Publications (6)
DiSCo: Diffusion Sequence Copilots for Shared Autonomy
Andy Wang, Xu Yan, Brandon McMahan +9
Shared autonomy combines human user and AI copilot actions to control complex systems such as robotic arms. When a task is challenging, requires high dimensional control, or is sub…
SPADE-S: A Sparsity-Robust Foundational Forecaster
Malcolm Wolff, Matthew Li, Ravi Kiran Selvam +11
Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging fo…
Towards Trainable Saliency Maps in Medical Imaging
Mehak Aggarwal, Nishanth Arun, Sharut Gupta +9
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread…
Redatuming physical systems using symmetric autoencoders
Pawan Bharadwaj, Matthew Li, Laurent Demanet
This paper considers physical systems described by hidden states and indirectly observed through repeated measurements corrupted by unmodeled nuisance parameters. A network-based r…
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data.…
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime
Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales. Our framework consists of the newly intro…