Large models such as GPT-3 and ChatGPT have transformed deep learning (DL), powering
applications that have captured the public's imagination. Such models must be trained on …

The sparse module of the popular SciPy Python library is widely used across applications in
scientific computing, data analysis and machine learning. The standard implementation of …

It is well-established that large, diverse datasets play a pivotal role in the performance of
modern AI systems for text and image modalities. However, there are no datasets for tabular …

Bangkok is notorious for its chronic traffic congestion due to the rapid urbanization and the
haphazard city plan. The Sathorn Road network area stands to be one of the most critical …

Rewrite systems [11, 16, 18] have been widely employing equality saturation [15], which is
an optimisation methodology that uses a saturated e-graph to represent all possible …

Deep learning is emerging as an effective tool in drug discovery, with potential applications
in both predictive and generative models. Generative Flow Networks (GFlowNets/GFNs) are …

In this article, we develop a modular framework for the application of Reinforcement
Learning to the problem of Optimal Trade Execution. The framework is designed with …

There is a critical need for the development of cleaner and more efficient combustion
technologies to limit their detrimental effects on the climate and the environment and reduce …

Abstract Machine learning (ML) models have been shown to leak private information from
their training datasets. Differential Privacy (DP), typically implemented through the …

Over the past decade, video games have become increasingly utilised for research in
artificial intelligence. Perhaps the most extensive use of video games has been as …