4 accepted papers
The utility of Graph Neural Networks (GNN) for the paradigm of forecasting short-term stock price movements is investigated. In particular, a finance-specific graph pooling operation, referred to as StockPool, is introduced to efficiently coarsen the stock graph. This is achieved by employing domain…
Hyperparameter optimisation is a prerequisite for state-of-the- art performance in machine learning, with current strategies including Bayesian optimisation, hyperband, and evolutionary methods. While such methods have been shown to improve performance, none of these is designed to explicitly take a…
The attention mechanism is at the core of modern Natural Language Processing (NLP) models, owing to its ability to focus on the most contextually relevant part of a sequence. However, current attention models rely on "flat-view" matrix methods to process tokens embedded in vector spaces; this result…
Tensor network (TN) methods have proven their considerable potential in deterministic regression and classification related paradigms, but remain underexplored in probabilistic settings. To this end, we introduce a variational inference framework for supervised learning in the context of TNs, referr…