Patrick Haffner

Artificial Intelligence · 1998–2014

A researcher in the Science corpus: 6 papers; trusts 41 researchers and is trusted by 14,783 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #30,603 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #92 of 21,575 by trust here, #90 by citations
• Optimization & Training — 17 citations into it
• Statistical Learning Theory — 13 citations into it; #764 of 1,038 by trust here, #569 by citations
• Model Architectures — 10 citations into it
• Scaling & Learning Theory — 10 citations into it
• Computational Mechanics — 9 citations into it
• Natural Language Processing — 6 citations into it

Whom Patrick Haffner trusts overall

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

1. Robert E. Schapire
2. L. Eon Bottou
3. Yann LeCun
4. Bernhard Schölkopf
5. Vladimir Naumovich Vapnik
6. Yoshua Bengio
7. Shai Shalev-Shwartz
8. T. J. Watson
9. Mehryar Mohri
10. Andrew Y. Ng

Who trusts Patrick Haffner overall

1. Yoshua Bengio
2. Yann LeCun
3. Ruslan Salakhutdinov
4. Trevor Darrell
5. Jitendra Malik
6. Kyunghyun Cho
7. Dacheng Tao
8. Quoc V. Le
9. Xiaogang Wang
10. Sergey Levine

Papers from Patrick Haffner's perspective

Ordered by how strongly the researchers Patrick Haffner 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Gradient-based learning applied to document recognition (1998)
• Object Recognition with Gradient-Based Learning (1999)
• Support vector machines for histogram-based image classification (1999)
• Accelerated Parallel Optimization Methods for Large Scale Machine Learning (2014) — 7 citations
• ACAS: automated construction of application signatures (2005) — 431 citations
• Rational Kernels: Theory and Algorithms (2004) — 206 citations