Bryon Aragam

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 82 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #9,659 of 35,345 by trust here, #13,353 by citations
• Statistics and Probability — 39 citations into it
• Optimization & Training — 33 citations into it
• Causal Inference & Discovery — 28 citations into it
• Probabilistic & Bayesian Methods — 26 citations into it
• Computational Mechanics — 21 citations into it
• Computational Theory and Mathematics — 17 citations into it; #5,033 of 12,182 by trust here, #3,924 by citations
• Management Science and Operations Research — 16 citations into it

Whom Bryon Aragam trusts overall

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

1. Peter Buhlmann
2. Cun-Hui Zhang
3. Robert Tibshirani
4. Po-Ling Loh
5. David Maxwell Chickering
6. Jan Peters
7. Jianqing Fan
8. Martin J. Wainwright
9. Nicolai Meinshausen
10. David E. Heckerman

Who trusts Bryon Aragam overall

1. Sébastien Lachapelle
2. Philippe Brouillard
3. Ignavier Ng
4. Gonçalo Faria
5. Roxana Pamfil
6. Lars Lorch
7. Shengyu Zhu
8. Xun Zheng
9. Tristan Deleu
10. Simon Lacoste-Julien

Papers from Bryon Aragam's perspective

Ordered by how strongly the researchers Bryon Aragam 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Regression Shrinkage and Selection via the Lasso

Their papers

• DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018) — 1,439 citations
• Learning Sparse Nonparametric DAGs (2019) — 339 citations
• DYNOTEARS: Structure Learning from Time-Series Data (2020) — 311 citations
• Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression (2015) — 47 citations
• Fault Tolerance in Iterative-Convergent Machine Learning (2018) — 50 citations
• Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017) — 23 citations