1.7k citations · 3.2k across the 31 of their papers we have counts for
4 papers · 1 filter
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…
Memory-Efficient Implementation of DenseNets
Geoff Pleiss, Danlu Chen, Gao Huang +3
The DenseNet architecture is highly computationally efficient as a result of feature reuse. However, a naive DenseNet implementation can require a significant amount of GPU memory:…
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…