438 citations · 549 across the 9 of their papers we have counts for
9 papers
A Normative Theory of Decision Making from Multiple Stimuli: The Contextual Diffusion Decision Model
Michael Shvartsman, Vaibhav Srivastava, Narayanan Sundaram +1
The dynamics of simple two-alternative forced-choice (2AFC) decisions are well-modeled by a class of random walk models (e.g. Laming, 1968; Ratcliff, 1978; Usher & McClelland, 2001…
First-Generation Inference Accelerator Deployment at Facebook
Michael Anderson, Benny Chen, Stephen Chen +112
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesse…
The LDBC Graphalytics Benchmark
Alexandru Iosup, Ahmed Musaafir, Alexandru Uta +15
In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms. The main goal of Graphalytics is to enable the fair and objective compa…
Matrix-normal models for fMRI analysis
Michael Shvartsman, Narayanan Sundaram, Mikio C. Aoi +3
Multivariate analysis of fMRI data has benefited substantially from advances in machine learning. Most recently, a range of probabilistic latent variable models applied to fMRI dat…
Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies
Brian Friesen, Md. Mostofa Ali Patwary, Brian Austin +8
The nature of dark energy and the complete theory of gravity are two central questions currently facing cosmology. A vital tool for addressing them is the 3-point correlation funct…
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
Thorsten Kurth, Jian Zhang, Nadathur Satish +12
This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised conv…
PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures
Md. Mostofa Ali Patwary, Nadathur Rajagopalan Satish, Narayanan Sundaram +8
Computing -Nearest Neighbors (KNN) is one of the core kernels used in many machine learning, data mining and scientific computing applications. Although kd-tree based $O(\log n)…
GraphMat: High performance graph analytics made productive
Narayanan Sundaram, Nadathur Rajagopalan Satish, Md Mostofa Ali Patwary +4
Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat…
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek, Oren Rippel, Kevin Swersky +6
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined b…