Thomas Parnell

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it
• Electrical and Electronic Engineering — 27 citations into it
• Information Systems — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #15,125 of 21,575 by trust here, #20,271 by citations
• Optimization & Training — 8 citations into it
• Computational Mechanics — 7 citations into it
• Statistical and Nonlinear Physics — 7 citations into it
• Recommender Systems — 5 citations into it

Whom Thomas Parnell trusts overall

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

1. Shai Shalev-Shwartz
2. Inderjit S. Dhillon
3. Jaewon Yang
4. Chih-Jen Lin
5. Christina Heinze
6. Shin Matsushima
7. Tong Zhang
8. Celestine Mendler-Dünner
9. Jure Leskovec
10. Michelle Girvan

Who trusts Thomas Parnell overall

1. Rémi Le Priol
2. Chun-Hua Tsai
3. Georgina Peake

Papers from Thomas Parnell's perspective

Ordered by how strongly the researchers Thomas Parnell 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Neuromorphic computing with multi-memristive synapses (2017) — 751 citations
• Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering (2016) — 59 citations
• Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems (2017) — 17 citations