McTorch, a manifold optimization library for deep learning

Mayank Meghwanshi; Pratik Jawanpuria; Anoop Kunchukuttan; Hiroyuki Kasai; Bamdev Mishra

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arXiv · 1810.01811

McTorch, a manifold optimization library for deep learning

Mayank Meghwanshi·Pratik Jawanpuria·Anoop Kunchukuttan·Hiroyuki Kasai·Bamdev Mishra

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Abstract

In this paper, we introduce McTorch, a manifold optimization library for deep learning that extends PyTorch. It aims to lower the barrier for users wishing to use manifold constraints in deep learning applications, i.e., when the parameters are constrained to lie on a manifold. Such constraints include the popular orthogonality and rank constraints, and have been recently used in a number of applications in deep learning. McTorch follows PyTorch's architecture and decouples manifold definitions and optimizers, i.e., once a new manifold is added it can be used with any existing optimizer and vice-versa. McTorch is available at https://github.com/mctorch .

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BibTeX RIS

Mayank Meghwanshi, Pratik Jawanpuria, Anoop Kunchukuttan, Hiroyuki Kasai, Bamdev Mishra. 2018-10-03. McTorch, a manifold optimization library for deep learning. https://arxiv.org/abs/1810.01811

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