Dan Tu

Optimization & Training · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Dan Tu trusts overall

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

  1. Simon Lacoste-Julien

Who trusts Dan Tu overall

  1. Sebastian Pokutta
  2. Cyrille W. Combettes
  3. Alejandro Carderera

Papers from Dan Tu's perspective

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

  1. Frank-Wolfe with Subsampling Oracle
  2. On the Global Linear Convergence of Frank-Wolfe Optimization Variants
  3. ImageNet classification with deep convolutional neural networks
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. ImageNet: A large-scale hierarchical image database
  6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  7. Attention is All you Need (Neural Information Processing Systems)
  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. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

Their papers