Elman Mansimov
Artificial Intelligence · 2015–2018
A researcher in the Science corpus: 4 papers; trusts 456 researchers and is trusted by 3,662 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 70 citations into it; #806 of 35,345 by trust here, #1,870 by citations
- Computer Vision and Pattern Recognition — 60 citations into it; #553 of 21,575 by trust here, #1,323 by citations
- Natural Language Processing — 35 citations into it; #1,226 of 5,688 by trust here, #2,263 by citations
- Model Architectures — 24 citations into it
- Reinforcement Learning — 19 citations into it; #1,305 of 2,242 by trust here, #1,004 by citations
- Optimization & Training — 16 citations into it; #1,560 of 2,601 by trust here, #1,009 by citations
- Generative Models — 15 citations into it
- Probabilistic & Bayesian Methods — 11 citations into it
Whom Elman Mansimov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Graves
- Ilya Sutskever
- Yoshua Bengio
- Karen Simonyan
- Kyunghyun Cho
- Oriol Vinyals
- J. Urgen Schmidhuber
Who trusts Elman Mansimov overall
- X. Wang
- Abhinav Kumar Gupta
- Zhe Gan
- Christoph Feichtenhofer
- Yoshua Bengio
- Sergey Levine
- Andrea Vedaldi
- Jitendra Malik
- Jiatao Gu
- Mohit Bansal
Papers from Elman Mansimov's perspective
Ordered by how strongly the researchers Elman Mansimov 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)
- 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
- 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
- Unsupervised Learning of Video Representations using LSTMs (2015) — 2,744 citations
- Generating Images from Captions with Attention (2015) — 495 citations
- Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (2017) — 674 citations
- Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (2018) — 498 citations