Nir Levine

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 8 papers; trusts 155 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 264 citations into it
• Reinforcement Learning — 171 citations into it
• Computer Vision and Pattern Recognition — 110 citations into it
• Management Science and Operations Research — 47 citations into it
• Computational Theory and Mathematics — 36 citations into it
• Control and Systems Engineering — 36 citations into it
• Optimization & Training — 35 citations into it
• Representation & Self-Supervised Learning — 30 citations into it

Whom Nir Levine trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Sergey Levine
8. Andrew Barto
9. Volodymyr Mnih
10. Kaiming He

Who trusts Nir Levine overall

1. Brian Ichter
2. Brandon Cui
3. Cosmin Paduraru
4. Gabriel Dulac-Arnold
5. Seyed Iman Mirzadeh
6. Arthur Argenson
7. Yinlam Chow
8. Pierre Sermanet

Papers from Nir Levine's perspective

Ordered by how strongly the researchers Nir Levine 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Improved Knowledge Distillation via Teacher Assistant (2019) — 1,428 citations
• Challenges of real-world reinforcement learning: definitions, benchmarks and analysis (2021) — 780 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Task-agnostic Continual Learning with Hybrid Probabilistic Models (2021) — 24 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• An empirical investigation of the challenges of real-world reinforcement learning (2020) — 140 citations
• Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint. (2020) — 44 citations
• Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control (2019) — 35 citations