research
∙
03/16/2020
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
This paper studies the problem of post-hoc calibration of machine learni...
share
research
∙
03/16/2020
Anomalous Instance Detection in Deep Learning: A Survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial ...
research
∙
02/28/2020
Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
research
∙
02/25/2020
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
research
∙
12/16/2019
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
In the past few years, generative models like Generative Adversarial Net...
research
∙
12/05/2019
Merlin: Enabling Machine Learning-Ready HPC Ensembles
With the growing complexity of computational and experimental facilities...
research
∙
10/13/2019
Deep Probabilistic Kernels for Sample-Efficient Learning
Gaussian Processes (GPs) with an appropriate kernel are known to provide...
research
∙
07/26/2019
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics wh...
research
∙
07/06/2019
Generative Counterfactual Introspection for Explainable Deep Learning
In this work, we propose an introspection technique for deep neural netw...
research
∙
06/06/2019
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
This paper provides a general framework to study the effect of sampling ...
research
∙
01/05/2019
Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery
Material scientists are increasingly adopting the use of machine learnin...
research
∙
11/22/2018
MR-GAN: Manifold Regularized Generative Adversarial Networks
Despite the growing interest in generative adversarial networks (GANs), ...
research
∙
11/20/2018
MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense
Solving inverse problems continues to be a central challenge in computer...
research
∙
11/09/2018
Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses
We study the problem of finding a universal (image-agnostic) perturbatio...
research
∙
11/09/2018
Universal Hard-label Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses
We study the problem of finding a universal (image-agnostic) perturbatio...
research
∙
09/05/2018
Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization
A common challenge in machine learning and related fields is the need to...
research
∙
05/25/2018
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zeroth-order (gradient-free) optimization acc...
research
∙
05/18/2018
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
Solving inverse problems continues to be a challenge in a wide array of ...
research
∙
01/29/2018
Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges
The emerging paradigm of Human-Machine Inference Networks (HuMaINs) comb...
research
∙
12/16/2017
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms
This paper proposes a new approach to construct high quality space-filli...
research
∙
10/14/2017
Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines
In this paper, we consider the problem of federated (or decentralized) l...
research
∙
01/22/2016
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
This paper considers the problem of high dimensional signal detection in...
research
∙
04/14/2015
