Ami Wiesel
Electrical and Electronic Engineering · 2006–2018
A researcher in the Science corpus: 6 papers; trusts 29 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 31 citations into it; #793 of 16,539 by trust here, #4,881 by citations
- Artificial Intelligence — 22 citations into it; #9,004 of 35,345 by trust here, #15,004 by citations
- Computer Networks and Communications — 22 citations into it; #1,491 of 9,886 by trust here, #2,424 by citations
- Optimization & Training — 13 citations into it
- Signal Processing — 12 citations into it; #6,231 of 7,572 by trust here, #7,324 by citations
- Model Architectures — 7 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it; #17,848 of 21,575 by trust here, #21,103 by citations
Whom Ami Wiesel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Johan Löfberg
- L. Eon Bottou
- Giuseppe Caire
- Geoffrey E. Hinton
- Kaiming He
- Quentin H. Spencer
- Yann LeCun
- Diederik P. Kingma
- Karol Gregor
- David E. Rumelhart
Who trusts Ami Wiesel overall
- Rui Zhang
- Emil Björnson
- Wei Yu
- Gan Zheng
- Qingqing Wu
- Ying-Chang Liang
- David Gesbert
- Jehad M. Hamamreh
- Yiliang Liu
- Eleftherios Karipidis
Papers from Ami Wiesel's perspective
Ordered by how strongly the researchers Ami Wiesel trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Going deeper with convolutions
- 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)
Their papers
- Linear precoding via conic optimization for fixed MIMO receivers (2006) — 1,024 citations
- Deep MIMO detection (2017) — 445 citations
- Zero-Forcing Precoding and Generalized Inverses (2008) — 618 citations
- Learning to Detect (2018) — 494 citations
- Dynamic reconfiguration of the default mode network during narrative comprehension (2016) — 577 citations
- Distributed principal component analysis on networks via directed graphical models (2012) — 25 citations