Nathan D. Cahill

Artificial Intelligence · 2004–2018

A researcher in the Science corpus: 4 papers; trusts 137 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #5,706 of 35,345 by trust here, #8,283 by citations
• Computer Vision and Pattern Recognition — 42 citations into it
• Graph Learning — 22 citations into it
• Signal Processing — 22 citations into it
• Model Architectures — 17 citations into it
• Optimization & Training — 15 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Statistical and Nonlinear Physics — 11 citations into it

Whom Nathan D. Cahill trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Yoshua Bengio
6. Diederik P. Kingma
7. Longxin Lin
8. Ronald Kemker
9. X. Zhang
10. Charu C. Aggarwal

Who trusts Nathan D. Cahill overall

1. Tyler L. Hayes
2. Vincenzo Lomonaco
3. Christopher Kanan
4. Arslan Chaudhry
5. James Smith
6. Chris Dongjoo Kim
7. Davide Maltoni
8. German Ignacio Parisi
9. Timothée Lesort
10. Jiangpeng He

Papers from Nathan D. Cahill's perspective

Ordered by how strongly the researchers Nathan D. Cahill 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Memory Efficient Experience Replay for Streaming Learning (2018) — 266 citations
• New Metrics and Experimental Paradigms for Continual Learning (2018) — 37 citations
• Robust Spatial Filtering With Graph Convolutional Neural Networks (2017) — 142 citations
• Fibonacci and Lucas Numbers as Tridiagonal Matrix Determinants (2004) — 81 citations