On hyperparameter optimization of machine learning algorithms: Theory and practice
Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
Big data systems meet machine learning challenges: towards big data science as a service
Recently, we have been witnessing huge advancements in the scale of data we routinely
generate and collect in pretty much everything we do, as well as our ability to exploit modern …
generate and collect in pretty much everything we do, as well as our ability to exploit modern …
Automated machine learning: State-of-the-art and open challenges
With the continuous and vast increase in the amount of data in our digital world, it has been
acknowledged that the number of knowledgeable data scientists can not scale to address …
acknowledged that the number of knowledgeable data scientists can not scale to address …
Data lifecycle challenges in production machine learning: a survey
Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …
Predictive analytics for demand forecasting: A deep learning-based decision support system
The demand is often forecasted using econometric (regression) or statistical forecasting
methods. However, most of these methods lack the ability to model both temporal (linear and …
methods. However, most of these methods lack the ability to model both temporal (linear and …
AI meets database: AI4DB and DB4AI
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
Elastic machine learning algorithms in amazon sagemaker
There is a large body of research on scalable machine learning (ML). Nevertheless, training
ML models on large, continuously evolving datasets is still a difficult and costly undertaking …
ML models on large, continuously evolving datasets is still a difficult and costly undertaking …
Serverless linear algebra
Datacenter disaggregation provides numerous benefits to both the datacenter operator and
the application designer. However switching from the server-centric model to a …
the application designer. However switching from the server-centric model to a …
Database meets artificial intelligence: A survey
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
A survey on large-scale machine learning
Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …
quality predictions and having been widely used in real-world applications, such as text …