Search Results for author:
Found 24 papers, 4 papers with code
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks.
Improving model calibration with accuracy versus uncertainty optimization
Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications.
Adaptive Convolutional Neural Networks
The quest for increased visual recognition performance has led to the development of highly complex neural networks with very deep topologies.
Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN Models
Optimizing the pipeline for both compression and task-performance requires high-quality estimates of the information-theoretic rate of the intermediate features.
A Greedy Part Assignment Algorithm for Real-time Multi-person 2D Pose Estimation
We propose a greedy part assignment algorithm that exploits the inherent structure of the human body to achieve a lower complexity, compared to any of the prior published works.
BAR: Bayesian Activity Recognition using variational inference
We show that the Bayesian inference applied to DNNs provide reliable confidence measures for visual activity recognition task as compared to conventional DNNs.
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference
In the multimodal setting, the proposed framework improved precision-recall AUC by 10. 2% on the subset of MiT dataset as compared to non-Bayesian baseline.
Real-time Approximate Bayesian Computation for Scene Understanding
The underlying generative models are built from realistic simulation software, wrapped in a Bayesian error model for the gap between simulation outputs and real data.
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
We present a principled approach for detecting out-of-distribution (OOD) and adversarial samples in deep neural networks.
Deep Probabilistic Models to Detect Data Poisoning Attacks
In the second approach, we use Bayesian deep neural networks trained with mean-field variational inference to estimate model uncertainty associated with the predictions.
Tree pyramidal adaptive importance sampling
After each new sample operation, a set of tree leaves are subdivided for improving the approximation of the proposal distribution to the target density.
B-SCST: Bayesian Self-Critical Sequence Training for Image Captioning
The "baseline" for the policy-gradients in B-SCST is generated by averaging predictive quality metrics (CIDEr-D) of the captions drawn from the distribution obtained using a Bayesian DNN model.
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders.
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features
We also show that the feature reconstruction error, which is the $L_2$-norm of the difference between the original feature and the pre-image of its embedding, is highly effective for OOD detection and in some cases superior to the log-likelihood scores.
Energy-Based Anomaly Detection and Localization
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem.
Data augmentation to improve robustness of image captioning solutions
In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution, and notice a degradation in solution performance as blur intensity increases.
Partially-Supervised Novel Object Captioning Leveraging Context from Paired Data
PS-NOC is agnostic to model architecture, and primarily focuses on the training approach that uses existing fully paired image-caption data and the images with only the novel object detection labels (partially paired data).
Mitigating Sampling Bias and Improving Robustness in Active Learning
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Robust Contrastive Active Learning with Feature-guided Query Strategies
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations.
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection
This paper presents a fast, principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN).
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 DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-server (or cloud).
FRE: A Fast Method For Anomaly Detection And Segmentation
This paper presents a fast and principled approach for solving the visual anomaly detection and segmentation problem.
Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization
Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making.
Rate-Distortion Theory in Coding for Machines and its Application
To meet this growing demand, several methods have been developed for image and video coding for machines.
