42 citations · 89 across the 8 of their papers we have counts for
8 papers
Solving MaxSAT with Matrix Multiplication
David Warde-Farley, Vinod Nair, Yujia Li +3
We propose an incomplete algorithm for Maximum Satisfiability (MaxSAT) specifically designed to run on neural network accelerators such as GPUs and TPUs. Given a MaxSAT problem ins…
Automap: Towards Ergonomic Automated Parallelism for ML Models
Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis +8
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline…
Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
Nicolas Sonnerat, Pengming Wang, Ira Ktena +2
Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by…
Solving Mixed Integer Programs Using Neural Networks
Vinod Nair, Sergey Bartunov, Felix Gimeno +16
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice.…
Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving
Xujie Si, Yujia Li, Vinod Nair +1
We propose prioritized unit propagation with periodic resetting, which is a simple but surprisingly effective algorithm for solving random SAT instances that are meant to be hard.…
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
Aditya Paliwal, Felix Gimeno, Vinod Nair +4
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based wor…
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam +3
We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill…
A Structured Prediction Approach for Missing Value Imputation
Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam +1
Missing value imputation is an important practical problem. There is a large body of work on it, but there does not exist any work that formulates the problem in a structured outpu…