Jonas Köhler
Artificial Intelligence · with Lucchi · 2018–2020
A researcher in the Science corpus: 6 papers; trusts 97 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 144 citations into it; #27,837 of 35,345 by trust here, #20,377 by citations
- Optimization & Training — 128 citations into it
- Computational Mechanics — 44 citations into it; #7,873 of 10,001 by trust here, #4,600 by citations
- Computer Vision and Pattern Recognition — 37 citations into it
- Computational Theory and Mathematics — 26 citations into it
- Model Architectures — 21 citations into it
- Management Science and Operations Research — 20 citations into it
- Numerical Analysis — 16 citations into it
Whom Jonas Köhler trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yurii E. Nesterov
- James Martens
- L. Eon Bottou
- Diederik P. Kingma
- Yann LeCun
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Barak A. Pearlmutter
- John C. Duchi
Who trusts Jonas Köhler overall
- Lei Huang
- Piotr A. Sokól
- Leonard Adolphs
- Xiaoxiao Li
- Il-Su Park
- Yi Zhou
- Giambattista Parascandolo
- Zhiyuan Li
- Sanjeev Arora
- Matthew Staib
Papers from Jonas Köhler's perspective
Ordered by how strongly the researchers Jonas Köhler 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)
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization (2018) — 98 citations
- Escaping Saddles with Stochastic Gradients (2018) — 176 citations
- Batch normalization provably avoids ranks collapse for randomly initialised deep networks (2020) — 70 citations
- Adaptive norms for deep learning with regularized Newton methods (2019) — 3 citations
- Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training (2019) — 12 citations
- Towards a Theoretical Understanding of Batch Normalization (2018) — 50 citations