Daniel Harborne
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 86 citations into it; #27,389 of 35,345 by trust here, #10,942 by citations
- Safety, Robustness & Interpretability — 67 citations into it
- Computer Vision and Pattern Recognition — 37 citations into it
- Natural Language Processing — 14 citations into it
Whom Daniel Harborne trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zachary Chase Lipton
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Scott M. Lundberg
- Yann LeCun
- Diederik P. Kingma
- Ilya Sutskever
- Thomas G. Dietterich
- Kaiming He
Who trusts Daniel Harborne overall
Papers from Daniel Harborne's perspective
Ordered by how strongly the researchers Daniel Harborne 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Interpretability of deep learning models: A survey of results (2017) — 469 citations
- Sanity Checks for Saliency Metrics (2019) — 210 citations
- Stakeholders in Explainable AI (2018) — 183 citations
- Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems (2018) — 191 citations