Mohammad Babaeizadeh

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 10 papers; trusts 591 researchers and is trusted by 174 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 215 citations into it; #2,828 of 21,575 by trust here, #3,124 by citations
• Artificial Intelligence — 165 citations into it; #25,216 of 35,345 by trust here, #5,880 by citations
• Reinforcement Learning — 111 citations into it; #1,395 of 2,242 by trust here, #728 by citations
• Generative Models — 107 citations into it; #728 of 1,956 by trust here, #439 by citations
• Control and Systems Engineering — 47 citations into it; #7,272 of 8,257 by trust here, #1,545 by citations
• Robotics & Control — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 21 citations into it

Whom Mohammad Babaeizadeh trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Chelsea Finn
4. Alex Krizhevsky
5. Alex Graves
6. Yoshua Bengio
7. Yann LeCun
8. Sergey Levine
9. Kaiming He
10. Ilya Sutskever

Who trusts Mohammad Babaeizadeh overall

1. Suraj Nair
2. Chelsea Finn
3. Sergey Levine
4. Danijar Hafner
5. Yuxi Li
6. Chen Sun
7. Steven Dalton
8. Pieter Abbeel
9. Karl Pertsch
10. Manoj Kumar

Papers from Mohammad Babaeizadeh's perspective

Ordered by how strongly the researchers Mohammad Babaeizadeh 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Stochastic Variational Video Prediction (2017) — 585 citations
• Model-Based Reinforcement Learning for Atari (2019) — 995 citations
• Phenaki: Variable Length Video Generation From Open Domain Textual Description (2022) — 572 citations
• Fast Generation for Convolutional Autoregressive Models (2017) — 67 citations
• VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation (2019) — 143 citations
• Seq-NMS for Video Object Detection (2016) — 323 citations
• Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU (2016) — 297 citations
• VideoFlow: A Flow-Based Generative Model for Video (2019) — 135 citations
• Time Reversal as Self-Supervision (2018) — 12 citations
• GA3C: GPU-based A3C for Deep Reinforcement Learning (2016) — 75 citations