Matan Haroush
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 2 papers; trusts 44 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 47 citations into it; #26,735 of 35,345 by trust here, #15,542 by citations
- Reinforcement Learning — 31 citations into it; #1,799 of 2,242 by trust here, #1,663 by citations
- Computer Vision and Pattern Recognition — 25 citations into it
- Natural Language Processing — 11 citations into it
- Model Architectures — 7 citations into it
- Optimization & Training — 6 citations into it
Whom Matan Haroush trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Christian Szegedy
- Diederik P. Kingma
- Sergey Ioffe
- Yann LeCun
- Ilya Sutskever
- Richard S. Sutton
Who trusts Matan Haroush overall
- Prithviraj Ammanabrolu
- Hongxu Yin
- Liangchen Luo
- James Smith
- Yaohui Cai
- Matthew J. Hausknecht
- Itay Hubara
- Zi-Mei Yao
- Mark Sandler
Papers from Matan Haroush's perspective
Ordered by how strongly the researchers Matan Haroush trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning (2018) — 218 citations
- The Knowledge Within: Methods for Data-Free Model Compression (2019) — 117 citations