Dmitry Lepikhin
Artificial Intelligence · 2019–2025
A researcher in the Science corpus: 12 papers; trusts 93 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 541 citations into it; #13,535 of 35,345 by trust here, #1,073 by citations
- Natural Language Processing — 316 citations into it; #4,584 of 5,688 by trust here, #588 by citations
- Computer Vision and Pattern Recognition — 236 citations into it; #9,722 of 21,575 by trust here, #1,698 by citations
- Model Architectures — 64 citations into it
- Efficiency & Compression — 59 citations into it; #796 of 1,264 by trust here, #127 by citations
- Optimization & Training — 52 citations into it
- Representation & Self-Supervised Learning — 40 citations into it
- Hardware and Architecture — 35 citations into it
Whom Dmitry Lepikhin trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Noam Shazeer
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Diederik P. Kingma
- Kaiming He
- Alex Graves
- Quoc V. Le
- Kyunghyun Cho
Who trusts Dmitry Lepikhin overall
- Yi Tay
- Hieu Pham
- Barret Zoph
- Joan Puigcerver
- Mostafa Dehghani
- Ankur Bapna
- William Fedus
- Carlos Riquelme
- Basil Mustafa
- Jiaao He
Papers from Dmitry Lepikhin's perspective
Ordered by how strongly the researchers Dmitry Lepikhin 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- LaMDA: Language Models for Dialog Applications (2022) — 1,891 citations
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding (2020) — 2,182 citations
- GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021) — 1,261 citations
- PaLM 2 Technical Report (2023) — 1,524 citations
- GSPMD: General and Scalable Parallelization for ML Computation Graphs (2021) — 188 citations
- Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges (2019) — 477 citations
- Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
- Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference (2021) — 136 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 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