Search Results for author: Vinod Nair

Found 11 papers, 2 papers with code

Automap: Towards Ergonomic Automated Parallelism for ML Models

no code implementations6 Dec 2021 Michael Schaarschmidt, Dominik Grewe, Dimitrios Vytiniotis, Adam Paszke, Georg Stefan Schmid, Tamara Norman, James Molloy, Jonathan Godwin, Norman Alexander Rink, Vinod Nair, Dan Belov

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 parallelism.

Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs

1 code implementation21 Jul 2021 Nicolas Sonnerat, Pengming Wang, Ira Ktena, Sergey Bartunov, Vinod Nair

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 searching a large neighborhood around the current assignment.

Combinatorial Optimization Imitation Learning

Continuous Latent Search for Combinatorial Optimization

no code implementations NeurIPS Workshop LMCA 2020 Sergey Bartunov, Vinod Nair, Peter Battaglia, Tim Lillicrap

Combinatorial optimization problems are notoriously hard because they often require enumeration of the exponentially large solution space.

Combinatorial Optimization

Neural Large Neighborhood Search

no code implementations NeurIPS Workshop LMCA 2020 Ravichandra Addanki, Vinod Nair, Mohammad Alizadeh

Results on several datasets show that it is possible to learn a neighbor selection policy that allows LNS to efficiently find good solutions.

Combinatorial Optimization

Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving

no code implementations4 Dec 2019 Xujie Si, Yujia Li, Vinod Nair, Felix Gimeno

We share this observation in the hope that it helps the SAT community better understand the hardness of random instances used in competitions and inspire other interesting ideas on SAT solving.

A Structured Prediction Approach for Missing Value Imputation

no code implementations9 Nov 2013 Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi

In this paper we propose a structured output approach for missing value imputation that also incorporates domain constraints.

Imputation Structured Prediction

3D Object Recognition with Deep Belief Nets

no code implementations NeurIPS 2009 Vinod Nair, Geoffrey E. Hinton

Our model achieves 6. 5% error on the test set, which is close to the best published result for NORB (5. 9%) using a convolutional neural net that has built-in knowledge of translation invariance.

3D Object Recognition Translation

Implicit Mixtures of Restricted Boltzmann Machines

no code implementations NeurIPS 2008 Vinod Nair, Geoffrey E. Hinton

We present a mixture model whose components are Restricted Boltzmann Machines (RBMs).

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