Rik Koncel-Kedziorski
Natural Language Processing · 2014–2021
A researcher in the Science corpus: 8 papers; trusts 187 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 156 citations into it; #1,084 of 5,688 by trust here, #773 by citations
- Artificial Intelligence — 152 citations into it; #4,239 of 35,345 by trust here, #2,989 by citations
- Computer Vision and Pattern Recognition — 45 citations into it; #17,694 of 21,575 by trust here, #15,955 by citations
- Evaluation & Benchmarks — 27 citations into it
- Model Architectures — 13 citations into it
- Reinforcement Learning — 10 citations into it
- Computational Theory and Mathematics — 9 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
Whom Rik Koncel-Kedziorski trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Geoffrey E. Hinton
- Alex Graves
- Yoshua Bengio
- Sepp Hochreiter
- Diederik P. Kingma
- Kaiming He
- Christopher D. Manning
- Alex Krizhevsky
- Hannaneh Hajishirzi
Who trusts Rik Koncel-Kedziorski overall
- Lei Wang
- Jipeng Zhang
- Dongxiang Zhang
- Zhenwen Liang
- Jierui Li
- Namgyu Ho
- Subhro Roy
- Felix Stahlberg
- Yining Hong
- Bugeun Kim
Papers from Rik Koncel-Kedziorski's perspective
Ordered by how strongly the researchers Rik Koncel-Kedziorski 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
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms (2019) — 894 citations
- MAWPS: A Math Word Problem Repository (2016) — 523 citations
- Text Generation from Knowledge Graphs with Graph Transformers (2019) — 363 citations
- Parsing Algebraic Word Problems into Equations (2015) — 366 citations
- Multi-Resolution Language Grounding with Weak Supervision (2014) — 13 citations
- A Controllable Model of Grounded Response Generation (2020) — 89 citations
- Scarecrow: A Framework for Scrutinizing Machine Text (2021) — 36 citations
- Data-Driven Methods for Solving Algebra Word Problems (2018) — 27 citations