Madhu S. Advani

Artificial Intelligence · 2013–2019

A researcher in the Science corpus: 7 papers; trusts 110 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #4,756 of 35,345 by trust here, #5,600 by citations
• Scaling & Learning Theory — 69 citations into it; #84 of 428 by trust here, #118 by citations
• Optimization & Training — 50 citations into it
• Computational Mechanics — 46 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Statistics and Probability — 32 citations into it; #6,441 of 12,932 by trust here, #7,508 by citations
• Statistical Learning Theory — 30 citations into it
• Statistical and Nonlinear Physics — 24 citations into it; #919 of 10,127 by trust here, #769 by citations

Whom Madhu S. Advani trusts overall

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

1. David L. Donoho
2. E.J. Candes
3. Yann LeCun
4. Surya Ganguli
5. Robert Tibshirani
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Haim Sompolinsky
9. Sundeep Rangan
10. Andrea Montanari

Who trusts Madhu S. Advani overall

1. Andrew M. Saxe
2. Mario Geiger
3. Abdulkadir Canatar
4. Blake Bordelon
5. Yamini Bansal
6. Zhenyu A. Liao
7. Surya Ganguli
8. Stefano Spigler
9. Ping Luo
10. Gauthier Gidel

Papers from Madhu S. Advani's perspective

Ordered by how strongly the researchers Madhu S. Advani 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• High-dimensional dynamics of generalization error in neural networks (2017) — 540 citations
• On the information bottleneck theory of deep learning (2018) — 692 citations
• Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup (2019) — 186 citations
• Statistical Mechanics of Optimal Convex Inference in High Dimensions (2016) — 57 citations
• Statistical mechanics of complex neural systems and high dimensional data (2013) — 79 citations
• An equivalence between high dimensional Bayes optimal inference and M-estimation (2016) — 16 citations
• Minnorm training: an algorithm for training over-parameterized deep neural networks (2018) — 18 citations