Benjamin Goodrich

Artificial Intelligence · 2014–2015

A researcher in the Science corpus: 5 papers; trusts 111 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #15,481 of 35,345 by trust here, #21,878 by citations
• Model Architectures — 23 citations into it
• Reinforcement Learning — 16 citations into it
• Optimization & Training — 15 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it; #13,136 of 21,575 by trust here, #17,258 by citations
• Computer Networks and Communications — 6 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Benjamin Goodrich trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Ian J. Goodfellow
4. Yoshua Bengio
5. J. E. Ramos
6. R. M. French
7. Paul J. Werbos
8. Itamar Arel
9. James Bergstra
10. Alex Krizhevsky

Who trusts Benjamin Goodrich overall

1. Heechul Jung
2. Jeongwoo Ju
3. Tomer Lancewicki
4. Minju Jung
5. Junmo Kim
6. Itamar Arel
7. Herbert Jaeger
8. Matthew Riemer

Papers from Benjamin Goodrich's perspective

Ordered by how strongly the researchers Benjamin Goodrich 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

• Mitigating Catastrophic Forgetting in Temporal Difference Learning with Function Approximation (2015)
• Neuron Clustering for Mitigating Catastrophic Forgetting in Supervised and Reinforcement Learning (2015) — 9 citations
• Sequential Covariance-Matrix Estimation with Application to Mitigating Catastrophic Forgetting (2015) — 7 citations
• Neuron clustering for mitigating catastrophic forgetting in feedforward neural networks (2014) — 8 citations
• Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks (2014) — 20 citations