Saeed Saremi
Artificial Intelligence · 2007–2019
A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 30 citations into it; #6,456 of 35,345 by trust here, #14,438 by citations
- Probabilistic & Bayesian Methods — 28 citations into it; #465 of 1,525 by trust here, #751 by citations
- Computer Vision and Pattern Recognition — 21 citations into it; #4,953 of 21,575 by trust here, #10,334 by citations
- Statistics and Probability — 12 citations into it
- Optimization & Training — 9 citations into it
- Representation & Self-Supervised Learning — 9 citations into it
- Generative Models — 8 citations into it; #829 of 1,956 by trust here, #1,382 by citations
- Model Architectures — 6 citations into it
Whom Saeed Saremi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Aapo Hyvärinen
- Diederik P. Kingma
- Yoshua Bengio
- Pascal Vincent
- David John Cameron MacKay
- John J. Hopfield
- Jimmy Ba
- Yann LeCun
- Alex Krizhevsky
Who trusts Saeed Saremi overall
- Zengyi Li
- Yang Song
- Zahra Kadkhodaie
- Yubei Chen
- Calvin Luo
- Stefano Ermon
- Arash Vahdat
- Friedrich T. Sommer
- Minghuan Liu
- Jiaming Song
Papers from Saeed Saremi's perspective
Ordered by how strongly the researchers Saeed Saremi 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Deep Energy Estimator Networks (2018) — 81 citations
- Neural Empirical Bayes (2019) — 88 citations
- Hierarchical model of natural images and the origin of scale invariance (2013) — 46 citations
- RKKY in half-filled bipartite lattices: Graphene as an example (2007) — 162 citations