Dave Braines
Artificial Intelligence · 2012–2020
A researcher in the Science corpus: 8 papers; trusts 53 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 140 citations into it; #26,341 of 35,345 by trust here, #10,207 by citations
- Safety, Robustness & Interpretability — 84 citations into it
- Computer Vision and Pattern Recognition — 35 citations into it
- Natural Language Processing — 18 citations into it
- Management Science and Operations Research — 11 citations into it
- Probabilistic & Bayesian Methods — 10 citations into it
- Evaluation & Benchmarks — 8 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Dave Braines trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zachary Chase Lipton
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Cynthia Rudin
- Yann LeCun
- Diederik P. Kingma
- Ilya Sutskever
- Kaiming He
- Alex Graves
Who trusts Dave Braines overall
Papers from Dave Braines's perspective
Ordered by how strongly the researchers Dave Braines 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)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- GENERATIVE ADVERSARIAL NETS
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Interpretability of deep learning models: A survey of results (2017) — 469 citations
- Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020) — 175 citations
- Explainable AI for Intelligence Augmentation in Multi-Domain Operations (2019) — 17 citations
- Stakeholders in Explainable AI (2018) — 183 citations
- Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (2018) — 191 citations
- Cognitive computing for coalition situational understanding (2017) — 20 citations
- Deep learning for situational understanding (2017) — 15 citations
- Integrating hard and soft information sources for D2D using controlled natural language (2012) — 47 citations