C. Piech
Artificial Intelligence · 2013–2021
A researcher in the Science corpus: 9 papers; trusts 32 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 60 citations into it; #12,641 of 35,345 by trust here, #2,028 by citations
- Probabilistic & Bayesian Methods — 33 citations into it
- Computer Vision and Pattern Recognition — 18 citations into it
- Generative Models — 17 citations into it
- Statistics and Probability — 15 citations into it
- Computer Science Applications — 10 citations into it; #523 of 1,855 by trust here, #145 by citations
- Management Science and Operations Research — 9 citations into it
- Safety Research — 6 citations into it; #2,981 of 5,829 by trust here, #4,191 by citations
Whom C. Piech trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Le Song
- Samuel R. Bowman
- Geoffrey E. Hinton
- Finn Årup Nielsen
- Alex Graves
- D. Rohrer
- Diederik P. Kingma
- Richard Socher
- Ronald J. Williams
Who trusts C. Piech overall
- René F. Kizilcec
- Rahul Gupta
- Sahil Bhatia
- Aritra Ghosh
- Chun-Kit Yeung
- Rishabh Singh
- Dana Pessach
- Hiromi Nakagawa
- Ghodai M. Abdelrahman
- Aditya Kanade
Papers from C. Piech's perspective
Ordered by how strongly the researchers C. Piech 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Deep Knowledge Tracing (2015) — 1,613 citations
- Learning Program Embeddings to Propagate Feedback on Student Code (2015) — 201 citations
- Tuned Models of Peer Assessment in MOOCs (2013) — 416 citations
- Variational Item Response Theory: Fast, Accurate, and Expressive (2020) — 62 citations
- Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction (2018) — 197 citations
- Modeling Item Response Theory with Stochastic Variational Inference (2021) — 5 citations
- Deep Knowledge Tracing On Programming Exercises (2017) — 107 citations
- Deconstructing disengagement: analyzing learner subpopulations in massive open online courses (2013) — 1,173 citations