Arash Mehrjou
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 4 papers; trusts 55 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 76 citations into it; #12,890 of 35,345 by trust here, #11,921 by citations
- Computer Vision and Pattern Recognition — 29 citations into it; #8,755 of 21,575 by trust here, #12,689 by citations
- Probabilistic & Bayesian Methods — 26 citations into it; #806 of 1,525 by trust here, #904 by citations
- Representation & Self-Supervised Learning — 24 citations into it
- Signal Processing — 16 citations into it
- Statistics and Probability — 16 citations into it
- Natural Language Processing — 12 citations into it
- Optimization & Training — 12 citations into it
Whom Arash Mehrjou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Aapo Hyvärinen
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Yann LeCun
- Radford M. Neal
- Pascal Vincent
Who trusts Arash Mehrjou overall
- Yang Song
- Zengyi Li
- Calvin Luo
- Zahra Kadkhodaie
- Akisato Kimura
- Charlie Nash
- Arash Vahdat
- Yubei Chen
- Stefano Ermon
- Francesco Locatello
Papers from Arash Mehrjou's perspective
Ordered by how strongly the researchers Arash Mehrjou 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Deep Energy Estimator Networks (2018) — 81 citations
- The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA (2019) — 118 citations
- Fidelity-Weighted Learning (2017) — 78 citations
- Real-time prediction of COVID-19 related mortality using electronic health records (2020) — 55 citations