research
∙
07/23/2023
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Tables are an abundant form of data with use cases across all scientific...
share
research
∙
06/07/2023
Interpretable Deep Clustering
Clustering is a fundamental learning task widely used as a first step in...
research
∙
06/01/2023
Anomaly Detection with Variance Stabilized Density Estimation
Density estimation based anomaly detection schemes typically model anoma...
research
∙
06/01/2023
Neuronal Cell Type Classification using Deep Learning
The brain is likely the most complex organ, given the variety of functio...
research
∙
03/16/2023
Multi-modal Differentiable Unsupervised Feature Selection
Multi-modal high throughput biological data presents a great scientific ...
research
∙
03/05/2023
Revisiting the Noise Model of Stochastic Gradient Descent
The stochastic gradient noise (SGN) is a significant factor in the succe...
research
∙
01/31/2023
Domain-Generalizable Multiple-Domain Clustering
Accurately clustering high-dimensional measurements is vital for adequat...
research
∙
01/01/2023
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
Classical methods for acoustic scene mapping require the estimation of t...
research
∙
10/28/2022
SG-VAD: Stochastic Gates Based Speech Activity Detection
We propose a novel voice activity detection (VAD) model in a low-resourc...
research
∙
04/19/2022
Imbalanced Classification via a Tabular Translation GAN
When presented with a binary classification problem where the data exhib...
research
∙
10/29/2021
Support Recovery with Stochastic Gates: Theory and Application for Linear Models
We analyze the problem of simultaneous support recovery and estimation o...
research
∙
10/11/2021
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Modern datasets often contain large subsets of correlated features and n...
research
∙
10/01/2021
Probabilistic Robust Autoencoders for Anomaly Detection
Empirical observations often consist of anomalies (or outliers) that con...
research
∙
06/11/2021
Locally Sparse Networks for Interpretable Predictions
Despite the enormous success of neural networks, they are still hard to ...
research
∙
03/19/2021
Refined Least Squares for Support Recovery
We study the problem of exact support recovery based on noisy observatio...
research
∙
10/12/2020
Deep Gated Canonical Correlation Analysis
Canonical Correlation Analysis (CCA) models can extract informative corr...
research
∙
09/09/2020
Kernel-based parameter estimation of dynamical systems with unknown observation functions
A low-dimensional dynamical system is observed in an experiment as a hig...
research
∙
07/09/2020
Let the Data Choose its Features: Differentiable Unsupervised Feature Selection
Scientific observations often consist of a large number of variables (fe...
research
∙
04/15/2020
LOCA: LOcal Conformal Autoencoder for standardized data coordinates
We propose a deep-learning based method for obtaining standardized data ...
research
∙
03/16/2020
Randomly Aggregated Least Squares for Support Recovery
We study the problem of exact support recovery: given an (unknown) vecto...
research
∙
02/27/2020
The Spectral Underpinning of word2vec
word2vec due to Mikolov et al. (2013) is a word embedding method that is...
research
∙
10/09/2018
Deep supervised feature selection using Stochastic Gates
In this study, we propose a novel non-parametric embedded feature select...
research
∙
02/14/2018
Geometry-Based Data Generation
Many generative models attempt to replicate the density of their input d...
research
∙
06/28/2016
Multi-View Kernel Consensus For Data Analysis and Signal Processing
The input data features set for many data driven tasks is high-dimension...
research
∙
08/23/2015