Blake Aaron Richards

Cognitive Neuroscience · 2016–2022

A researcher in the Science corpus: 6 papers; trusts 96 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 138 citations into it; #3,979 of 10,852 by trust here, #3,544 by citations
• Cellular and Molecular Neuroscience — 75 citations into it
• Artificial Intelligence — 67 citations into it; #15,867 of 35,345 by trust here, #13,760 by citations
• Electrical and Electronic Engineering — 47 citations into it; #4,565 of 16,539 by trust here, #5,630 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Neuroscience — 36 citations into it
• Optimization & Training — 24 citations into it
• Model Architectures — 19 citations into it

Whom Blake Aaron Richards trusts overall

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

1. David John Cameron MacKay
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Barbara Olshausen
6. Ronald J. Williams
7. Alex Krizhevsky
8. Ilya Sutskever
9. Diederik P. Kingma
10. David E. Rumelhart

Who trusts Blake Aaron Richards overall

1. Grace W. Lindsay
2. Guangyu Robert Yang
3. James C. R. Whittington
4. Uri Hasson
5. Emre O. Neftci
6. Timo Flesch

Papers from Blake Aaron Richards's perspective

Ordered by how strongly the researchers Blake Aaron Richards 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures (2018) — 281 citations
• Towards deep learning with segregated dendrites (2016) — 423 citations
• A deep learning framework for neuroscience (2019) — 1,016 citations
• Catalyzing next-generation Artificial Intelligence through NeuroAI (2022) — 299 citations
• Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020) — 331 citations
• The Persistence and Transience of Memory. (2017) — 285 citations