Dmitry Kalashnikov

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 13 papers; trusts 175 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 387 citations into it; #23,017 of 35,345 by trust here, #2,822 by citations
• Computer Vision and Pattern Recognition — 265 citations into it; #7,811 of 21,575 by trust here, #1,467 by citations
• Reinforcement Learning — 234 citations into it; #863 of 2,242 by trust here, #186 by citations
• Control and Systems Engineering — 142 citations into it; #2,761 of 8,257 by trust here, #132 by citations
• Robotics & Control — 87 citations into it
• Biomedical Engineering — 42 citations into it; #4,671 of 10,734 by trust here, #1,313 by citations
• Management Science and Operations Research — 15 citations into it

Whom Dmitry Kalashnikov trusts overall

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

1. Sergey Levine
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yoshua Bengio
7. Chelsea Finn
8. Ilya Sutskever
9. Jan Peters
10. Pieter Abbeel

Who trusts Dmitry Kalashnikov overall

1. Sergey Levine
2. Ted Xiao
3. Chelsea Finn
4. Aviral Kumar
5. Suraj Nair
6. Fei Xia
7. Sergey I. Nikolenko
8. Yevgen Chebotar
9. Justin Fu
10. Nicklas Hansen

Papers from Dmitry Kalashnikov's perspective

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

Their papers

• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation (2018) — 1,735 citations
• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022) — 3,376 citations
• RT-1: Robotics Transformer for Real-World Control at Scale (2022) — 2,545 citations
• MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale (2021) — 331 citations
• Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks (2018) — 508 citations
• Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills (2021) — 178 citations
• Thinking While Moving: Deep Reinforcement Learning with Concurrent Control (2020) — 47 citations
• Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 (2023) — 1,105 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Gemini Robotics: Bringing AI into the Physical World (2025) — 385 citations
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023) — 3,674 citations
• The Forward Physics Facility at the High-Luminosity LHC (2022) — 274 citations
• Hybrid Random Features (2021) — 27 citations