10,008 research outputs found
CellML, SED-ML, and the Physiome Model Repository
No full textInvited presentation delivered at COMBINE 2016.CellML, SED-ML, and the Physiome Model Repository.David Nickerson, Auckland Bioengineering Institute, University of Auckland, New Zealand.CellML is an XML-based protocol for storing and exchanging computer-based mathematical models in an unambiguous, modular, and reusable manner. In addition to introducing CellML, in this presentation I will provide some of physiological examples that have help drive the development and adoption of CellML. I will also present the proposed changes being introduced in the next version of the specification, CellML 2.0.The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format for encoding simulation setups to ensure exchangeability and reproducibility of simulation experiments. Here I will present an overview of the current version of SED-ML (Level 1 Version 2) and discuss the additional features introduced in the draft Level 1 Version 3 specification, as well as some of other upcoming features often requested.I will also demonstrate some of the functionality and content being added to the Physiome Model Repository which makes extensive use of both CellML and SED-ML to provide a rich repository of reproducible model descriptions.</div
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
No full textThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Reproducible computational biology experiments with SED-ML-the Simulation Experiment Description Markup Language
No full textThe increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
SED-ML
No full text<p>My SED-ML talk from the 7th International CellML Workshop, giving a brief overview of the current L1V1 specification and then going over the main changes to be introduced in SED-ML L1V2. The slides I presented are mostly borrowed from previous presentations by Dagmar Waltemath and Frank Bergmann.</p
Introduction to SED-ML
No full text<p>My "introduction to SED-ML" presentation from the 8th International CellML Workshop.</p
Mechanisms of Psychological Distress following War in the Former Yugoslavia: The Role of Interpersonal Sensitivity
No full textThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This study was funded by a grant from the European Commission, contract number INCO-CT-2004-509176. AN was supported by a Clinical Early Career Research Fellowship (113295) and a Project Grant (104288
Building a generalisable ML pipeline at ING
No full textAdvances in data science have caused an increase in the use of Artificial Intelligence (AI), specifically Machine Learning (ML), throughout various fields. Not only in research but in the industry as well, has ML been receiving increasing amounts of interest. Many companies rely on ML models to increase the efficiency of existing processes or offer new services and products. The industry, however, is facing several additional challenges compared to the academic context. One of those challenges is applying the Development Operations (DevOps) model to an ML application, also referred to as MLOps. This thesis sets out to find the specific challenges that practitioners encounter while operationalising ML models. To do so, we perform a single-case case study on an ML pipeline built by the Trade & Communication Surveillance team at the ING bank. This case study consists of conducting a set of interviews and performing a manual code inspection of the pipeline. The team faces challenges ranging from having insufficient time for operationalising each ML project individually to operating in the highlyregulated fintech context. Their pipeline is able to deploy a single ML model but it does not generalise well to other projects. We present the first version of an application that mitigates these challenges. The application is able to deploy ML models to the development environment at ING and can be operated by data scientists to reduce the effort of operationalising an ML model. Computer Science | Software Technolog
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