research
∙
07/07/2023
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness
The robustness of deep neural networks (DNNs) against adversarial attack...
share
research
∙
05/11/2023
Promise and Limitations of Supervised Optimal Transport-Based Graph Summarization via Information Theoretic Measures
Graph summarization is the problem of producing smaller graph representa...
research
∙
10/28/2022
Improving Hyperspectral Adversarial Robustness using Ensemble Networks in the Presences of Multiple Attacks
Semantic segmentation of hyperspectral images (HSI) has seen great strid...
research
∙
04/26/2022
Theoretical Understanding of the Information Flow on Continual Learning Performance
Continual learning (CL) is a setting in which an agent has to learn from...
research
∙
04/14/2022
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability
Raw deep neural network (DNN) performance is not enough; in real-world s...
research
∙
03/22/2022
The Stanford Drone Dataset is More Complex than We Think: An Analysis of Key Characteristics
Several datasets exist which contain annotated information of individual...
research
∙
03/09/2022
CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering
Most semantic segmentation approaches of Hyperspectral images (HSIs) use...
research
∙
06/22/2020
Slimming Neural Networks using Adaptive Connectivity Scores
There are two broad approaches to deep neural network (DNN) pruning: 1) ...
research
∙
03/18/2020
MINT: Deep Network Compression via Mutual Information-based Neuron Trimming
Most approaches to deep neural network compression via pruning either ev...
research
∙
10/02/2019
Geometric Online Adaptation: Graph-Based OSFS for Streaming Samples
Feature selection seeks a curated subset of available features such that...
research
∙
05/21/2019
Geometric Estimation of Multivariate Dependency
This paper proposes a geometric estimator of dependency between a pair o...
research
∙
02/10/2019
Feature Selection for multi-labeled variables via Dependency Maximization
Feature selection and reducing the dimensionality of data is an essentia...
research
∙
11/15/2018
Learning to Bound the Multi-class Bayes Error
In the context of supervised learning, meta learning uses features, meta...
research
∙
10/01/2018
Convergence Rates for Empirical Estimation of Binary Classification Bounds
Bounding the best achievable error probability for binary classification...
research
∙
02/13/2018
A Dimension-Independent discriminant between distributions
Henze-Penrose divergence is a non-parametric divergence measure that can...
research
∙
02/17/2017
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
We propose a direct estimation method for Rényi and f-divergence measure...
research
∙
09/13/2016
