28 citations · 136 across the 33 of their papers we have counts for
5 papers · 1 filter
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…
Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels
Kashyap Chitta, Jianwei Feng, Martial Hebert
Training deep networks for semantic segmentation requires annotation of large amounts of data, which can be time-consuming and expensive. Unfortunately, these trained networks stil…
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…
Targeted Kernel Networks: Faster Convolutions with Attentive Regularization
Kashyap Chitta
We propose Attentive Regularization (AR), a method to constrain the activation maps of kernels in Convolutional Neural Networks (CNNs) to specific regions of interest (ROIs). Each…
Learning Sampling Policies for Domain Adaptation
Yash Patel, Kashyap Chitta, Bhavan Jasani
We address the problem of semi-supervised domain adaptation of classification algorithms through deep Q-learning. The core idea is to consider the predictions of a source domain ne…