49 citations · 88 across the 11 of their papers we have counts for
11 papers
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
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…
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…
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
Francois Belletti, Minmin Chen, Ed H. Chi
Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data. Long Range Dependence (LRD) --- referring to long-ran…
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…
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Jiaxi Tang, Francois Belletti, Sagar Jain +4
Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and ite…
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…
Top-K Off-Policy Correction for a REINFORCE Recommender System
Minmin Chen, Alex Beutel, Paul Covington +3
Industrial recommender systems deal with extremely large action spaces -- many millions of items to recommend. Moreover, they need to serve billions of users, who are unique at any…
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning
Francois Belletti, Daniel Haziza, Gabriel Gomes +1
This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be…
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Francois W. Belletti, Evan R. Sparks, Michael J. Franklin +2
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causa…
Embarrassingly Parallel Time Series Analysis for Large Scale Weak Memory Systems
Francois Belletti, Evan Sparks, Michael Franklin +1
Second order stationary models in time series analysis are based on the analysis of essential statistics whose computations follow a common pattern. In particular, with a map-reduc…