163 citations · 704 across the 74 of their papers we have counts for
7 papers · 1 filter
Robust and scalable methods for the dynamic mode decomposition
Travis Askham, Peng Zheng, Aleksandr Aravkin +1
The dynamic mode decomposition (DMD) is a broadly applicable dimensionality reduction algorithm that approximates a matrix containing time-series data by the outer product of a mat…
Distributed Bundle Adjustment
Karthikeyan Natesan Ramamurthy, Chung-Ching Lin, Aleksandr Aravkin +2
Most methods for Bundle Adjustment (BA) in computer vision are either centralized or operate incrementally. This leads to poor scaling and affects the quality of solution as the nu…
Learning Robust Representations for Computer Vision
Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy +1
Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in th…
Time Series Using Exponential Smoothing Cells
Avner Abrami, Aleksandr Y. Aravkin, Younghun Kim
Time series analysis is used to understand and predict dynamic processes, including evolving demands in business, weather, markets, and biological rhythms. Exponential smoothing is…
Estimating Shape Parameters of Piecewise Linear-Quadratic Problems
Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy
Piecewise Linear-Quadratic (PLQ) penalties are widely used to develop models in statistical inference, signal processing, and machine learning. Common examples of PLQ penalties inc…
Foundations of gauge and perspective duality
Alexandre Y. Aravkin, James V. Burke, Dmitriy Drusvyatskiy +2
We revisit the foundations of gauge duality and demonstrate that it can be explained using a modern approach to duality based on a perturbation framework. We therefore put gauge du…