Umut Simsekli
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 5 papers; trusts 62 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 90 citations into it; #26,283 of 35,345 by trust here, #20,377 by citations
- Computer Vision and Pattern Recognition — 55 citations into it
- Optimization & Training — 46 citations into it
- Computational Mechanics — 33 citations into it
- Computer Networks and Communications — 21 citations into it
- Statistics and Probability — 21 citations into it
- Signal Processing — 11 citations into it
- Mathematical Physics — 7 citations into it
Whom Umut Simsekli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Radford M. Neal
- Geoffrey E. Hinton
- Yurii E. Nesterov
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- Max Welling
- L. Eon Bottou
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Umut Simsekli overall
- Stephanie L. Hyland
- Jingzhao Zhang
- Kumar Kshitij Patel
- Shruti Tople
- Pan Zhou
- Sai Praneeth Karimireddy
- Aymeric Dieuleveut
- Andreas Veit
- Jiashi Feng
- Seungyeon Kim
Papers from Umut Simsekli's perspective
Ordered by how strongly the researchers Umut Simsekli 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks (2019) — 354 citations
- Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization (2018) — 22 citations
- Relative Positional Encoding for Transformers with Linear Complexity (2021) — 64 citations
- Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks (2019) — 33 citations
- Generalized Sliced Wasserstein Distances (2019) — 389 citations