913 citations · 2.7k across the 51 of their papers we have counts for
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Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN). We do so…
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
Jiaming Zeng, Adam Lesnikowski, Jose M. Alvarez
One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural n…
ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks
Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
We introduce an approach to training a given compact network. To this end, we leverage over-parameterization, which typically improves both neural network optimization and generali…
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
Annotating the right data for training deep neural networks is an important challenge. Active learning using uncertainty estimates from Bayesian Neural Networks (BNNs) could provid…
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann +2
Training a deep network to perform semantic segmentation requires large amounts of labeled data. To alleviate the manual effort of annotating real images, researchers have investig…