research
∙
09/13/2021
Improving Robustness and Efficiency in Active Learning with Contrastive Loss
This paper introduces supervised contrastive active learning (SCAL) by l...
share
research
∙
09/13/2021
Mitigating Sampling Bias and Improving Robustness in Active Learning
This paper presents simple and efficient methods to mitigate sampling bi...
research
∙
09/10/2021
Partially-supervised novel object captioning leveraging context from paired data
In this paper, we propose an approach to improve image captioning soluti...
research
∙
06/10/2021
Data augmentation to improve robustness of image captioning solutions
In this paper, we study the impact of motion blur, a common quality flaw...
research
∙
04/06/2020
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning
Bayesian deep neural networks (DNN) provide a mathematically grounded fr...
research
∙
12/03/2019
Deep Probabilistic Models to Detect Data Poisoning Attacks
Data poisoning attacks compromise the integrity of machine-learning mode...
research
∙
06/12/2019
MOPED: Efficient priors for scalable variational inference in Bayesian deep neural networks
Variational inference for Bayesian deep neural networks (DNNs) requires ...
research
∙
11/27/2018
Uncertainty aware multimodal activity recognition with Bayesian inference
Deep neural networks (DNNs) provide state-of-the-art results for a multi...
research
∙
11/08/2018
