Benchmark and survey of automated machine learning frameworks
Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …
However, building well performing machine learning applications requires highly …
Machine Learning in Tourism: A Brief Overview: Generation of Knowledge from Experience
R Egger - Applied data science in tourism: Interdisciplinary …, 2022 - Springer
In the last few years, a large hype around the topic of Machine Learning (ML) has emerged;
this can be justified thanks to various ML approaches having recently undergone rapid …
this can be justified thanks to various ML approaches having recently undergone rapid …
A comparison of AutoML tools for machine learning, deep learning and XGBoost
This paper presents a benchmark of supervised Automated Machine Learning (AutoML)
tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto …
tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto …
[HTML][HTML] Automated data processing and feature engineering for deep learning and big data applications: a survey
A Mumuni, F Mumuni - Journal of Information and Intelligence, 2024 - Elsevier
Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly
from data. This approach has achieved impressive results and has contributed significantly …
from data. This approach has achieved impressive results and has contributed significantly …
Lightautoml: Automl solution for a large financial services ecosystem
A Vakhrushev, A Ryzhkov, M Savchenko… - arXiv preprint arXiv …, 2021 - arxiv.org
We present an AutoML system called LightAutoML developed for a large European financial
services company and its ecosystem satisfying the set of idiosyncratic requirements that this …
services company and its ecosystem satisfying the set of idiosyncratic requirements that this …
{SOTER}: Guarding Black-box Inference for General Neural Networks at the Edge
The prosperity of AI and edge computing has pushed more and more well-trained DNN
models to be deployed on third-party edge devices to compose mission-critical applications …
models to be deployed on third-party edge devices to compose mission-critical applications …
Neural production networks: AI's infrastructural geographies
F Ferrari - Environment and Planning F, 2023 - journals.sagepub.com
It is commonly argued that a handful of technology firms own the infrastructure that
underpins the proliferation of artificial neural networks. But little is known about how this …
underpins the proliferation of artificial neural networks. But little is known about how this …
Deep fair models for complex data: Graphs labeling and explainable face recognition
The central goal of Algorithmic Fairness is to develop AI-based systems which do not
discriminate subgroups in the population with respect to one or multiple notions of inequity …
discriminate subgroups in the population with respect to one or multiple notions of inequity …
[PDF][PDF] Deep active learning for computer vision: Past and future
As an important data selection schema, active learning emerges as the essential component
when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the …
when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the …
Probabilistic safety risk assessment in large-diameter tunnel construction using an interactive and explainable tree-based pipeline optimization method
Due to knowledge alienation, the application of artificial intelligence (AI) techniques in
tunnel construction has been greatly stunted in recent years. In order to motivate the …
tunnel construction has been greatly stunted in recent years. In order to motivate the …