Stan Matwin

Artificial Intelligence · 1993–2019

A researcher in the Science corpus: 8 papers; trusts 175 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #34,813 of 35,345 by trust here, #25,040 by citations
• Safety, Robustness & Interpretability — 31 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Natural Language Processing — 15 citations into it
• Computational Theory and Mathematics — 10 citations into it
• Signal Processing — 9 citations into it
• Efficiency & Compression — 8 citations into it
• Model Architectures — 8 citations into it

Whom Stan Matwin trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Yoshua Bengio
5. R. Caruana
6. Thorsten Joachims
7. Ilya Sutskever
8. Kaiming He
9. Karen Simonyan
10. Diederik P. Kingma

Who trusts Stan Matwin overall

1. Khaled M. Hammouda
2. Sunghun Kim
3. Michael J. Pazzani
4. B. Liu
5. Dani Yogatama
6. Mohamed S. Kamel

Papers from Stan Matwin's perspective

Ordered by how strongly the researchers Stan Matwin 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. 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities (2019) — 7 citations
• Black Box Explanation by Learning Image Exemplars in the Latent Feature Space (2019) — 77 citations
• Improving the Interpretability of Deep Neural Networks with Knowledge Distillation (2018) — 114 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations
• A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013) — 319 citations
• Large Scale Text Classification using Semisupervised Multinomial Naive Bayes (2011) — 89 citations
• Feature Engineering for Text Classification (1999)
• Using Qualitative Models to Guide Inductive Learning (1993)