Daniel B. Neill

Artificial Intelligence · 2008–2018

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 27 citations into it; #23,166 of 35,345 by trust here, #19,439 by citations
• Statistics and Probability — 18 citations into it
• Sociology and Political Science — 9 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Causal Inference & Discovery — 7 citations into it
• Safety, Robustness & Interpretability — 7 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Daniel B. Neill trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Arthur Gretton
2. Bernhard Schölkopf
3. Christopher K. I. Williams
4. Alex Smola
5. Tom L. Burr
6. Carl E. Rasmussen
7. Alexandra Chouldechova
8. Kenji Fukumizu
9. Jan Peters
10. Michael R. Kosorok

Who trusts Daniel B. Neill overall

1. Charu C. Aggarwal
2. Krikamol Muandet

Papers from Daniel B. Neill's perspective

Ordered by how strongly the researchers Daniel B. Neill trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Learning Multiple Layers of Features from Tiny Images
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. GENERATIVE ADVERSARIAL NETS
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference (2015) — 44 citations
• Anomaly pattern detection in categorical datasets (2008) — 129 citations
• Identifying Significant Predictive Bias in Classifiers (2016) — 68 citations
• Machine Learning for the Developing World (2018) — 75 citations