Ignacio Cases

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 118 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 135 citations into it; #7,617 of 35,345 by trust here, #3,338 by citations
• Reinforcement Learning — 62 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it; #5,754 of 21,575 by trust here, #2,688 by citations
• Model Architectures — 43 citations into it
• Representation & Self-Supervised Learning — 32 citations into it; #1,106 of 3,145 by trust here, #539 by citations
• Management Science and Operations Research — 23 citations into it
• Meta & Continual Learning — 23 citations into it; #241 of 1,041 by trust here, #63 by citations
• Natural Language Processing — 23 citations into it

Whom Ignacio Cases trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Matthew Riemer
5. J. Urgen Schmidhuber
6. Yoshua Bengio
7. Alex Krizhevsky
8. Ronald J. Williams
9. Yann LeCun
10. Ilya Sutskever

Who trusts Ignacio Cases overall

1. Matthew Riemer
2. Lucas Caccia
3. Zheda Mai
4. Zhen Wang
5. Marc Masana
6. Arslan Chaudhry
7. Seyed Iman Mirzadeh
8. Mehrdad Farajtabar
9. Matteo Boschini
10. Clemens Rosenbaum

Papers from Ignacio Cases's perspective

Ordered by how strongly the researchers Ignacio Cases 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018) — 934 citations
• Routing Networks and the Challenges of Modular and Compositional Computation (2019) — 93 citations
• Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences (2018) — 25 citations
• On the Role of Weight Sharing During Deep Option Learning (2019) — 22 citations
• Continual Learning In Environments With Polynomial Mixing Times (2021) — 17 citations
• Recursive Routing Networks: Learning to Compose Modules for Language Understanding (2019) — 29 citations