Michael E. Houle

Artificial Intelligence · 2010–2018

A researcher in the Science corpus: 6 papers; trusts 76 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #14,033 of 35,345 by trust here, #6,848 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #15,022 of 21,575 by trust here, #9,079 by citations
• Safety, Robustness & Interpretability — 35 citations into it
• Signal Processing — 25 citations into it; #4,999 of 7,572 by trust here, #6,591 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Optimization & Training — 11 citations into it
• Information Retrieval & Search — 9 citations into it; #714 of 1,022 by trust here, #939 by citations
• Computer Graphics and Computer-Aided Design — 7 citations into it

Whom Michael E. Houle trusts overall

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

1. Gonzalo Navarro
2. Yann LeCun
3. William W. Pugh
4. Nong Ye
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Sunil Arya
8. Marius Muja
9. Yoshua Bengio
10. Robert Krauthgamer

Who trusts Michael E. Houle overall

1. Ricardo José Gabrielli Barreto Campello
2. Laurent Amsaleg
3. Peer Kröger
4. Xingjun Ma
5. Jörg Sander
6. Arthur Zimek
7. David Stutz
8. Junnan Li
9. Yisen Wang
10. Hans-Peter Kriegel

Papers from Michael E. Houle's perspective

Ordered by how strongly the researchers Michael E. Houle 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality (2018) — 849 citations
• Dimensionality-Driven Learning with Noisy Labels (2018) — 475 citations
• The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality (2017) — 62 citations
• Rank-Based Similarity Search: Reducing the Dimensional Dependence (2015) — 66 citations
• Estimating Local Intrinsic Dimensionality (2015) — 168 citations
• Can Shared-Neighbor Distances Defeat the Curse of Dimensionality? (2010) — 305 citations