Guido Montúfar
Artificial Intelligence · 2010–2021
A researcher in the Science corpus: 7 papers; trusts 84 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 76 citations into it; #8,621 of 35,345 by trust here, #4,772 by citations
- Graph Learning — 44 citations into it
- Computer Vision and Pattern Recognition — 38 citations into it; #9,896 of 21,575 by trust here, #14,478 by citations
- Model Architectures — 27 citations into it; #406 of 2,278 by trust here, #426 by citations
- Statistical and Nonlinear Physics — 19 citations into it
- Probabilistic & Bayesian Methods — 13 citations into it; #1,268 of 1,525 by trust here, #1,390 by citations
- Optimization & Training — 11 citations into it
- Algorithms & Complexity — 10 citations into it
Whom Guido Montúfar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Razvan Pascanu
- Nicolas Le Roux
- Alex Krizhevsky
- Diederik P. Kingma
- Yann LeCun
- Simon Osindero
- Radford M. Neal
Who trusts Guido Montúfar overall
- Yoshua Bengio
- Or Sharir
- Razvan Pascanu
- Kyunghyun Cho
- Asja Fischer
- Nadav Cohen
- Amnon Shashua
- Thiago Serra
- Johannes Schmidt-Hieber
- Byeongho Heo
Papers from Guido Montúfar's perspective
Ordered by how strongly the researchers Guido Montúfar 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
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- On the Number of Linear Regions of Deep Neural Networks (2014) — 1,419 citations
- Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units (2013) — 41 citations
- Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines (2010) — 96 citations
- On the number of response regions of deep feed forward networks with piece-wise linear activations (2013) — 285 citations
- Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021) — 342 citations
- Weisfeiler and Lehman Go Cellular: CW Networks (2021) — 317 citations
- On the number of inference regions of deep feed forward networks with piece-wise linear activations (2013) — 84 citations