Scalable Spatiotemporal Prediction with Bayesian Neural Fields

1 code implementation • 12 Mar 2024 • Feras Saad, Jacob Burnim, Colin Carroll, Brian Patton, Urs Köster, Rif A. Saurous, Matthew Hoffman

Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in many scientific and business-intelligence applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting.

Bayesian Inference Uncertainty Quantification

Adaptive Braking for Mitigating Gradient Delay

no code implementations • 2 Jul 2020 • Abhinav Venigalla, Atli Kosson, Vitaliy Chiley, Urs Köster

Neural network training is commonly accelerated by using multiple synchronized workers to compute gradient updates in parallel.

Pipelined Backpropagation at Scale: Training Large Models without Batches

no code implementations • 25 Mar 2020 • Atli Kosson, Vitaliy Chiley, Abhinav Venigalla, Joel Hestness, Urs Köster

New hardware can substantially increase the speed and efficiency of deep neural network training.

Image Classification Stochastic Optimization

Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks

no code implementations • NeurIPS 2017 • Urs Köster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oğuz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao

Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.

Generative Adversarial Network