trust here, #1,693 by citations
- Statistical and Nonlinear Physics — 20 citations into it; #6,409 of 10,127 by trust here, #4,281 by citations
- Statistics and Probability — 15 citations into it
- Generative Models — 14 citations into it
Whom Jiri Hron trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yarin Gal
- Geoffrey E. Hinton
- Radford M. Neal
- Alex Krizhevsky
- Yann LeCun
- Max Welling
- Yoshua Bengio
- Nitish Srivastava
- Zoubin Ghahramani
Who trusts Jiri Hron overall
- Greg Yang
- Keuntaek Lee
- Timothy Nguyen
- Lechao Xiao
- Roman Novak
- Jaehoon Lee
- Carlos Riquelme
- Charles Herrmann
- Chun-Hao Kingsley Chang
- Guodong Zhang
Papers from Jiri Hron's perspective
Ordered by how strongly the researchers Jiri Hron trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Concrete Dropout (2017) — 677 citations
- Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes (2018) — 331 citations
- Variational Gaussian Dropout is not Bayesian (2017) — 49 citations
- Neural Tangents: Fast and Easy Infinite Neural Networks in Python (2019) — 261 citations
- Variational Bayesian dropout: pitfalls and fixes (2018) — 70 citations
- Orthogonal Estimation of Wasserstein Distances (2019) — 53 citations
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (2023) — 337 citations
- Gaussian Process Behaviour in Wide Deep Neural Networks (2018) — 619 citations