
Efficient robust optimal transport: formulations and algorithms
The problem of robust optimal transport (OT) aims at recovering the best...
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Learning Geometric Word MetaEmbeddings
We propose a geometric framework for learning metaembeddings of words f...
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A Simple Approach to Learning Unsupervised Multilingual Embeddings
Recent progress on unsupervised learning of crosslingual embeddings in ...
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Riemannian optimization on the simplex of positive definite matrices
We discuss optimizationrelated ingredients for the Riemannian manifold ...
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Detection of Review Abuse via SemiSupervised Binary MultiTarget Tensor Decomposition
Product reviews and ratings on ecommerce websites provide customers wit...
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Lowrank approximations of hyperbolic embeddings
The hyperbolic manifold is a smooth manifold of negative constant curvat...
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Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
Dictionary leaning (DL) and dimensionality reduction (DR) are powerful t...
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Adaptive stochastic gradient algorithms on Riemannian manifolds
Adaptive stochastic gradient algorithms in the Euclidean space have attr...
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McTorch, a manifold optimization library for deep learning
In this paper, we introduce McTorch, a manifold optimization library for...
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Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
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Learning Multilingual Word Embeddings in a Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
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Lowrank geometric mean metric learning
We propose a lowrank approach to learning a Mahalanobis metric from dat...
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A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
We present a novel framework for domain adaptation, whereby both geometr...
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Bayesian SemiSupervised Tensor Decomposition using Natural Gradients for Anomaly Detection
Anomaly Detection has several important applications. In this paper, our...
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Inductive Framework for MultiAspect Streaming Tensor Completion with Side Information
Lowrank tensor completion is a wellstudied problem and has application...
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A Dual Framework for Lowrank Tensor Completion
We propose a novel formulation of the lowrank tensor completion problem...
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A Saddle Point Approach to Structured Lowrank Matrix Learning
We propose a novel optimization approach for learning a lowrank matrix ...
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Riemannian stochastic quasiNewton algorithm with variance reduction and its convergence analysis
Stochastic variance reduction algorithms have recently become popular fo...
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Riemannian stochastic variance reduced gradient
Stochastic variance reduction algorithms have recently become popular fo...
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Lowrank tensor completion: a Riemannian manifold preconditioning approach
We propose a novel Riemannian manifold preconditioning approach for the ...
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Riemannian stochastic variance reduced gradient on Grassmann manifold
Stochastic variance reduction algorithms have recently become popular fo...
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Symmetryinvariant optimization in deep networks
Recent works have highlighted scale invariance or symmetry that is prese...
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Understanding symmetries in deep networks
Recent works have highlighted scale invariance or symmetry present in th...
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Heterogeneous Tensor Decomposition for Clustering via Manifold Optimization
Tensors or multiarray data are generalizations of matrices. Tensor clust...
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Manopt, a Matlab toolbox for optimization on manifolds
Optimization on manifolds is a rapidly developing branch of nonlinear op...
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