6 citations · 9 across the 5 of their papers we have counts for
6 papers
Meronymic Ontology Extraction via Large Language Models
Dekai Zhang, Simone Conia, Antonio Rago
Ontologies have become essential in today's digital age as a way of organising the vast amount of readily available unstructured text. In providing formal structure to this informa…
Clustered Federated Learning via Embedding Distributions
Dekai Zhang, Matthew Williams, Francesca Toni
Federated learning (FL) is a widely used framework for machine learning in distributed data environments where clients hold data that cannot be easily centralised, such as for data…
XAI-Units: Benchmarking Explainability Methods with Unit Tests
Jun Rui Lee, Sadegh Emami, Michael David Hollins +5
Feature attribution (FA) methods are widely used in explainable AI (XAI) to help users understand how the inputs of a machine learning model contribute to its outputs. However, dif…
Contestable AI needs Computational Argumentation
Francesco Leofante, Hamed Ayoobi, Adam Dejl +10
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI…
The Neumann problem of special Lagrangian type equations
Guohuan Qiu, Dekai Zhang
We study the Neumann problem for special Lagrangian type equations with critical and supercritical phases. These equations naturally generalize the special Lagrangian equation and…
Targeted Activation Penalties Help CNNs Ignore Spurious Signals
Dekai Zhang, Matthew Williams, Francesca Toni
Neural networks (NNs) can learn to rely on spurious signals in the training data, leading to poor generalisation. Recent methods tackle this problem by training NNs with additional…