Houtao Deng

Artificial Intelligence · 2012–2018

A researcher in the Science corpus: 8 papers; trusts 71 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #2,978 of 35,345 by trust here, #6,796 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #4,824 of 21,575 by trust here, #6,089 by citations
• Molecular Biology — 17 citations into it; #2,676 of 17,773 by trust here, #13,009 by citations
• Statistics and Probability — 16 citations into it
• Algorithms & Complexity — 15 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Information Systems — 14 citations into it
• Statistical Learning Theory — 10 citations into it

Whom Houtao Deng trusts overall

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

1. Leo Breiman
2. Jerome H. Friedman
3. Isabelle M Guyon
4. Andy Liaw
5. Trevor J. Hastie
6. George C. Runger
7. Robert Tibshirani
8. Rakesh Agrawal
9. Pedro M. Domingos
10. J. Ross Quinlan

Who trusts Houtao Deng overall

1. Miron Bartosz Kursa
2. Osbert Bastani
3. Satoshi Hara
4. Emilio Carrizosa
5. Gary Smith
6. Gérard Biau
7. Gilles Vandewiele
8. Mengnan Du
9. Stefano Nembrini
10. Xin Wang

Papers from Houtao Deng's perspective

Ordered by how strongly the researchers Houtao Deng 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A time series forest for classification and feature extraction (2013) — 622 citations
• Interpreting tree ensembles with inTrees (2014) — 272 citations
• Gene selection with guided regularized random forest (2012) — 304 citations
• Guided Random Forest in the RRF Package (2013) — 76 citations
• CBC: An associative classifier with a small number of rules (2014) — 40 citations
• Interpreting tree ensembles with inTrees (2018)
• Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation (2015) — 800 citations
• Feature selection via regularized trees (2012) — 227 citations