Search Results for author: Vibhav Gogate

Found 16 papers, 0 papers with code

A Novel Approach for Constrained Optimization in Graphical Models

no code implementations NeurIPS 2020 Sara Rouhani, Tahrima Rahman, Vibhav Gogate

Given two (possibly identical) PGMs $M_1$ and $M_2$ defined over the same set of variables and a real number $q$, find an assignment of values to all variables such that the probability of the assignment is maximized w. r. t.

Domain Aware Markov Logic Networks

no code implementations3 Jul 2018 Happy Mittal, Ayush Bhardwaj, Vibhav Gogate, Parag Singla

Experiments on the benchmark Friends & Smokers domain show that our ap- proach results in significantly higher accuracies compared to existing methods when testing on domains whose sizes different from those seen during training.

Lifted Marginal MAP Inference

no code implementations2 Jul 2018 Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla

Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other.

Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization

no code implementations14 Jun 2018 Yibo Yang, Nicholas Ruozzi, Vibhav Gogate

We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods.

Neural Network Compression Quantization

Automatic Parameter Tying in Neural Networks

no code implementations ICLR 2018 Yibo Yang, Nicholas Ruozzi, Vibhav Gogate

Recently, there has been growing interest in methods that perform neural network compression, namely techniques that attempt to substantially reduce the size of a neural network without significant reduction in performance.

L2 Regularization Neural Network Compression +1

Joint Inference for Event Coreference Resolution

no code implementations COLING 2016 Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng

Event coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs.

Coreference Resolution Event Coreference Resolution

Lifted Region-Based Belief Propagation

no code implementations30 Jun 2016 David Smith, Parag Singla, Vibhav Gogate

Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation.

Probabilistic Inference Modulo Theories

no code implementations26 May 2016 Rodrigo de Salvo Braz, Ciaran O'Reilly, Vibhav Gogate, Rina Dechter

We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, propositional, equalities on discrete sorts, and inequalities, more specifically difference arithmetic, on bounded integers).

Join-Graph Propagation Algorithms

no code implementations15 Jan 2014 Robert Mateescu, Kalev Kask, Vibhav Gogate, Rina Dechter

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP).

Structured Message Passing

no code implementations26 Sep 2013 Vibhav Gogate, Pedro Domingos

In this paper, we present structured message passing (SMP), a unifying framework for approximate inference algorithms that take advantage of structured representations such as algebraic decision diagrams and sparse hash tables.

Dynamic Blocking and Collapsing for Gibbs Sampling

no code implementations26 Sep 2013 Deepak Venugopal, Vibhav Gogate

Our dynamic algorithm periodically updates the partitioning into blocked and collapsed variables by leveraging correlation statistics gathered from the generated samples and enables rapid mixing by blocking together and collapsing highly correlated variables.

On Lifting the Gibbs Sampling Algorithm

no code implementations NeurIPS 2012 Deepak Venugopal, Vibhav Gogate

Statistical relational learning models combine the power of first-order logic, the de facto tool for handling relational structure, with that of probabilistic graphical models, the de facto tool for handling uncertainty.

Relational Reasoning

Learning Efficient Markov Networks

no code implementations NeurIPS 2010 Vibhav Gogate, William Webb, Pedro Domingos

We present an algorithm for learning high-treewidth Markov networks where inference is still tractable.

Lifted Inference Seen from the Other Side : The Tractable Features

no code implementations NeurIPS 2010 Abhay Jha, Vibhav Gogate, Alexandra Meliou, Dan Suciu

Lifted inference algorithms for representations that combine first-order logic and probabilistic graphical models have been the focus of much recent research.

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