no code implementations • 22 Jan 2024 • Aissatou Diallo, Antonis Bikakis, Luke Dickens, Anthony Hunter, Rob Miller
We iteratively learn the graph structure and the parameters of a $\mathsf{GNN}$ encoding the texts (text-to-graph) one sequence at a time while providing the supervision by decoding the graph into text (graph-to-text) and comparing the generated text to the input.
no code implementations • 12 Jan 2024 • Aissatou Diallo, Antonis Bikakis, Luke Dickens, Anthony Hunter, Rob Miller
Decoding the core of procedural texts, exemplified by cooking recipes, is crucial for intelligent reasoning and instruction automation.
no code implementations • 15 Jun 2023 • Antonis Bikakis, Aissatou Diallo, Luke Dickens, Anthony Hunter, Rob Miller
Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework.
no code implementations • 21 Jun 2022 • Jeya Vikranth Jeyakumar, Luke Dickens, Luis Garcia, Yu-Hsi Cheng, Diego Ramirez Echavarria, Joseph Noor, Alessandra Russo, Lance Kaplan, Erik Blasch, Mani Srivastava
CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts.
no code implementations • 29 Sep 2021 • Harald Stromfelt, Luke Dickens, Artur Garcez, Alessandra Russo
Human defined concepts are inherently transferable, but it is not clear under what conditions they can be modelled effectively by non-symbolic artificial learners.
no code implementations • 17 Sep 2021 • Antonis Bikakis, Luke Dickens, Anthony Hunter, Rob Miller
Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY.
no code implementations • 13 Nov 2020 • Harald Strömfelt, Luke Dickens, Artur d'Avila Garcez, Alessandra Russo
We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties.
no code implementations • 5 Oct 2020 • Diego Ramirez-Echavarria, Antonis Bikakis, Luke Dickens, Rob Miller, Andreas Vlachidis
This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data.
no code implementations • 7 Mar 2019 • Ekaterina Abramova, Luke Dickens, Daniel Kuhn, Aldo Faisal
We show that a small number of locally optimal linear controllers are able to solve global nonlinear control problems with unknown dynamics when combined with a reinforcement learner in this hierarchical framework.
no code implementations • 20 Mar 2017 • Fabio Aurelio D'Asaro, Antonis Bikakis, Luke Dickens, Rob Miller
We also describe an ASP implementation of PEC and show the sense in which this is sound and complete.