William G. Macready

Artificial Intelligence · 1997–2019

A researcher in the Science corpus: 6 papers; trusts 296 researchers and is trusted by 149 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #20,579 of 35,345 by trust here, #24,417 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #9,245 of 21,575 by trust here, #12,172 by citations
• Generative Models — 55 citations into it; #870 of 1,956 by trust here, #1,336 by citations
• Probabilistic & Bayesian Methods — 50 citations into it
• Optimization & Training — 20 citations into it
• Model Architectures — 12 citations into it
• Statistical and Nonlinear Physics — 10 citations into it; #3,328 of 10,127 by trust here, #3,735 by citations
• Signal Processing — 8 citations into it; #4,615 of 7,572 by trust here, #4,373 by citations

Whom William G. Macready trusts overall

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

1. Diederik P. Kingma
2. Andriy Mnih
3. Ruslan Salakhutdinov
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Alex Graves
7. Ronald J. Williams
8. Radford M. Neal
9. Max Welling
10. Yann LeCun

Who trusts William G. Macready overall

1. Seyedali Mirjalili
2. Arash Vahdat
3. Xin-She Yang
4. Efrén Mezura-Montes
5. Seyed Mohammad Mirjalili
6. Frank Hutter
7. Christian Igel
8. Amir H. Gandomi
9. Xin Yao
10. Thomas Weise

Papers from William G. Macready's perspective

Ordered by how strongly the researchers William G. Macready 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Parameter space exploration with Gaussian process trees (2004) — 82 citations
• DVAE++: Discrete Variational Autoencoders with Overlapping Transformations (2018) — 81 citations
• A practical heuristic for finding graph minors (2014) — 361 citations
• Undirected Graphical Models as Approximate Posteriors (2019) — 15 citations
• DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors (2018) — 56 citations
• No free lunch theorems for optimization (1997)