Evgeny Andriyash
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 4 papers; trusts 296 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 90 citations into it
- Computer Vision and Pattern Recognition — 88 citations into it; #14,560 of 21,575 by trust here, #9,722 by citations
- Generative Models — 70 citations into it; #1,387 of 1,956 by trust here, #1,577 by citations
- Probabilistic & Bayesian Methods — 55 citations into it
- Signal Processing — 16 citations into it
- Statistical and Nonlinear Physics — 15 citations into it; #6,964 of 10,127 by trust here, #6,727 by citations
- Representation & Self-Supervised Learning — 13 citations into it
- Model Architectures — 12 citations into it
Whom Evgeny Andriyash trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Radford M. Neal
- Ruslan Salakhutdinov
- Yoshua Bengio
- Andriy Mnih
- Alex Graves
- Alex Krizhevsky
- Max Welling
- Danilo Jimenez Rezende
Who trusts Evgeny Andriyash overall
Papers from Evgeny Andriyash's perspective
Ordered by how strongly the researchers Evgeny Andriyash 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- PixelVAE++: Improved PixelVAE with Discrete Prior (2019) — 34 citations
- Undirected Graphical Models as Approximate Posteriors (2019) — 15 citations
- DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors (2018) — 56 citations
- Quantum Boltzmann Machine (2016) — 599 citations