2.4k citations · 12.3k across the 115 of their papers we have counts for
4 papers · 2 filters
On Fairness and Calibration
Geoff Pleiss, Manish Raghavan, Felix Wu +2
The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models. This has motivated a growing line of work on w…
On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun +1
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applic…
Snapshot Ensembles: Train 1, get M for free
Gao Huang, Yixuan Li, Geoff Pleiss +3
Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally…
Multi-Scale Dense Networks for Resource Efficient Image Classification
Gao Huang, Danlu Chen, Tianhong Li +3
In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction…