Challenges in deploying machine learning: a survey of case studies
In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …
to a field capable of solving real-world business problems. However, the deployment of …
[HTML][HTML] Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
The reticular chemistry of metal–organic frameworks (MOFs) allows for the generation of an
almost boundless number of materials some of which can be a substitute for the traditionally …
almost boundless number of materials some of which can be a substitute for the traditionally …
Autogluon-tabular: Robust and accurate automl for structured data
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a
single line of Python to train highly accurate machine learning models on an unprocessed …
single line of Python to train highly accurate machine learning models on an unprocessed …
Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …
impact on every industry and research discipline. At the core of this revolution lies the tools …
Auto-sklearn 2.0: Hands-free automl via meta-learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …
tedious task of designing machine learning pipelines and has recently achieved substantial …
Review of ML and AutoML solutions to forecast time-series data
A Alsharef, K Aggarwal, Sonia, M Kumar… - … Methods in Engineering, 2022 - Springer
Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …
of the same variable are analyzed to predict the future values of the time series. Its …
Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review
Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
Random search and reproducibility for neural architecture search
L Li, A Talwalkar - Uncertainty in artificial intelligence, 2020 - proceedings.mlr.press
Neural architecture search (NAS) is a promising research direction that has the potential to
replace expert-designed networks with learned, task-specific architectures. In order to help …
replace expert-designed networks with learned, task-specific architectures. In order to help …
[PDF][PDF] Taking human out of learning applications: A survey on automated machine learning
Machine learning techniques have deeply rooted in our everyday life. However, since it is
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …
knowledge-and labor-intensive to pursue good learning performance, humans are heavily …
Human-AI collaboration in data science: Exploring data scientists' perceptions of automated AI
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One
application domain is data science. New techniques in automating the creation of AI, known …
application domain is data science. New techniques in automating the creation of AI, known …