
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
Explainable machine learning and artificial intelligence models have bee...
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Domain Aware Markov Logic Networks
Combining logic and probability has been a long standing goal of AI. Mar...
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Lifted Marginal MAP Inference
Lifted inference reduces the complexity of inference in relational proba...
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Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
We propose a simple and easy to implement neural network compression alg...
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Lifted RegionBased Belief Propagation
Due to the intractable nature of exact lifted inference, research has re...
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JoinGraph Propagation Algorithms
The paper investigates parameterized approximate messagepassing schemes...
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Structured Message Passing
In this paper, we present structured message passing (SMP), a unifying f...
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A Complete Anytime Algorithm for Treewidth
In this paper, we present a Branch and Bound algorithm called QuickBB fo...
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Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid ...
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Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
This paper describes a general framework called Hybrid Dynamic Mixed Net...
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AND/OR Importance Sampling
The paper introduces AND/OR importance sampling for probabilistic graphi...
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FormulaBased Probabilistic Inference
Computing the probability of a formula given the probabilities or weight...
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Probabilistic Theorem Proving
Many representation schemes combining firstorder logic and probability ...
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Approximation by Quantization
Inference in graphical models consists of repeatedly multiplying and sum...
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Vibhav Gogate
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