44 citations · 171 across the 16 of their papers we have counts for
17 papers
SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
Lizao Li, Rob Carver, Ignacio Lopez-Gomez +2
Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing un…
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez +5
We introduce a data-driven learning framework that assimilates two powerful ideas: ideal large eddy simulation (LES) from turbulence closure modeling and neural stochastic differen…
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models
Zhong Yi Wan, Ricardo Baptista, Yi-fan Chen +4
We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data…
Accelerating large-eddy simulations of clouds with Tensor Processing Units
Sheide Chammas, Qing Wang, Tapio Schneider +3
Clouds, especially low clouds, are crucial for regulating Earth's energy balance and mediating the response of the climate system to changes in greenhouse gas concentrations. Despi…
A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units
Qing Wang, Matthias Ihme, Yi-Fan Chen +1
A computational fluid dynamics (CFD) simulation framework for fluid-flow prediction is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with…
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Fantine Huot, R. Lily Hu, Matthias Ihme +6
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly…
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng +15
Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they…
Neural Collaborative Filtering vs. Matrix Factorization Revisited
Steffen Rendle, Walid Krichene, Li Zhang +1
Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to com…
Superbloom: Bloom filter meets Transformer
John Anderson, Qingqing Huang, Walid Krichene +2
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash to…
Sensitivity Analysis in the Dupire Local Volatility Model with Tensorflow
Francois Belletti, Davis King, James Lottes +2
In a recent paper, we have demonstrated how the affinity between TPUs and multi-dimensional financial simulation resulted in fast Monte Carlo simulations that could be setup in a f…
Large-Scale Discrete Fourier Transform on TPUs
Tianjian Lu, Yi-Fan Chen, Blake Hechtman +2
In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are asso…
High-resolution imaging on TPUs
Fantine Huot, Yi-Fan Chen, Robert Clapp +2
The rapid evolution of artificial intelligence (AI) is leading to a new generation of hardware accelerators optimized for deep learning. Some of the designs of these accelerators a…
Tensor Processing Units for Financial Monte Carlo
Francois Belletti, Davis King, Kun Yang +4
Monte Carlo methods are critical to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computa…
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Francois Belletti, Karthik Lakshmanan, Walid Krichene +7
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap, we propose…
High Performance Monte Carlo Simulation of Ising Model on TPU Clusters
Kun Yang, Yi-Fan Chen, Georgios Roumpos +2
Large-scale deep learning benefits from an emerging class of AI accelerators. Some of these accelerators' designs are general enough for compute-intensive applications beyond AI an…
Scalable Realistic Recommendation Datasets through Fractal Expansions
Francois Belletti, Karthik Lakshmanan, Walid Krichene +2
Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap w…
Efficient Training on Very Large Corpora via Gramian Estimation
Walid Krichene, Nicolas Mayoraz, Steffen Rendle +5
We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of r…