Grzegorz Chrupała
Artificial Intelligence · 2014–2020
A researcher in the Science corpus: 9 papers; trusts 874 researchers and is trusted by 83 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 116 citations into it; #2,379 of 35,345 by trust here, #5,970 by citations
- Computer Vision and Pattern Recognition — 82 citations into it; #3,306 of 21,575 by trust here, #5,082 by citations
- Natural Language Processing — 69 citations into it; #863 of 5,688 by trust here, #1,674 by citations
- Model Architectures — 36 citations into it
- Multimodal Learning — 34 citations into it; #124 of 1,211 by trust here, #605 by citations
- Representation & Self-Supervised Learning — 28 citations into it; #577 of 3,145 by trust here, #1,543 by citations
- Signal Processing — 20 citations into it; #920 of 7,572 by trust here, #1,538 by citations
- Speech & Audio — 20 citations into it; #263 of 1,076 by trust here, #452 by citations
Whom Grzegorz Chrupała trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David F. Harwath
- Geoffrey E. Hinton
- Alex Graves
- Yoshua Bengio
- Ilya Sutskever
- Karen Simonyan
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
Who trusts Grzegorz Chrupała overall
- Yonatan Belinkov
- Herman Kamper
- Afra Alishahi
- David F. Harwath
- Fahim Dalvi
- William N. Havard
- James R. Glass
- Ankita Pasad
- Hassan Sajjad
- John Wu
Papers from Grzegorz Chrupała's perspective
Ordered by how strongly the researchers Grzegorz Chrupała 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Representation of Linguistic Form and Function in Recurrent Neural Networks (2016) — 164 citations
- Representations of language in a model of visually grounded speech signal (2017) — 135 citations
- Normalizing tweets with edit scripts and recurrent neural embeddings (2014) — 85 citations
- Correlating Neural and Symbolic Representations of Language (2019) — 75 citations
- From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning (2016) — 31 citations
- Proceedings of the 2018 EMNLP Workshop BlackboxNLP : Analyzing and Interpreting Neural Networks for NLP (2018) — 98 citations
- Symbolic Inductive Bias for Visually Grounded Learning of Spoken Language (2018) — 28 citations
- Encoding of phonology in a recurrent neural model of grounded speech (2017) — 60 citations
- Analyzing analytical methods: The case of phonology in neural models of spoken language (2020) — 27 citations