Mahdi Pakdaman Naeini

Artificial Intelligence · 2010–2015

A researcher in the Science corpus: 4 papers; trusts 26 researchers and is trusted by 48 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it; #5,473 of 35,345 by trust here, #9,275 by citations
• Evaluation & Benchmarks — 9 citations into it
• Statistics and Probability — 6 citations into it
• Management Science and Operations Research — 5 citations into it; #4,569 of 11,946 by trust here, #7,638 by citations
• Probabilistic & Bayesian Methods — 5 citations into it; #490 of 1,525 by trust here, #485 by citations

Whom Mahdi Pakdaman Naeini trusts overall

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

1. Bianca Zadrozny
2. J. Platt
3. Charles Peter Elkan
4. Alexandru Niculescu-Mizil
5. Chih-Jen Lin
6. David E. Heckerman
7. Paul J. Werbos
8. Bernard Walter Silverman
9. R. Caruana
10. Jaka Demsar

Who trusts Mahdi Pakdaman Naeini overall

1. Jishnu Mukhoti
2. Will Serrano
3. Florian Wenzel
4. Ananya Kumar
5. Berk Ustun
6. A.J.P. Samarawickrama
7. Sreekumar Pulakkazhy
8. William Fedus
9. Fredrik K. Gustafsson
10. Dustin Tran

Papers from Mahdi Pakdaman Naeini's perspective

Ordered by how strongly the researchers Mahdi Pakdaman Naeini trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
3. ImageNet classification with deep convolutional neural networks
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. LIBLINEAR: A Library for Large Linear Classification
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. LIBSVM: A library for support vector machines

Their papers

• Obtaining Well Calibrated Probabilities Using Bayesian Binning (2015) — 1,951 citations
• Binary Classifier Calibration: Non-parametric approach (2014) — 18 citations
• Stock market value prediction using neural networks (2010) — 162 citations
• Binary Classifier Calibration: Bayesian Non-Parametric Approach (2014)