[图书][B] The algebra and machine representation of statistical models

E Patterson - 2020 - search.proquest.com
As the twin movements of open science and open source bring an ever greater share of the
scientific process into the digital realm, new opportunities arise for the meta-scientific study …

Modelling serendipity in a computational context

J Corneli, A Jordanous, C Guckelsberger… - arXiv preprint arXiv …, 2014 - arxiv.org
The term serendipity describes a creative process that develops, in context, with the active
participation of a creative agent, but not entirely within that agent's control. While a system …

Generating effective software obfuscation sequences with reinforcement learning

H Wang, S Wang, D Xu, X Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Obfuscation is a prevalent security technique which transforms syntactic representation of a
program to a complicated form, but still keeps program semantics unchanged. So far …

Topological differential testing

K Ambrose, S Huntsman, M Robinson… - arXiv preprint arXiv …, 2020 - arxiv.org
We introduce topological differential testing (TDT), an approach to extracting the consensus
behavior of a set of programs on a corpus of inputs. TDT uses the topological notion of a …

Satyrn: A Platform for Analytics Augmented Generation

M Sterbentz, C Barrie, S Shahi, A Dutta… - arXiv preprint arXiv …, 2024 - arxiv.org
Large language models (LLMs) are capable of producing documents, and retrieval
augmented generation (RAG) has shown itself to be a powerful method for improving …

Cross-sectorial semantic model for support of data analytics in process industries

M Sarnovsky, P Bednar, M Smatana - Processes, 2019 - mdpi.com
The process industries rely on various software systems and use a wide range of
technologies. Predictive modeling techniques are often applied to data obtained from these …

Ontologies for data science: On its application to data pipelines

MÁ Sicilia, E García-Barriocanal… - Metadata and Semantic …, 2019 - Springer
Ontologies are usually applied to drive intelligent applications and also as a resource for
integrating or extracting information, as in the case of Natural Language Processing (NLP) …

A knowledge-driven AutoML architecture

C Cofaru, J Loeckx - arXiv preprint arXiv:2311.17124, 2023 - arxiv.org
This paper proposes a knowledge-driven AutoML architecture for pipeline and deep feature
synthesis. The main goal is to render the AutoML process explainable and to leverage …

[PDF][PDF] Lightweight Knowledge Representations for Automating Data Analysis

M Sterbentz, C Barrie, D Hooshmand… - arXiv preprint arXiv …, 2023 - c3lab.northwestern.edu
The principal goal of data science is to derive meaningful information from data. To do this,
data scientists develop a space of analytic possibilities and from it reach their information …

[PDF][PDF] Semantic program analysis for scientific model augmentation

C Herlihy, K Cao, S Reparti, E Briscoe… - Modeling the World's …, 2019 - jpfairbanks.com
SemanticModels. jl is a system for extracting semantic information from scientific code and
reconciling it with conceptual descriptions to build a knowledge graph. This knowledge …