research
∙
05/10/2023
Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming
Creating a digital world that closely mimics the real world with its man...
share
research
∙
04/30/2023
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning
Rehearsal-based approaches are a mainstay of continual learning (CL). Th...
research
∙
04/16/2023
Explanations of Black-Box Models based on Directional Feature Interactions
As machine learning algorithms are deployed ubiquitously to a variety of...
research
∙
02/09/2023
Geometry of Score Based Generative Models
In this work, we look at Score-based generative models (also called diff...
research
∙
02/05/2023
Divide and Compose with Score Based Generative Models
While score based generative models, or diffusion models, have found suc...
research
∙
02/02/2023
High-precision regressors for particle physics
Monte Carlo simulations of physics processes at particle colliders like ...
research
∙
10/09/2022
Pruning Adversarially Robust Neural Networks without Adversarial Examples
Adversarial pruning compresses models while preserving robustness. Curre...
research
∙
10/05/2022
Explanation Uncertainty with Decision Boundary Awareness
Post-hoc explanation methods have become increasingly depended upon for ...
research
∙
06/24/2022
Analyzing the Effects of Classifier Lipschitzness on Explainers
Machine learning methods are getting increasingly better at making predi...
research
∙
02/01/2022
Deep Layer-wise Networks Have Closed-Form Weights
There is currently a debate within the neuroscience community over the l...
research
∙
01/12/2022
Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network
Beam selection for millimeter-wave links in a vehicular scenario is a ch...
research
∙
11/08/2021
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
There are limited works showing the efficacy of unsupervised Out-of-Dist...
research
∙
09/29/2021
Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space
Estimating Kullback Leibler (KL) divergence from samples of two distribu...
research
∙
06/13/2021
Deep Bayesian Unsupervised Lifelong Learning
Lifelong Learning (LL) refers to the ability to continually learn and so...
research
∙
06/04/2021
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness
We investigate the HSIC (Hilbert-Schmidt independence criterion) bottlen...
research
∙
05/04/2021
On the Sample Complexity of Rank Regression from Pairwise Comparisons
We consider a rank regression setting, in which a dataset of N samples w...
research
∙
02/15/2021
Machine Learning on Camera Images for Fast mmWave Beamforming
Perfect alignment in chosen beam sectors at both transmit- and receive-n...
research
∙
12/13/2020
Open-World Class Discovery with Kernel Networks
We study an Open-World Class Discovery problem in which, given labeled t...
research
∙
12/13/2020
Learn-Prune-Share for Lifelong Learning
In lifelong learning, we wish to maintain and update a model (e.g., a ne...
research
∙
11/04/2020
Kernel Dependence Network
We propose a greedy strategy to spectrally train a deep network for mult...
research
∙
09/23/2020
Using Undersampling with Ensemble Learning to Identify Factors Contributing to Preterm Birth
In this paper, we propose Ensemble Learning models to identify factors c...
research
∙
06/15/2020
Layer-wise Learning of Kernel Dependence Networks
We propose a greedy strategy to train a deep network for multi-class cla...
research
∙
03/22/2020
Deep Markov Spatio-Temporal Factorization
We introduce deep Markov spatio-temporal factorization (DMSTF), a deep g...
research
∙
02/23/2020
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling
Many contemporary machine learning models require extensive tuning of hy...
research
∙
01/03/2020
Segmentation of Cellular Patterns in Confocal Images of Melanocytic Lesions in vivo via a Multiscale Encoder-Decoder Network (MED-Net)
In-vivo optical microscopy is advancing into routine clinical practice f...
research
∙
09/06/2019
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence...
research
∙
09/06/2019
Solving Interpretable Kernel Dimension Reduction
Kernel dimensionality reduction (KDR) algorithms find a low dimensional ...
research
∙
08/09/2019
Deep Kernel Learning for Clustering
We propose a deep learning approach for discovering kernels tailored to ...
research
∙
06/21/2019
Neural Topographic Factor Analysis for fMRI Data
Neuroimaging experiments produce a large volume (gigabytes) of high-dime...
research
∙
01/18/2019
Accelerated Experimental Design for Pairwise Comparisons
Pairwise comparison labels are more informative and less variable than c...
research
∙
11/06/2018
Deep feature transfer between localization and segmentation tasks
In this paper, we propose a new pre-training scheme for U-net based imag...
research
∙
02/13/2018
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning
Deep Learning (DL) methods have been transforming computer vision with i...
research
∙
03/15/2012