research
∙
04/20/2023
Light-weight Deep Extreme Multilabel Classification
Extreme multi-label (XML) classification refers to the task of supervise...
share
research
∙
11/30/2022
Generalised Spherical Text Embedding
This paper aims to provide an unsupervised modelling approach that allow...
research
∙
10/10/2022
Rieoptax: Riemannian Optimization in JAX
We present Rieoptax, an open source Python library for Riemannian optimi...
research
∙
10/04/2022
ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits
In this work, we propose a multi-armed bandit based framework for identi...
research
∙
05/19/2021
Light-weight Document Image Cleanup using Perceptual Loss
Smartphones have enabled effortless capturing and sharing of documents i...
research
∙
03/18/2021
SPOT: A framework for selection of prototypes using optimal transport
In this work, we develop an optimal transport (OT) based framework to se...
research
∙
03/01/2021
Manifold optimization for optimal transport
Optimal transport (OT) has recently found widespread interest in machine...
research
∙
10/22/2020
Efficient robust optimal transport: formulations and algorithms
The problem of robust optimal transport (OT) aims at recovering the best...
research
∙
04/20/2020
Learning Geometric Word Meta-Embeddings
We propose a geometric framework for learning meta-embeddings of words f...
research
∙
04/10/2020
A Simple Approach to Learning Unsupervised Multilingual Embeddings
Recent progress on unsupervised learning of cross-lingual embeddings in ...
research
∙
02/08/2020
Statistical Optimal Transport posed as Learning Kernel Embedding
This work takes the novel approach of posing the statistical Optimal Tra...
research
∙
06/25/2019
Riemannian optimization on the simplex of positive definite matrices
We discuss optimization-related ingredients for the Riemannian manifold ...
research
∙
03/18/2019
Low-rank approximations of hyperbolic embeddings
The hyperbolic manifold is a smooth manifold of negative constant curvat...
research
∙
02/04/2019
Adaptive stochastic gradient algorithms on Riemannian manifolds
Adaptive stochastic gradient algorithms in the Euclidean space have attr...
research
∙
10/03/2018
McTorch, a manifold optimization library for deep learning
In this paper, we introduce McTorch, a manifold optimization library for...
research
∙
08/27/2018
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
research
∙
08/27/2018
Learning Multilingual Word Embeddings in a Latent Metric Space: A Geometric Approach
We propose a novel geometric approach for learning bilingual mappings gi...
research
∙
06/14/2018
Low-rank geometric mean metric learning
We propose a low-rank approach to learning a Mahalanobis metric from dat...
research
∙
12/04/2017
A Dual Framework for Low-rank Tensor Completion
We propose a novel formulation of the low-rank tensor completion problem...
research
∙
04/24/2017
A Saddle Point Approach to Structured Low-rank Matrix Learning
We propose a novel optimization approach for learning a low-rank matrix ...
research
∙
11/18/2015