438 citations · 1.1k across the 12 of their papers we have counts for
12 papers
Coloring Big Graphs with AlphaGoZero
Jiayi Huang, Mostofa Patwary, Gregory Diamos
We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications. Because t…
Language Modeling at Scale
Mostofa Patwary, Milind Chabbi, Heewoo Jun +3
We show how Zipf's Law can be used to scale up language modeling (LM) to take advantage of more training data and more GPUs. LM plays a key role in many important natural language…
Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani +6
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely belie…
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…
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)…
A New Parallel Algorithm for Two-Pass Connected Component Labeling
Siddharth Gupta, Diana Palsetia, Md. Mostofa Ali Patwary +2
Connected Component Labeling (CCL) is an important step in pattern recognition and image processing. It assigns labels to the pixels such that adjacent pixels sharing the same feat…
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…
Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection
Bharath Pattabiraman, Md. Mostofa Ali Patwary, Assefaw H. Gebremedhin +2
The maximum clique problem is a well known NP-Hard problem with applications in data mining, network analysis, information retrieval and many other areas related to the World Wide…
Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin +1
We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a r…
What if CLIQUE were fast? Maximum Cliques in Information Networks and Strong Components in Temporal Networks
Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin +1
Exact maximum clique finders have progressed to the point where we can investigate cliques in million-node social and information networks, as well as find strongly connected compo…
Fast Algorithms for the Maximum Clique Problem on Massive Sparse Graphs
Bharath Pattabiraman, Md. Mostofa Ali Patwary, Assefaw H. Gebremedhin +2
The maximum clique problem is a well known NP-Hard problem with applications in data mining, network analysis, informatics, and many other areas. Although there exist several algor…