2.4k citations · 12.3k across the 115 of their papers we have counts for
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Fast Reading Comprehension with ConvNets
Felix Wu, Ni Lao, John Blitzer +2
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets. Their sequential nature is a natural fit for language, but it also precludes parallelizat…
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
Gao Huang, Shichen Liu, Laurens van der Maaten +1
Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unp…
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…
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…