Learning parametrised regularisation functions via quotient minimisation

Verifying Parametrised Hardware Designs Via Counter Automata

The paper presents a new approach to formal verification of generic (i.e. parametrised) hardware designs specified in VHDL. The proposed approach is based on a translation of such designs to counter automata and on exploiting the recent advances achieved in the area of their automated formal verification. We have implemented the proposed translation. Using one of the state-of-the-art tools for ...

Regularisation in Sequential Learning Algorithms

In this paper, we discuss regularisation in online/sequential learning algorithms. In environments where data arrives sequentially, techniques such as cross-validation to achieve regularisation or model selection are not possible. Further, bootstrapping to determine a confidence level is not practical. To surmount these problems, a minimum variance estimation approach that makes use of the exte...

Variational data assimilation via sparse regularisation

This paper studies the role of sparse regularisation in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest exhibits sparsity in the real or transform domains. We show that in the presence of sparsity, the ‘1-norm regularisation produces more accurate a...

Learning Robust Graph Regularisation for Subspace Clustering

Various subspace clustering methods have benefited from introducing a graph regularisation term in their objective functions. In this work, we identify two critical limitations of the graph regularisation term employed in existing subspace clustering models and provide solutions for both of them. First, the squared l2-norm used in the existing term is replaced by a l1-norm term to make the regu...

Fibers, Surjective Functions, and Quotient Groups