Andrew J. Ballard

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 27 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #35,009 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 66 citations into it
• Model Architectures — 37 citations into it
• Reinforcement Learning — 36 citations into it; #2,231 of 2,242 by trust here, #1,501 by citations

Whom Andrew J. Ballard trusts overall

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

1. Ronald J. Williams
2. Alex Graves
3. J. Urgen Schmidhuber
4. Diederik P. Kingma
5. Yoshua Bengio
6. Geoffrey E. Hinton
7. Sepp Hochreiter
8. Yann LeCun
9. Richard S. Sutton
10. Alex Krizhevsky

Who trusts Andrew J. Ballard overall

1. Felix Dräxler
2. Alvaro Sanchez-Gonzalez
3. Mikael Henaff
4. Mario Geiger
5. Victor Bapst
6. Clemens Rosenbaum
7. Stefano Spigler

Papers from Andrew J. Ballard's perspective

Ordered by how strongly the researchers Andrew J. Ballard 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Metacontrol for Adaptive Imagination-Based Optimization (2017) — 72 citations
• Highly accurate protein structure prediction for the human proteome (2021) — 2,524 citations
• Energy landscapes for machine learning. (2017) — 101 citations
• Relational inductive biases, deep learning, and graph networks (2018) — 3,656 citations