Ankesh Anand
Artificial Intelligence · 2017–2025
A researcher in the Science corpus: 5 papers; trusts 36 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 78 citations into it
- Computer Vision and Pattern Recognition — 46 citations into it
- Reinforcement Learning — 39 citations into it
Whom Ankesh Anand trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
- Richard S. Sutton
- Ilya Sutskever
- Yann LeCun
- Sergey Ioffe
- Marc G. Bellemare
Who trusts Ankesh Anand overall
Papers from Ankesh Anand's perspective
Ordered by how strongly the researchers Ankesh Anand trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- HoME: a Household Multimodal Environment (2017) — 107 citations
- Data-Efficient Reinforcement Learning with Self-Predictive Representations (2020) — 426 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
- Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (2023) — 337 citations