Michael Matena
Artificial Intelligence · 2019–2024
A researcher in the Science corpus: 4 papers; trusts 385 researchers and is trusted by 1,068 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 214 citations into it; #748 of 35,345 by trust here, #325 by citations
- Natural Language Processing — 132 citations into it; #398 of 5,688 by trust here, #93 by citations
- Computer Vision and Pattern Recognition — 110 citations into it; #1,468 of 21,575 by trust here, #262 by citations
- Evaluation & Benchmarks — 49 citations into it
- Large Language Models — 44 citations into it; #66 of 799 by trust here, #58 by citations
- Model Architectures — 44 citations into it; #1,252 of 2,278 by trust here, #1,246 by citations
- Optimization & Training — 37 citations into it; #1,524 of 2,601 by trust here, #1,986 by citations
- Representation & Self-Supervised Learning — 33 citations into it
Whom Michael Matena trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- James Martens
- Alex Krizhevsky
- Diederik P. Kingma
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Richard Socher
- Jimmy Ba
Who trusts Michael Matena overall
- Mitchell Wortsman
- Gabriel Ilharco
- Yi Tay
- Prateek Yadav
- Timo Schick
- Mohit Bansal
- Le Yu
- Margaret Li
- Suchin Gururangan
- Mostafa Dehghani
Papers from Michael Matena's perspective
Ordered by how strongly the researchers Michael Matena trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019) — 26,606 citations
- Do Transformer Modifications Transfer Across Implementations and Applications? (2021) — 138 citations
- Merging Models with Fisher-Weighted Averaging (2021) — 671 citations
- When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024) — 153 citations