Andrew Kyle Lampinen

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 18 papers; trusts 533 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 467 citations into it; #10,959 of 35,345 by trust here, #4,602 by citations
• Computer Vision and Pattern Recognition — 187 citations into it; #20,814 of 21,575 by trust here, #8,722 by citations
• Natural Language Processing — 142 citations into it
• Reinforcement Learning — 115 citations into it
• Cognitive Neuroscience — 77 citations into it
• Model Architectures — 71 citations into it
• Representation & Self-Supervised Learning — 65 citations into it
• Cognitive Science & Psychology — 61 citations into it

Whom Andrew Kyle Lampinen trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Alex Graves
9. Sepp Hochreiter
10. J. Urgen Schmidhuber

Who trusts Andrew Kyle Lampinen overall

1. James L. McClelland
2. J'er'emy Scheurer
3. Tyler Lee
4. Sebastian Lee
5. Felix Hill
6. Jie Huang
7. Jon Ander Campos
8. Jason Wei
9. Corey Lynch
10. Eric Zelikman

Papers from Andrew Kyle Lampinen's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Environmental drivers of systematicity and generalization in a situated agent (2019) — 111 citations
• Can language models learn from explanations in context? (2022) — 383 citations
• An analytic theory of generalization dynamics and transfer learning in deep linear networks (2018) — 152 citations
• What shapes feature representations? Exploring datasets, architectures, and training (2020) — 179 citations
• Semantic Exploration from Language Abstractions and Pretrained Representations (2022) — 84 citations
• Language models show human-like content effects on reasoning (2022) — 230 citations
• Automated curricula through setter-solver interactions (2019) — 60 citations
• Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022) — 389 citations
• Symbolic Behaviour in Artificial Intelligence (2021) — 40 citations
• Transforming task representations to perform novel tasks (2020) — 18 citations
• Towards mental time travel: a hierarchical memory for reinforcement learning agents (2021) — 61 citations
• Tell me why! Explanations support learning relational and causal structure (2021) — 51 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• Tell me why! - Explanations support learning of relational and causal structure (2021) — 6 citations
• Integration of New Information in Memory: New Insights from a Complementary Learning Systems Perspective (2020) — 113 citations
• Embedded Meta-Learning: Toward more flexible deep-learning models (2019)
• Emergent Systematic Generalization in a Situated Agent (2019) — 39 citations
• Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers