Albin Cassirer

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 9 papers; trusts 115 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 233 citations into it; #10,951 of 35,345 by trust here, #1,636 by citations
• Reinforcement Learning — 149 citations into it; #913 of 2,242 by trust here, #978 by citations
• Computer Vision and Pattern Recognition — 107 citations into it
• Management Science and Operations Research — 42 citations into it; #3,185 of 11,946 by trust here, #3,247 by citations
• Generative Models — 38 citations into it
• Optimization & Training — 33 citations into it
• Cognitive Neuroscience — 14 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it

Whom Albin Cassirer trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Longxin Lin
4. Diederik P. Kingma
5. Marc G. Bellemare
6. Yoshua Bengio
7. Alex Krizhevsky
8. Ian Osband
9. Volodymyr Mnih
10. Yann LeCun

Who trusts Albin Cassirer overall

1. Ian Osband
2. Matthew W. Hoffman
3. Wendelin Böhmer
4. Aravind Rajeswaran
5. Scott Fujimoto
6. Brendan O'Donoghue

Papers from Albin Cassirer's perspective

Ordered by how strongly the researchers Albin Cassirer trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Improving language models by retrieving from trillions of tokens (2021) — 1,785 citations
• Randomized Prior Functions for Deep Reinforcement Learning (2018) — 434 citations
• Acme: A Research Framework for Distributed Reinforcement Learning (2020) — 248 citations
• Transformation-based Adversarial Video Prediction on Large-Scale Data (2020) — 95 citations
• Unified Scaling Laws for Routed Language Models (2022) — 285 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
• Reverb: A Framework For Experience Replay (2021) — 46 citations