Travis Desell

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 293 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #7,015 of 35,345 by trust here, #20,836 by citations
• Model Architectures — 35 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it; #14,744 of 21,575 by trust here, #19,310 by citations
• Optimization & Training — 13 citations into it
• Electrical and Electronic Engineering — 9 citations into it; #12,770 of 16,539 by trust here, #15,217 by citations
• Neural Architecture Search & AutoML — 9 citations into it
• Evolutionary Computation — 5 citations into it

Whom Travis Desell trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Kaiming He
6. J. Urgen Schmidhuber
7. Risto Miikkulainen
8. Kenneth O. Stanley
9. Ilya Sutskever
10. Faustino J. Gomez

Who trusts Travis Desell overall

1. A. Elsaid
2. Emmanuel Dufourq
3. Alexander Ororbia
4. Haifeng Jin
5. Andrés Camero
6. Bruce A. Bassett
7. Qingquan Song
8. Steven A. Benson
9. Enrique Alba
10. Xia Hu

Papers from Travis Desell's perspective

Ordered by how strongly the researchers Travis Desell 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Evolving Recurrent Neural Networks for Time Series Data Prediction of Coal Plant Parameters (2019) — 30 citations
• Investigating recurrent neural network memory structures using neuro-evolution (2019) — 62 citations
• Using ant colony optimization to optimize long short-term memory recurrent neural networks (2018) — 30 citations
• Large scale evolution of convolutional neural networks using volunteer computing (2017) — 77 citations