research
∙
05/26/2023
Rate-Distortion Theory in Coding for Machines and its Application
Recent years have seen a tremendous growth in both the capability and po...
share
research
∙
12/09/2022
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Reliable uncertainty quantification in deep neural networks is very cruc...
research
∙
11/23/2022
FRE: A Fast Method For Anomaly Detection And Segmentation
This paper presents a fast and principled approach for solving the visua...
research
∙
08/24/2022
A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing
Split computing has emerged as a recent paradigm for implementation of D...
research
∙
03/20/2022
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection
This paper presents a fast, principled approach for detecting anomalous ...
research
∙
09/13/2021
Improving Robustness and Efficiency in Active Learning with Contrastive Loss
This paper introduces supervised contrastive active learning (SCAL) by l...
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
∙
05/07/2021
Energy-Based Anomaly Detection and Localization
This brief sketches initial progress towards a unified energy-based solu...
research
∙
12/14/2020
Improving model calibration with accuracy versus uncertainty optimization
Obtaining reliable and accurate quantification of uncertainty estimates ...
research
∙
12/08/2020
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features
This paper presents a principled approach for detecting out-of-distribut...
research
∙
11/15/2020
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Transparency of algorithmic systems entails exposing system properties t...
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
∙
12/18/2019
Tree pyramidal adaptive importance sampling
This paper introduces Tree-Pyramidal Adaptive Importance Sampling (TP-AI...
research
∙
12/03/2019
Deep Probabilistic Models to Detect Data Poisoning Attacks
Data poisoning attacks compromise the integrity of machine-learning mode...
research
∙
09/25/2019
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
We present a principled approach for detecting out-of-distribution (OOD)...
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