Lars Buesing

Artificial Intelligence · 2008–2021

A researcher in the Science corpus: 16 papers; trusts 198 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 281 citations into it; #13,027 of 35,345 by trust here, #3,289 by citations
• Reinforcement Learning — 210 citations into it; #1,680 of 2,242 by trust here, #402 by citations
• Computer Vision and Pattern Recognition — 130 citations into it
• Cognitive Neuroscience — 92 citations into it; #1,930 of 10,852 by trust here, #2,336 by citations
• Representation & Self-Supervised Learning — 60 citations into it
• Probabilistic & Bayesian Methods — 49 citations into it
• Control and Systems Engineering — 45 citations into it
• Cellular and Molecular Neuroscience — 40 citations into it; #713 of 2,998 by trust here, #1,274 by citations

Whom Lars Buesing trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Radford M. Neal
5. Richard S. Sutton
6. Sergey Levine
7. Yann LeCun
8. Danilo Jimenez Rezende
9. Alex Krizhevsky
10. Alex Graves

Who trusts Lars Buesing overall

1. Wolfgang Maass
2. Nikolaus Kriegeskorte
3. Danijar Hafner
4. Karol Gregor
5. Danilo Jimenez Rezende
6. Bernhard Schölkopf
7. Yoshua Bengio
8. Sergey Levine
9. Amirhossein Tavanaei
10. Karl Pertsch

Papers from Lars Buesing's perspective

Ordered by how strongly the researchers Lars Buesing 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Neural scene representation and rendering (2018) — 674 citations
• Imagination-Augmented Agents for Deep Reinforcement Learning (2017) — 599 citations
• Learning and Querying Fast Generative Models for Reinforcement Learning (2018) — 135 citations
• Learning model-based planning from scratch (2017) — 102 citations
• Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search (2018) — 167 citations
• BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS (2015) — 175 citations
• Causally Correct Partial Models for Reinforcement Learning (2020) — 36 citations
• Counterfactual Credit Assignment in Model-Free Reinforcement Learning (2020) — 73 citations
• Representation Learning via Invariant Causal Mechanisms (2020) — 290 citations
• Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons (2011) — 133 citations
• Physically Embedded Planning Problems: New Challenges for Reinforcement Learning (2020) — 14 citations
• Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013) — 310 citations
• Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression (2008) — 154 citations
• Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011) — 441 citations
• Advancing mathematics by guiding human intuition with AI (2021) — 574 citations
• Neural belief states for partially observed domains (2018)