Peter Alexander Jansen
Artificial Intelligence · 2012–2022
A researcher in the Science corpus: 9 papers; trusts 345 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 114 citations into it; #7,081 of 35,345 by trust here, #7,172 by citations
- Natural Language Processing — 96 citations into it; #1,161 of 5,688 by trust here, #1,366 by citations
- Computer Vision and Pattern Recognition — 40 citations into it; #7,678 of 21,575 by trust here, #6,542 by citations
- Evaluation & Benchmarks — 27 citations into it; #728 of 3,147 by trust here, #1,138 by citations
- Reinforcement Learning — 15 citations into it
- Large Language Models — 13 citations into it; #768 of 799 by trust here, #770 by citations
- Prompting & Reasoning — 10 citations into it
- Developmental and Educational Psychology — 8 citations into it
Whom Peter Alexander Jansen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Teuvo Kohonen
- Jeffrey L. Elman
- Peter Clark
- Geoffrey E. Hinton
- David E. Rumelhart
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Richard Socher
Who trusts Peter Alexander Jansen overall
- Tushar Khot
- Marco Valentino
- Peter Clark
- Todor Mihaylov
- Zhengnan Xie
- Sarah Wiegreffe
- Mokanarangan Thayaparan
- Chenhao Tan
- Ashish Sabharwal
- Ruben Cartuyvels
Papers from Peter Alexander Jansen's perspective
Ordered by how strongly the researchers Peter Alexander Jansen 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
- 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
- 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)
Their papers
- Visually-Grounded Planning without Vision: Language Models Infer Detailed Plans from High-level Instructions (2020) — 62 citations
- QASC: A Dataset for Question Answering via Sentence Composition (2019) — 401 citations
- WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference (2018) — 135 citations
- Explaining Answers with Entailment Trees (2021) — 232 citations
- Behavior Cloned Transformers are Neurosymbolic Reasoners (2022) — 14 citations
- Controlling Information Aggregation for Complex Question Answering (2018) — 13 citations
- Strong systematicity through sensorimotor conceptual grounding: an unsupervised, developmental approach to connectionist sentence processing (2012) — 33 citations
- WorldTree V2: A Corpus of Science-Domain Structured Explanations and Inference Patterns supporting Multi-Hop Inference (2020) — 90 citations
- What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams (2016) — 67 citations