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An Introduction to Variational Methods for Graphical Models
TLDR
This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields), and describes a general framework for generating variational transformations based on convex duality.
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Exploiting Generative Models in Discriminative Classifiers
TLDR
A natural way of achieving this combination by deriving kernel functions for use in discriminative methods such as support vector machines from generative probability models is developed.
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Style Transfer from Non-Parallel Text by Cross-Alignment
TLDR
This paper proposes a method that leverages refined alignment of latent representations to perform style transfer on the basis of non-parallel text, and demonstrates the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
View PDF on arXiv
Junction Tree Variational Autoencoder for Molecular Graph Generation
TLDR
The junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network, which allows for incrementally expand molecules while maintaining chemical validity at every step.
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Maximum-Margin Matrix Factorization
TLDR
A novel approach to collaborative prediction is presented, using low-norm instead of low-rank factorizations, inspired by, and has strong connections to, large-margin linear discrimination.
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Rationalizing Neural Predictions
TLDR
The approach combines two modular components, generator and encoder, which are trained to operate well together and specifies a distribution over text fragments as candidate rationales and these are passed through the encoder for prediction.
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Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
TLDR
This paper proposes an alternative inference method for additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states.
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MAP estimation via agreement on trees: message-passing and linear programming
TLDR
This work develops and analyze methods for computing provably optimal maximum a posteriori probability (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles and establishes a connection between a certain LP relaxation of the mode-finding problem and a reweighted form of the max-product (min-sum) message-passing algorithm.
View on IEEE
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations
TLDR
A novel message passing algorithm for approximating the MAP problem in graphical models that is derived via block coordinate descent in a dual of the LP relaxation of MAP, but does not require any tunable parameters such as step size or tree weights.
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A new class of upper bounds on the log partition function
TLDR
A new class of upper bounds on the log partition function of a Markov random field (MRF) is introduced, based on concepts from convex duality and information geometry, and the Legendre mapping between exponential and mean parameters is exploited.
View on IEEE
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