Benjamin David Haeffele

Optimization & Training · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 32 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 18 citations into it; #2,354 of 2,601 by trust here, #1,942 by citations
• Artificial Intelligence — 16 citations into it; #30,300 of 35,345 by trust here, #23,077 by citations
• Computational Mechanics — 13 citations into it; #2,142 of 10,001 by trust here, #1,816 by citations
• Signal Processing — 11 citations into it
• Electrical and Electronic Engineering — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Benjamin David Haeffele trusts overall

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

1. Francis R. Bach
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Yann LeCun
7. Julien Mairal
8. Tamara G. Kolda
9. David E. Rumelhart
10. Ruslan Salakhutdinov

Who trusts Benjamin David Haeffele overall

1. Simon Shaolei Du
2. Jason D. Lee
3. Quynh N. Nguyen
4. Qiuwei Li
5. Sanjeev Arora
6. Itay Safran
7. Pratik Chaudhari
8. Yassine Benyahia
9. Colin Wei
10. René Vidal

Papers from Benjamin David Haeffele's perspective

Ordered by how strongly the researchers Benjamin David Haeffele 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Global Optimality in Neural Network Training (2017) — 125 citations
• Global Optimality in Tensor Factorization, Deep Learning, and Beyond (2015) — 151 citations