Majid Janzamin

Artificial Intelligence · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 39 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Statistical Learning Theory — 11 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Computational Mathematics — 8 citations into it
• Computational Mechanics — 8 citations into it
• Model Architectures — 8 citations into it

Whom Majid Janzamin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Andrew R. Barron
2. Aapo Hyvärinen
3. Anima Anandkumar
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Kevin Swersky
7. Marc'Aurelio Ranzato
8. Alex Krizhevsky
9. Yann LeCun
10. Tommi S. Jaakkola

Who trusts Majid Janzamin overall

1. Yuchen Zhang
2. Jean Kossaifi
3. Hossein Mobahi
4. Daniel Soudry
5. Jason D. Lee
6. Zachary Chase Lipton
7. Eugene Belilovsky
8. Zeyuan Allen-Zhu
9. Pratik Chaudhari
10. Yang He

Papers from Majid Janzamin's perspective

Ordered by how strongly the researchers Majid Janzamin trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods (2017) — 245 citations
• Supplementary Material for the AISTATS 2016 Paper: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models (2016) — 55 citations
• Generalization Bounds for Neural Networks through Tensor Factorization (2015) — 26 citations
• Score Function Features for Discriminative Learning: Matrix and Tensor Framework (2014) — 45 citations