Rohan Anil
Artificial Intelligence · 2016–2025
A researcher in the Science corpus: 16 papers; trusts 535 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 556 citations into it; #18,734 of 35,345 by trust here, #1,734 by citations
- Computer Vision and Pattern Recognition — 263 citations into it; #13,135 of 21,575 by trust here, #5,422 by citations
- Natural Language Processing — 112 citations into it
- Optimization & Training — 104 citations into it; #1,992 of 2,601 by trust here, #2,112 by citations
- Representation & Self-Supervised Learning — 66 citations into it; #3,118 of 3,145 by trust here, #774 by citations
- Model Architectures — 52 citations into it
- Large Language Models — 51 citations into it
- Efficiency & Compression — 35 citations into it; #731 of 1,264 by trust here, #422 by citations
Whom Rohan Anil trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Jimmy Ba
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Alex Graves
- Ross B. Girshick
Who trusts Rohan Anil overall
- Rodrigo Nogueira
- Jiaxi Tang
- Lala Li
- Jihong Park
- Ashok Cutkosky
- Xu Tan
- Lin Wang
- Jie Huang
- Zhiqiang Shen
- Xuandong Zhao
Papers from Rohan Anil's perspective
Ordered by how strongly the researchers Rohan Anil 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
- ImageNet: A large-scale hierarchical image database
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- PaLM 2 Technical Report (2023) — 1,524 citations
- Knowledge distillation: A good teacher is patient and consistent (2021) — 403 citations
- Large scale distributed neural network training through online distillation (2018) — 455 citations
- Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling (2019) — 215 citations
- Wide & Deep Learning for Recommender Systems (2016) — 4,198 citations
- Efficiently Identifying Task Groupings for Multi-Task Learning (2021) — 332 citations
- Large-Scale Differentially Private BERT (2021) — 162 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
- Multi-task Learning (2020)
- Revisiting the Generalization of Adaptive Gradient Methods (2019) — 5 citations
- Memory-Efficient Adaptive Optimization for Large-Scale Learning (2019) — 50 citations
- Robust Bi-Tempered Logistic Loss Based on Bregman Divergences (2019) — 147 citations
- TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (2018) — 146 citations