Asynchronous Execution of Python Code on Task Based Runtime Systems

Despite advancements in the areas of parallel and distributed computing, the complexity of programming on High Performance Computing (HPC) resources has deterred many domain experts, especially in the areas of machine learning and artificial intelligence (AI), from utilizing performance benefits of such systems. Researchers and scientists favor high-productivity languages to avoid the inconvenience of programming in low-level languages and costs of acquiring the necessary skills required for programming at this level. In recent years, Python, with the support of linear algebra libraries like NumPy, has gained popularity despite facing limitations which prevent this code from distributed runs. Here we present a solution which maintains both high level programming abstractions as well as parallel and distributed efficiency. Phylanx, is an asynchronous array processing toolkit which transforms Python and NumPy operations into code which can be executed in parallel on HPC resources by mapping Python and NumPy functions and variables into a dependency tree executed by HPX, a general purpose, parallel, task-based runtime system written in C++. Phylanx additionally provides introspection and visualization capabilities for debugging and performance analysis. We have tested the foundations of our approach by comparing our implementation of widely used machine learning algorithms to accepted NumPy standards

Machine Learning for Neuroimaging with Scikit-Learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.Comment: Frontiers in neuroscience, Frontiers Research Foundation, 2013, pp.1

Somoclu: An Efficient Parallel Library for Self-Organizing Maps

Somoclu is a massively parallel tool for training self-organizing maps on large data sets written in C++. It builds on OpenMP for multicore execution, and on MPI for distributing the workload across the nodes in a cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful for high-dimensional but sparse data, such as the vector spaces common in text mining workflows. Python, R and MATLAB interfaces facilitate interactive use. Apart from fast execution, memory use is highly optimized, enabling training large emergent maps even on a single computer.Comment: 26 pages, 9 figures. The code is available at https://peterwittek.github.io/somoclu

PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures

Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications of data science. However, since the (metric) space of persistence diagrams is not Hilbert, they end up being difficult inputs for most Machine Learning techniques. To address this concern, several vectorization methods have been put forward that embed persistence diagrams into either finite-dimensional Euclidean space or (implicit) infinite dimensional Hilbert space with kernels. In this work, we focus on persistence diagrams built on top of graphs. Relying on extended persistence theory and the so-called heat kernel signature, we show how graphs can be encoded by (extended) persistence diagrams in a provably stable way. We then propose a general and versatile framework for learning vectorizations of persistence diagrams, which encompasses most of the vectorization techniques used in the literature. We finally showcase the experimental strength of our setup by achieving competitive scores on classification tasks on real-life graph datasets

Deep learning architectures for Computer Vision

Deep learning has become part of many state-of-the-art systems in multiple disciplines (specially in computer vision and speech processing). In this thesis Convolutional Neural Networks are used to solve the problem of recognizing people in images, both for verification and identification. Two different architectures, AlexNet and VGG19, both winners of the ILSVRC, have been fine-tuned and tested with four datasets: Labeled Faces in the Wild, FaceScrub, YouTubeFaces and Google UPC, a dataset generated at the UPC. Finally, with the features extracted from these fine-tuned networks, some verifications algorithms have been tested including Support Vector Machines, Joint Bayesian and Advanced Joint Bayesian formulation. The results of this work show that an Area Under the Receiver Operating Characteristic curve of 99.6% can be obtained, close to the state-of-the-art performance.El aprendizaje profundo se ha convertido en parte de muchos sistemas en el estado del arte de múltiples ámbitos (especialmente en visión por computador y procesamiento de voz). En esta tesis se utilizan las Redes Neuronales Convolucionales para resolver el problema de reconocer a personas en imágenes, tanto para verificación como para identificación. Dos arquitecturas diferentes, AlexNet y VGG19, ambas ganadores del ILSVRC, han sido afinadas y probadas con cuatro conjuntos de datos: Labeled Faces in the Wild, FaceScrub, YouTubeFaces y Google UPC, un conjunto generado en la UPC. Finalmente con las características extraídas de las redes afinadas, se han probado diferentes algoritmos de verificación, incluyendo Maquinas de Soporte Vectorial, Joint Bayesian y Advanced Joint Bayesian. Los resultados de este trabajo muestran que el Área Bajo la Curva de la Característica Operativa del Receptor puede llegar a ser del 99.6%, cercana al valor del estado del arte.L’aprenentatge profund s’ha convertit en una part importat de molts sistemes a l’estat de l’art de múltiples àmbits (especialment de la visió per computador i el processament de veu). A aquesta tesi s’utilitzen les Xarxes Neuronals Convolucionals per a resoldre el problema de reconèixer persones a imatges, tant per verificació com per identificatió. Dos arquitectures diferents, AlexNet i VGG19, les dues guanyadores del ILSVRC, han sigut afinades i provades amb quatre bases de dades: Labeled Faces in the Wild, FaceScrub, YouTubeFaces i Google UPC, un conjunt generat a la UPC. Finalment, amb les característiques extretes de les xarxes afinades, s’han provat diferents algoritmes de verificació, incloent Màquines de Suport Vectorial, Joint Bayesian i Advanced Joint Bayesian. Els resultats d’aquest treball mostres que un Àrea Baix la Curva de la Característica Operativa del Receptor por arribar a ser del 99.6%, propera al valor de l’estat de l’art