Aymeric Daphnis Kévin Dieuleveut

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Related publications (4)

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations

Martin Jaggi, Sidak Pal Singh, Aymeric Daphnis Kévin Dieuleveut, Andreas Hug

We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts w ...

ADDISON-WESLEY PUBL CO2020

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Aymeric Daphnis Kévin Dieuleveut, Scott William Pesme, Nicolas Henri Bernard Flammarion

Constant step-size Stochastic Gradient Descent exhibits two phases: a transient phase during which iterates make fast progress towards the optimum, followed by a stationary phase during which iterates oscillate around the optimal point. In this paper, we s ...

2020

Communication trade-offs for Local-SGD with large step size

Aymeric Daphnis Kévin Dieuleveut

Synchronous mini-batch SGD is state-of-the-art for large-scale distributed machine learning. However, in practice, its convergence is bottlenecked by slow communication rounds between worker nodes. A natural solution to reduce communication is to use the " ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Unsupervised Scalable Representation Learning for Multivariate Time Series

Martin Jaggi, Aymeric Daphnis Kévin Dieuleveut

Time series constitute a challenging data type for machine learning algorithms, due to their highly variable lengths and sparse labeling in practice. In this paper, we tackle this challenge by proposing an unsupervised method to learn universal embeddings ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

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