Search Results for author: Mayank Meghwanshi

Found 4 papers, 1 papers with code

A Simple Approach to Learning Unsupervised Multilingual Embeddings

no code implementations EMNLP 2020 Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra

Recent progress on unsupervised learning of cross-lingual embeddings in bilingual setting has given impetus to learning a shared embedding space for several languages without any supervision.

Bilingual Lexicon Induction Dependency Parsing +3

Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings

no code implementations ACL 2020 Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra

We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages.

Bilingual Lexicon Induction Domain Adaptation +1

Low-rank approximations of hyperbolic embeddings

no code implementations18 Mar 2019 Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra

While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine learning and natural language processing communities lately due to its usefulness in modeling continuous hierarchies.

McTorch, a manifold optimization library for deep learning

1 code implementation3 Oct 2018 Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, Bamdev Mishra

In this paper, we introduce McTorch, a manifold optimization library for deep learning that extends PyTorch.

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