244 citations · 348 across the 9 of their papers we have counts for
11 papers
Scalable Spatiotemporal Prediction with Bayesian Neural Fields
Feras Saad, Jacob Burnim, Colin Carroll +4
Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-de…
Robust Inverse Graphics via Probabilistic Inference
Tuan Anh Le, Pavel Sountsov, Matthew D. Hoffman +3
How do we infer a 3D scene from a single image in the presence of corruptions like rain, snow or fog? Straightforward domain randomization relies on knowing the family of corruptio…
Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes
Nishad Gothoskar, Matin Ghavami, Eric Li +8
Robots cannot yet match humans' ability to rapidly learn the shapes of novel 3D objects and recognize them robustly despite clutter and occlusion. We present Bayes3D, an uncertaint…
Sequential Monte Carlo Learning for Time Series Structure Discovery
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman +2
This paper presents a new approach to automatically discovering accurate models of complex time series data. Working within a Bayesian nonparametric prior over a symbolic space of…
Automatically Batching Control-Intensive Programs for Modern Accelerators
Alexey Radul, Brian Patton, Dougal Maclaurin +2
We present a general approach to batching arbitrary computations for accelerators such as GPUs. We show orders-of-magnitude speedups using our method on the No U-Turn Sampler (NUTS…
Universal Sound Separation
Ilya Kavalerov, Scott Wisdom, Hakan Erdogan +4
Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating…