
Improving Bayesian Inference in Deep Neural Networks with Variational Structured Dropout
Approximate inference in deep Bayesian networks exhibits a dilemma of ho...
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BoMbOT: On Batch of Minibatches Optimal Transport
Minibatch optimal transport (mOT) has been successfully used in practi...
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Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Choosing a proper set of kernel functions is an important problem in lea...
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Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Relational regularized autoencoder (RAE) is a framework to learn the dis...
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Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
Unveiling face images of a subject given his/her highlevel representati...
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Distributional SlicedWasserstein and Applications to Generative Modeling
SlicedWasserstein distance (SWD) and its variation, Max SlicedWasserst...
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On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm
We provide a computational complexity analysis for the Sinkhorn algorith...
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On Efficient Multilevel Clustering via Wasserstein Distances
We propose a novel approach to the problem of multilevel clustering, whi...
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Prediction, Consistency, Curvature: Representation Learning for LocallyLinear Control
Many realworld sequential decisionmaking problems can be formulated as...
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SpectralFPL: Online Spectral Learning for Single Topic Models
This paper studies how to efficiently learn an optimal latent variable m...
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Graphical Model Sketch
Structured highcardinality data arises in many domains, and poses a maj...
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Boosted Markov Networks for Activity Recognition
We explore a framework called boosted Markov networks to combine the lea...
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Human Activity Learning and Segmentation using Partially Hidden Discriminative Models
Learning and understanding the typical patterns in the daily activities ...
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Automorphism Groups of Graphical Models and Lifted Variational Inference
Using the theory of group action, we first introduce the concept of the ...
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Hung Bui
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