Dallas Card
Artificial Intelligence · 2015–2022
A researcher in the Science corpus: 9 papers; trusts 182 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 230 citations into it; #5,989 of 35,345 by trust here, #1,416 by citations
- Natural Language Processing — 119 citations into it; #2,977 of 5,688 by trust here, #1,262 by citations
- Computer Vision and Pattern Recognition — 83 citations into it
- Safety, Robustness & Interpretability — 75 citations into it; #855 of 2,251 by trust here, #109 by citations
- Evaluation & Benchmarks — 61 citations into it; #1,598 of 3,147 by trust here, #1,171 by citations
- Representation & Self-Supervised Learning — 31 citations into it
- Statistics and Probability — 26 citations into it; #12,593 of 12,932 by trust here, #4,964 by citations
- Optimization & Training — 21 citations into it
Whom Dallas Card trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Klaus Krippendorff
- Diederik P. Kingma
- Yoshua Bengio
- Yann LeCun
- Ilya Sutskever
- Kaiming He
- Richard Socher
- Jimmy Ba
Who trusts Dallas Card overall
- Xiaochuang Han
- Samuel R. Bowman
- Yulia Tsvetkov
- Mark T. Keane
- Bertie Vidgen
- Maarten Sap
- David M. Howcroft
- Elizabeth Clark
- Dan Hendrycks
- Jack Hessel
Papers from Dallas Card's perspective
Ordered by how strongly the researchers Dallas Card 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
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- The Risk of Racial Bias in Hate Speech Detection (2019) — 1,043 citations
- Show Your Work: Improved Reporting of Experimental Results (2019) — 310 citations
- With Little Power Comes Great Responsibility (2020) — 157 citations
- The Values Encoded in Machine Learning Research (2021) — 401 citations
- Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (2022) — 100 citations
- The Importance of Calibration for Estimating Proportions from Annotations (2018) — 46 citations
- The Media Frames Corpus: Annotations of Frames Across Issues (2015) — 286 citations
- Deep Weighted Averaging Classifiers (2018) — 42 citations