16,663 research outputs found
Lopez-Uribe et al - ML Phylogenetic Tree
No full textThis file contains the best maximum likelihood phylogenetic tree obtained from an analysis in RAxML using a modified dataset from Hedtke et al 2013
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
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
ModelXGlue: a benchmarking framework for ML tools in MDE
No full textThe integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue, a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue. This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE
MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems
No full textMachine learning-based systems play a critical and increasingly pervasive role in various aspects of daily life. Despite the growing recognition of the importance of producing high-quality code for Machine Learning (ML) pipelines to ensure proper evolution, maintenance, and reusability, actionable guidance at the design and implementation levels remains scarce. This paper introduces MLSToolbox Code Generator, a low-code tool designed to support data scientists in graphically defining ML pipelines and generating their corresponding Python code. The tool leverages core Software Engineering design principles to promote high-quality Python code. Through a detailed example, we demonstrate how data scientists can use the tool. The flexible and extensible architecture of the tool enables data scientists to customize ML pipeline generation to meet domain-specific requirements, fostering greater efficiency and adaptability in ML workflows.This work was supported by the “Spanish Ministerio de Ciencia e Innovación” under project / funding scheme PID2024-156019OB-I00.Peer ReviewedPostprint (published version
ml-struct-bio/cryodrgn: v3.1.0-b: interactive filtering
No full text<p>We have introduced a number of small fixes and feature updates since our last release <code>v3.0.1-beta</code>:</p>
<ul>
<li>creating a new interactive command-line interface <code>cryodrgn filter</code> as an alternative to the buggy interface in the Jupyter filtering notebook (https://github.com/ml-struct-bio/cryodrgn/issues/323)</li>
<li>making <code>cryodrgn analyze</code> produce a plot of the learning curve (https://github.com/ml-struct-bio/cryodrgn/issues/304)</li>
<li>adding cell in <code>cryoDRGN_filtering</code> jupyter notebook returned by <code>cryodrgn analyze</code> for filtering by UMAP/PC values (https://github.com/ml-struct-bio/cryodrgn/pull/313)</li>
<li>fixing bugs with deprecated signatures in plotting functions (https://github.com/ml-struct-bio/cryodrgn/issues/322) and numpy dependency versioning (https://github.com/ml-struct-bio/cryodrgn/issues/318)</li>
</ul>
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
Sampling Chemical Space: Activity Cliffs, Extended Similarity, and ML Performance
No full textThe presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its data high dependency, Machine Learning QSAR models will be highly influenced by the activity landscape of the data. We propose several extended similarity and extended SALI methods to study the implications of ACs distribution on the training and test sets on the model’s errors. Non-uniform ACs and chemical space distribution tends to lead to worse models than the proposed uniform methods. ML modeling on AC-rich sets needs to be analyzed case-by-case. Proposed methods can be used as a tool to study the dataset, with random and uniform splitting being the better overall data splitting alternatives
'Project smells' - Experiences in Analysing the Software Quality of ML Projects with mllint
No full textMachine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user. 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.Software EngineeringSoftware Technolog
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