Generative machine learning models have recently been applied to source code, for use
cases including translating code between programming languages, creating documentation …

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 …

Computational notebooks allow data scientists to express their ideas through a combination
of code and documentation. However, data scientists often pay attention only to the code …

Data science and machine learning (DS/ML) are at the heart of the recent advancements of
many Artificial Intelligence (AI) applications. There is an active research thread in AI,\autoai …

Cloud service providers have been shifting their workloads to microservices to take
advantage of their modularity, flexibility, agility, and scalability. However, numerous …

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series
forecasting applied to environmental datasets. The framework was applied for three different …

Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from
supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This …

To mitigate the pain of manually tuning hyperparameters of deep neural networks,
automated machine learning (AutoML) methods have been developed to search for an …

The rise of automated text processing systems has led to the development of tools designed
for a wide variety of application domains. These technologies are often developed to support …

Real-time news impact prediction on financial markets is a challenging task for finance
experts with limited IT expertise. Many practitioners build machine learning models trained …