Thomas Kemp

Computer Vision and Pattern Recognition · 2017–2019

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

Subfields

• Computer Vision and Pattern Recognition — 14 citations into it; #18,159 of 21,575 by trust here, #13,815 by citations

Whom Thomas Kemp trusts overall

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

1. Alex Krizhevsky
2. Radford M. Neal
3. Dimitri P. Bertsekas

Who trusts Thomas Kemp overall

1. Naoya Takahashi

Papers from Thomas Kemp's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Learning Multiple Layers of Features from Tiny Images
3. ImageNet: A large-scale hierarchical image database
4. Categorical Reparameterization with Gumbel-Softmax
5. Pattern Recognition and Machine Learning
6. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
7. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
8. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding
9. MobileNetV2: Inverted Residuals and Linear Bottlenecks
10. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Their papers

• Improving music source separation based on deep neural networks through data augmentation and network blending (2017) — 226 citations
• Mixed Precision DNNs: All you need is a good parametrization (2019) — 165 citations