Kirk Roberts
Artificial Intelligence · 2011–2020
A researcher in the Science corpus: 8 papers; trusts 115 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 94 citations into it; #23,306 of 35,345 by trust here, #12,244 by citations
- Natural Language Processing — 47 citations into it
- Computer Vision and Pattern Recognition — 35 citations into it; #20,159 of 21,575 by trust here, #12,419 by citations
- Molecular Biology — 25 citations into it; #16,665 of 17,773 by trust here, #9,270 by citations
- Evaluation & Benchmarks — 18 citations into it
- Model Architectures — 16 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Multimodal Learning — 12 citations into it
Whom Kirk Roberts trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Alex Graves
- Karen Simonyan
- Diederik P. Kingma
- Yann LeCun
- Sepp Hochreiter
- Christian Szegedy
Who trusts Kirk Roberts overall
- Yuan Ling
- Travis R. Goodwin
- Chao Yu
- B. van Ginneken
- Sean MacAvaney
- Sadid A. Hasan
- Nick Craswell
- Sanda M. Harabagiu
- Angela Zhang
- Yifan Peng
Papers from Kirk Roberts's perspective
Ordered by how strongly the researchers Kirk Roberts trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation (2016) — 394 citations
- Deep learning in clinical natural language processing: a methodical review (2019) — 437 citations
- TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19 (2020) — 130 citations
- TREC-COVID (2020) — 94 citations
- Enhancing Clinical Concept Extraction with Contextual Embedding (2019) — 329 citations
- Automatic extraction of relations between medical concepts in clinical texts (2011) — 192 citations
- Overview of the TREC 2020 Precision Medicine Track (2017) — 49 citations
- Overview of the TREC 2014 Clinical Decision Support Track (2014) — 193 citations