Daniel Reichman

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 9 citations into it
• Statistical Learning Theory — 5 citations into it
• Optimization & Training — 5 citations into it

Whom Daniel Reichman trusts overall

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

1. David Haussler
2. Alon Brutzkus
3. Surbhi Goel
4. Amir Globerson
5. Francis R. Bach

Papers from Daniel Reichman's perspective

Ordered by how strongly the researchers Daniel Reichman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications
2. Breaking the Curse of Dimensionality with Convex Neural Networks
3. Original Contribution: Training a 3-node neural network is NP-complete
4. Reliably Learning the ReLU in Polynomial Time
5. A threshold of ln n for approximating set cover (preliminary version)
6. Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. ImageNet classification with deep convolutional neural networks
9. Learning Multiple Layers of Features from Tiny Images
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• The Computational Complexity of Training ReLU(s) (2018) — 47 citations