Laurence Aitchison

Artificial Intelligence · 2014–2021

A researcher in the Science corpus: 9 papers; trusts 334 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #10,945 of 35,345 by trust here, #13,962 by citations
• Cognitive Neuroscience — 46 citations into it; #6,509 of 10,852 by trust here, #8,086 by citations
• Optimization & Training — 42 citations into it; #1,351 of 2,601 by trust here, #2,378 by citations
• Probabilistic & Bayesian Methods — 34 citations into it; #738 of 1,525 by trust here, #720 by citations
• Cellular and Molecular Neuroscience — 25 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #9,009 of 21,575 by trust here, #10,905 by citations
• Statistical and Nonlinear Physics — 12 citations into it
• Model Architectures — 11 citations into it

Whom Laurence Aitchison trusts overall

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

1. Alex Graves
2. Radford M. Neal
3. James Martens
4. Diederik P. Kingma
5. Roger Baker Grosse
6. Kaiming He
7. Ilya Loshchilov
8. Yoshua Bengio
9. Geoffrey E. Hinton
10. L. Eon Bottou

Who trusts Laurence Aitchison overall

1. Frederik Benzing
2. Ilya Loshchilov
3. Greg Yang
4. Frank Hutter
5. Brenden M. Lake
6. Timothy Nguyen
7. Simon Schug
8. Dami Choi
9. Jaehoon Lee
10. Tomer D. Ullman

Papers from Laurence Aitchison's perspective

Ordered by how strongly the researchers Laurence Aitchison 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Convolutional Networks as shallow Gaussian Processes (2018) — 286 citations
• Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables (2016) — 85 citations
• Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods (2018) — 23 citations
• Synaptic plasticity as Bayesian inference (2021) — 48 citations
• A unified theory of adaptive stochastic gradient descent as Bayesian filtering (2018) — 4 citations
• Confidence matching in group decision-making (2017) — 135 citations
• Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit (2017) — 25 citations
• Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations (2015) — 22 citations
• The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014) — 74 citations