This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...

Brochu, E., Cora, V.M. and de Freitas, N. (2010) A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning.

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

RNA design is central to next-generation therapeutics, yet identifying sequences that reliably fold into desired structures ...

Learning from potential disinformation introduces specific cognitive biases, causing individuals to systematically deviate from an idealized Bayesian updating strategy.

Accurate sunlight data is becoming essential for the clean-energy transition, but tracking how much solar radiation reaches ...

Observation is no substitute for participation. As automation replaces hands-on entry-level work, we limit learning and ...

Open-source agentic coding model Ornith-1.0, released today under the MIT license, uses a self-improving reinforcement ...

Abstract: Reconfigurable intelligent surface (RIS) provides a promising way to build the programmable wireless transmission environments in the future. Owing to the large number of reflecting elements ...

Find out more about undergraduate study at the School of Electronic Engineering and Computer Science.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of ...