Martin Tak'avc
Optimization & Training · 2019
A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Optimization & Training — 57 citations into it
- Artificial Intelligence — 49 citations into it
- Computational Mechanics — 37 citations into it
- Numerical Analysis — 13 citations into it
- Computational Theory and Mathematics — 12 citations into it
- Computer Networks and Communications — 12 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Statistics and Probability — 7 citations into it
Whom Martin Tak'avc trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jorge Nocedal
- L. Eon Bottou
- Diederik P. Kingma
- Laurent Condat
- Yann LeCun
- Yurii E. Nesterov
- Peter Richtárik
- Geoffrey E. Hinton
- Matthew D. Zeiler
- Jakub Konecný
Papers from Martin Tak'avc's perspective
Ordered by how strongly the researchers Martin Tak'avc trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
Their papers
- Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1 (2019) — 17 citations
- Distributed Fixed Point Methods with Compressed Iterates (2019) — 18 citations