Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

The Gumbel-max trick is a method to draw a sample from a categorical distribution, given by
its unnormalized (log-) probabilities. Over the past years, the machine learning community …

Diffusion models are a class of flexible generative models trained with an approximation to
the log-likelihood objective. However, most use cases of diffusion models are not concerned …

A core technology that has emerged from the artificial intelligence revolution is the recurrent
neural network (RNN). Its unique sequence-based architecture provides a tractable …

An effective person re-identification (re-ID) model should learn feature representations that
are both discriminative, for distinguishing similar-looking people, and generalisable, for …

Policy gradients methods apply to complex, poorly understood, control problems by
performing stochastic gradient descent over a parameterized class of polices. Unfortunately …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

Data augmentation (DA) techniques aim to increase data variability, and thus train deep
networks with better generalisation. The pioneering AutoAugment automated the search for …

Quantum architecture search (QAS) is the process of automating architecture engineering of
quantum circuits. It has been desired to construct a powerful and general QAS platform …

This paper presents an empirical study regarding training probabilistic neural networks
using training objectives derived from PAC-Bayes bounds. In the context of probabilistic …