Search Results for author: Daniel Sanz-Alonso

Found 6 papers, 1 papers with code

Auto-differentiable Ensemble Kalman Filters

1 code implementation16 Jul 2021 Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett

Data assimilation is concerned with sequentially estimating a temporally-evolving state.

Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective

no code implementations26 Aug 2020 Daniel Sanz-Alonso, Ruiyi Yang

In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption.

Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis

no code implementations6 Apr 2019 Nicolas Garcia Trillos, Daniel Sanz-Alonso, Ruiyi Yang

Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating mechanism.

Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning

no code implementations29 Jan 2019 Nicolas Garcia Trillos, Zach Kaplan, Daniel Sanz-Alonso

The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization.

On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms

no code implementations20 Oct 2017 Nicolas Garcia Trillos, Zachary Kaplan, Thabo Samakhoana, Daniel Sanz-Alonso

A popular approach to semi-supervised learning proceeds by endowing the input data with a graph structure in order to extract geometric information and incorporate it into a Bayesian framework.

Continuum Limit of Posteriors in Graph Bayesian Inverse Problems

no code implementations22 Jun 2017 Nicolas Garcia Trillos, Daniel Sanz-Alonso

We consider the problem of recovering a function input of a differential equation formulated on an unknown domain $M$.

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