Eric J. Humphrey

Computer Vision and Pattern Recognition · 2012–2017

A researcher in the Science corpus: 6 papers; trusts 41 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 31 citations into it; #8,579 of 21,575 by trust here, #17,757 by citations
• Signal Processing — 23 citations into it; #1,992 of 7,572 by trust here, #3,913 by citations
• Artificial Intelligence — 16 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Eric J. Humphrey trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Koray Kavukcuoglu
5. Diederik P. Kingma
6. Radford M. Neal
7. Alex Krizhevsky
8. Juan Pablo Bello
9. Ilya Sutskever
10. Raia Hadsell

Who trusts Eric J. Humphrey overall

1. Colin Raffel
2. Li Deng
3. Brian McFee
4. Juan Pablo Bello
5. Siddharth Sigtia
6. Bob L. Sturm
7. Keunwoo Choi
8. Justin Salamon
9. Jong Wook Kim
10. Emmanouil Benetos

Papers from Eric J. Humphrey's perspective

Ordered by how strongly the researchers Eric J. Humphrey 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. 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

• Rethinking Automatic Chord Recognition with Convolutional Neural Networks (2012) — 137 citations
• Singing Voice Separation with Deep U-Net Convolutional Networks (2017) — 462 citations
• A Software Framework for Musical Data Augmentation (2015) — 144 citations
• MIR_EVAL: A Transparent Implementation of Common MIR Metrics (2014) — 639 citations
• Feature learning and deep architectures: new directions for music informatics (2013) — 139 citations
• Moving Beyond Feature Design: Deep Architectures and Automatic Feature Learning in Music Informatics (2012) — 154 citations