1 code implementation • 30 Sep 2023 • Alexander Lin, Demba Ba
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.
no code implementations • 5 Jun 2023 • Alexander Lin, Bahareh Tolooshams, Yves Atchadé, Demba Ba
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.
no code implementations • 3 Jun 2023 • Alexander Lin, Lucas Monteiro Paes, Sree Harsha Tanneru, Suraj Srinivas, Himabindu Lakkaraju
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.
1 code implementation • 25 Feb 2022 • Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
1 code implementation • 10 Oct 2021 • Alexander Lin, Andrew H. Song, Demba Ba
State-of-the-art approaches for clustering high-dimensional data utilize deep auto-encoder architectures.
no code implementations • 21 May 2021 • Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba Ba
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.
2 code implementations • EMNLP 2020 • Alexander Lin, Jeremy Wohlwend, Howard Chen, Tao Lei
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
no code implementations • 23 Oct 2018 • Alexander Lin, Yingzhuo Zhang, Jeremy Heng, Stephen A. Allsop, Kay M. Tye, Pierre E. Jacob, Demba Ba
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups.