Sebastian Borgeaud
Artificial Intelligence · 2019–2025
A researcher in the Science corpus: 18 papers; trusts 199 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 597 citations into it
- Computer Vision and Pattern Recognition — 347 citations into it
- Natural Language Processing — 154 citations into it
- Model Architectures — 135 citations into it
- Reinforcement Learning — 71 citations into it
- Efficiency & Compression — 67 citations into it
- Generative Models — 33 citations into it
Whom Sebastian Borgeaud trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Alex Graves
- Kaiming He
- Yann LeCun
- Oriol Vinyals
- Jimmy Ba
Who trusts Sebastian Borgeaud overall
- Yi Tay
- Wayne Xin Zhao
- Niklas Muennighoff
- Q. Liao
- Wenhu Chen
- William Fedus
- Jason Wei
- Ankit Gupta
- Yuhui Li
- Sewon Min
Papers from Sebastian Borgeaud's perspective
Ordered by how strongly the researchers Sebastian Borgeaud 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
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
- Training Compute-Optimal Large Language Models (2022) — 3,535 citations
- Emergent Abilities of Large Language Models (2022) — 3,700 citations
- Improving language models by retrieving from trillions of tokens (2021) — 1,785 citations
- Unsupervised Learning of Object Keypoints for Perception and Control (2019) — 218 citations
- Perceiver IO: A General Architecture for Structured Inputs & Outputs (2021) — 871 citations
- Unified Scaling Laws for Routed Language Models (2022) — 285 citations
- General-purpose, long-context autoregressive modeling with Perceiver AR (2022) — 87 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
- Accelerating Large Language Model Decoding with Speculative Sampling (2023) — 973 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
- Flamingo: a Visual Language Model for Few-Shot Learning (2022) — 6,220 citations
- An empirical analysis of compute-optimal large language model training (2022) — 516 citations
- OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations