John Aslanides
Artificial Intelligence · arXiv.org · 2018–2025
A researcher in the Science corpus: 11 papers; trusts 164 researchers and is trusted by 27 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 409 citations into it; #7,475 of 35,345 by trust here, #1,383 by citations
- Reinforcement Learning — 236 citations into it; #744 of 2,242 by trust here, #669 by citations
- Natural Language Processing — 119 citations into it
- Computer Vision and Pattern Recognition — 103 citations into it
- Safety, Robustness & Interpretability — 84 citations into it
- Management Science and Operations Research — 60 citations into it; #3,080 of 11,946 by trust here, #1,867 by citations
- Evaluation & Benchmarks — 54 citations into it
- Optimization & Training — 50 citations into it
Whom John Aslanides trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Hado van Hasselt
- Geoffrey E. Hinton
- Diederik P. Kingma
- Volodymyr Mnih
- Alex Krizhevsky
- Yoshua Bengio
- Andrew Barto
- Marc G. Bellemare
- David Silver
Who trusts John Aslanides overall
- Ian Osband
- Brendan O'Donoghue
- Wendelin Böhmer
- Aravind Rajeswaran
- Scott Fujimoto
- Pieter Abbeel
- Kevin Lu
- Kimin Lee
- Yuri Burda
- Tianhe Yu
Papers from John Aslanides's perspective
Ordered by how strongly the researchers John Aslanides 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
- Improving alignment of dialogue agents via targeted human judgements (2022) — 676 citations
- Randomized Prior Functions for Deep Reinforcement Learning (2018) — 434 citations
- Red Teaming Language Models with Language Models (2022) — 1,219 citations
- Teaching language models to support answers with verified quotes (2022) — 349 citations
- When to use parametric models in reinforcement learning? (2019) — 211 citations
- Acme: A Research Framework for Distributed Reinforcement Learning (2020) — 248 citations
- Behaviour Suite for Reinforcement Learning (2019) — 212 citations
- TF-Replicator: Distributed Machine Learning for Researchers (2019) — 20 citations
- Fine-tuning language models to find agreement among humans with diverse preferences (2022) — 336 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations