Marten van Schijndel
Artificial Intelligence · 2016–2021
A researcher in the Science corpus: 3 papers; trusts 66 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 62 citations into it; #32,473 of 35,345 by trust here, #21,316 by citations
- Natural Language Processing — 38 citations into it; #5,224 of 5,688 by trust here, #4,601 by citations
- Cognitive Neuroscience — 14 citations into it
- Language and Linguistics — 12 citations into it
- Large Language Models — 12 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
- Evaluation & Benchmarks — 9 citations into it
- Representation & Self-Supervised Learning — 8 citations into it
Whom Marten van Schijndel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Roger Levy
- Ofir Press
- Richard Socher
- Ilya Sutskever
- Thorsten Joachims
- Diederik P. Kingma
- Tomas Mikolov
- Kawin Ethayarajh
Who trusts Marten van Schijndel overall
Papers from Marten van Schijndel's perspective
Ordered by how strongly the researchers Marten van Schijndel trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Quantity doesn’t buy quality syntax with neural language models (2019) — 76 citations
- All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality (2021) — 176 citations
- Investigating locality effects and surprisal in written English syntactic choice phenomena. (2016) — 40 citations