Vladimir Feinberg
Artificial Intelligence · 2018–2025
A researcher in the Science corpus: 8 papers; trusts 11 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 216 citations into it; #23,506 of 35,345 by trust here, #3,895 by citations
- Computer Vision and Pattern Recognition — 86 citations into it
- Optimization & Training — 19 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
Whom Vladimir Feinberg trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Timothy P. Lillicrap
- Geoffrey E. Hinton
- Richard S. Sutton
- David Silver
- Yoshua Bengio
- Gabriel Kalweit
- Ilya Sutskever
- Diederik P. Kingma
- Junhyuk Oh
Who trusts Vladimir Feinberg overall
- Michael Janner
- Ignasi Clavera
- Aravind Rajeswaran
- Yilun Du
- Igor Mordatch
- Tingwu Wang
- Jesse Clifton
- Marvin Zhang
- Tonghan Wang
- Sergey Levine
Papers from Vladimir Feinberg's perspective
Ordered by how strongly the researchers Vladimir Feinberg trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- PaLM 2 Technical Report (2023) — 1,524 citations
- Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning (2018) — 351 citations
- On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent (2018) — 82 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Gemma: Open Models Based on Gemini Research and Technology (2024) — 1,150 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Gemma 3 Technical Report (2025) — 1,621 citations
- MedGemma Technical Report (2025) — 365 citations