Search Results for author:
Found 8 papers, 4 papers with code
An Efficient Algorithm for Clustered Multi-Task Compressive Sensing
This paper considers clustered multi-task compressive sensing, a hierarchical model that solves multiple compressive sensing tasks by finding clusters of tasks that leverage shared information to mutually improve signal reconstruction.
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Latent Gaussian models have a rich history in statistics and machine learning, with applications ranging from factor analysis to compressed sensing to time series analysis.
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
We introduce a method for computing scores for each word in the prompt; these scores represent its influence on biases in the model's output.
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning
State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures.
Covariance-Free Sparse Bayesian Learning
The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix.
Autoregressive Knowledge Distillation through Imitation Learning
The performance of autoregressive models on natural language generation tasks has dramatically improved due to the adoption of deep, self-attentive architectures.
Ranked #21 on
Machine Translation
on IWSLT2014 German-English
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups.
