Mikel Menta

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 353 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 134 citations into it; #25,780 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 134 citations into it; #15,808 of 21,575 by trust here, #6,206 by citations
• Meta & Continual Learning — 83 citations into it; #807 of 1,041 by trust here, #236 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Optimization & Training — 16 citations into it
• Reinforcement Learning — 14 citations into it
• Media Technology — 7 citations into it
• Cognitive Neuroscience — 5 citations into it

Whom Mikel Menta trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Ross B. Girshick
6. Diederik P. Kingma
7. Yann LeCun
8. Karen Simonyan
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Mikel Menta overall

1. Liyuan Wang
2. Da-Wei Zhou

Papers from Mikel Menta's perspective

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

Their papers

• Class-Incremental Learning: Survey and Performance Evaluation on Image Classification (2020) — 891 citations
• Generative Feature Replay For Class-Incremental Learning (2020) — 190 citations