William Fithian
Statistics and Probability · 2013–2016
A researcher in the Science corpus: 7 papers; trusts 38 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 52 citations into it; #2,004 of 12,932 by trust here, #1,806 by citations
- Artificial Intelligence — 37 citations into it; #6,543 of 35,345 by trust here, #11,490 by citations
- Optimization & Training — 15 citations into it
- Statistical Learning Theory — 12 citations into it
- Applied Mathematics — 10 citations into it; #6,546 of 7,620 by trust here, #2,807 by citations
- Computer Vision and Pattern Recognition — 10 citations into it
- Computational Mechanics — 8 citations into it
- Control and Systems Engineering — 6 citations into it; #992 of 8,257 by trust here, #1,875 by citations
Whom William Fithian trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jonathan E. Taylor
- Robert Tibshirani
- Nicolai Meinshausen
- Jason D. Lee
- Yoav Benjamini
- Dennis L. Sun
- Bradley Efron
- Leo Breiman
- Trevor J. Hastie
- Hirotugu Akaike
Who trusts William Fithian overall
- Robert Tibshirani
- Stefan Wager
- Jonathan E. Taylor
- Xiaoying Tian
- M. G'Sell
- Sangwon Hyun
- Susan Athey
- Kenneth Hung
- Daniel Garcia Rasines
- Tijana Zrnic
Papers from William Fithian's perspective
Ordered by how strongly the researchers William Fithian 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Altitude Training: Strong Bounds for Single-Layer Dropout (2014) — 50 citations
- Data Augmentation via Levy Processes (2016) — 5 citations
- Optimal Inference After Model Selection (2014) — 371 citations
- Selective Sequential Model Selection (2015) — 66 citations
- Point process models for presence‐only analysis (2015) — 409 citations
- Rank verification for exponential families (2016) — 34 citations
- Effective degrees of freedom: a flawed metaphor (2013) — 67 citations