papers

Publications (52)

cs.AI2021

Formalising Concepts as Grounded Abstractions

Stephen Clark, Alexander Lerchner, Tamara von Glehn +4

The notion of concept has been studied for centuries, by philosophers, linguists, cognitive scientists, and researchers in artificial intelligence (Margolis & Laurence, 1999). Ther…

cs.IR2020

Learning to Personalize for Web Search Sessions

Saad Aloteibi, Stephen Clark

The task of session search focuses on using interaction data to improve relevance for the user's next query at the session level. In this paper, we formulate session search as a pe…

cs.AI2016

Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research

Douwe Kiela, Luana Bulat, Anita L. Vero +1

Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences. The field of Artifical In…

cs.CL2019

Neural Generative Rhetorical Structure Parsing

Amandla Mabona, Laura Rimell, Stephen Clark +1

Rhetorical structure trees have been shown to be useful for several document-level tasks including summarization and document classification. Previous approaches to RST parsing hav…

cs.CL2017

Jointly Learning Sentence Embeddings and Syntax with Unsupervised Tree-LSTMs

Jean Maillard, Stephen Clark, Dani Yogatama

We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied alon…

cs.CL2010

Concrete Sentence Spaces for Compositional Distributional Models of Meaning

Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen Clark +2

Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a compositional model of meaning for distributional semantics, in which each word in a sentence has a meaning vec…

hep-ph2006

Interactions of Q-balls and matter

Stephen Clark

We know from previous work \cite{clark} that non topological solitons, Q balls, evaporate into fermions. All the constructions we used to find evaporation rate were dased on the fa…

math.SP2008

Borg-Marchenko-type Uniqueness Results for CMV Operators

Stephen Clark, Fritz Gesztesy, Maxim Zinchenko

We prove local and global versions of Borg-Marchenko-type uniqueness theorems for half-lattice and full-lattice CMV operators (CMV for Cantero, Moral, and Velazquez \cite{CMV03}).…

cs.CL2013

A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure

Stephen Clark, Bob Coecke, Edward Grefenstette +2

We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation. In fact, this protocol was the main sour…

cs.AI2024

Towards Compositional Interpretability for XAI

Sean Tull, Robin Lorenz, Stephen Clark +2

Artificial intelligence (AI) is currently based largely on black-box machine learning models which lack interpretability. The field of eXplainable AI (XAI) strives to address this…

cs.CL2019

Understanding Early Word Learning in Situated Artificial Agents

Felix Hill, Stephen Clark, Karl Moritz Hermann +1

Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-…

math.DS1999

Stability radius and internal versus external stability in Banach spaces: an evolution semigroup approach

Stephen Clark, Yuri Latushkin, Stephen J. Montgomery-Smith +1

In this paper the theory of evolution semigroups is developed and used to provide a framework to study the stability of general linear control systems. These include time-varying s…

cs.AI2020

Probing Emergent Semantics in Predictive Agents via Question Answering

Abhishek Das, Federico Carnevale, Hamza Merzic +8

Recent work has shown how predictive modeling can endow agents with rich knowledge of their surroundings, improving their ability to act in complex environments. We propose questio…

quant-ph2023

Peptide Binding Classification on Quantum Computers

Charles London, Douglas Brown, Wenduan Xu +5

We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology. By constructing quantum models based on parameterised quantum…

quant-ph2002

Coupling of effective one-dimensional two-level atoms to squeezed light

Stephen Clark, Scott Parkins

A cavity QED system is analyzed which duplicates the dynamics of a two-level atom in free space interacting exclusively with broadband squeezed light. We consider atoms in a three…

cs.LG2021

Imitating Interactive Intelligence

Josh Abramson, Arun Ahuja, Iain Barr +26

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through…

cs.CL2019

Scalable Syntax-Aware Language Models Using Knowledge Distillation

Adhiguna Kuncoro, Chris Dyer, Laura Rimell +2

Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential langua…

cs.CL2021

Something Old, Something New: Grammar-based CCG Parsing with Transformer Models

Stephen Clark

This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead…

cs.CL2014

Using Sentence Plausibility to Learn the Semantics of Transitive Verbs

Tamara Polajnar, Laura Rimell, Stephen Clark

The functional approach to compositional distributional semantics considers transitive verbs to be linear maps that transform the distributional vectors representing nouns into a v…

cs.CL2021

lambeq: An Efficient High-Level Python Library for Quantum NLP

Dimitri Kartsaklis, Ian Fan, Richie Yeung +7

We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes im…

cs.CL2014

The Frobenius anatomy of word meanings I: subject and object relative pronouns

Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke

This paper develops a compositional vector-based semantics of subject and object relative pronouns within a categorical framework. Frobenius algebras are used to formalise the oper…

math.SP2012

Boundary Data Maps and Krein's Resolvent Formula for Sturm-Liouville Operators on a Finite Interval

Stephen Clark, Fritz Gesztesy, Roger Nichols +1

We continue the study of boundary data maps, that is, generalizations of spectral parameter dependent Dirichlet-to-Neumann maps for (three-coefficient) Sturm-Liouville operators on…

hep-th2007

Q-Ball Condensation

Stephen Clark

Q-balls arise in particle theories with U(1) global symmetry. The coupling of the corresponding scalar field to fermions leads to Q-ball evaporation. In this paper we consider the…

cs.CL2020

Learning to Segment Actions from Observation and Narration

Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom +3

We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no acti…

cs.AI2020

Environmental drivers of systematicity and generalization in a situated agent

Felix Hill, Andrew Lampinen, Rosalia Schneider +4

The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here…

cs.CV2024

Enhancing Surgical Performance in Cardiothoracic Surgery with Innovations from Computer Vision and Artificial Intelligence: A Narrative Review

Merryn D. Constable, Hubert P. H. Shum, Stephen Clark

When technical requirements are high, and patient outcomes are critical, opportunities for monitoring and improving surgical skills via objective motion analysis feedback may be pa…

cs.AI2018

Emergent Communication through Negotiation

Kris Cao, Angeliki Lazaridou, Marc Lanctot +3

Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the eme…

quant-ph2024

Quixer: A Quantum Transformer Model

Nikhil Khatri, Gabriel Matos, Luuk Coopmans +1

Progress in the realisation of reliable large-scale quantum computers has motivated research into the design of quantum machine learning models. We present Quixer: a novel quantum…

q-bio.NC2023

Formalising and Learning a Quantum Model of Concepts

Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic +1

In this report we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned automatically from data.…

cs.CL2010

Mathematical Foundations for a Compositional Distributional Model of Meaning

Bob Coecke, Mehrnoosh Sadrzadeh, Stephen Clark

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, for…

quant-ph2026

Automated near-term quantum algorithm discovery for molecular ground states

Fabian Finger, Frederic Rapp, Pranav Kalidindi +10

Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform…

math.SP2015

Principal Solutions Revisited

Stephen Clark, Fritz Gesztesy, Roger Nichols

The main objective of this paper is to identify principal solutions associated with Sturm-Liouville operators on arbitrary open intervals , as introduce…

q-bio.NC2023

From Conceptual Spaces to Quantum Concepts: Formalising and Learning Structured Conceptual Models

Sean Tull, Razin A. Shaikh, Sara Sabrina Zemljic +1

In this article we present a new modelling framework for structured concepts using a category-theoretic generalisation of conceptual spaces, and show how the conceptual representat…

quant-ph2003

Unconditional preparation of entanglement between atoms in cascaded optical cavities

Stephen Clark, Amy Peng, Mile Gu +1

We propose a scheme to unconditionally entangle the internal states of atoms trapped in separate high finesse optical cavities. The scheme uses the technique of quantum reservoir e…

cs.CL2019

Factorising AMR generation through syntax

Kris Cao, Stephen Clark

Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not constrained by the semantic graph. To explicitly account for…

hep-ph2006

Particle production from Q-balls

Stephen Clark

Non topological solitons, Q-balls can arise in many particle theories with U(1) global symmetries. As was shown by Cohen et al. \cite{Qballscohen}, if the corresponding scalar fiel…

cs.CL2014

Learning Type-Driven Tensor-Based Meaning Representations

Tamara Polajnar, Luana Fagarasan, Stephen Clark

This paper investigates the learning of 3rd-order tensors representing the semantics of transitive verbs. The meaning representations are part of a type-driven tensor-based semanti…

cs.CL2024

Learning Complex Word Embeddings in Classical and Quantum Spaces

Carys Harvey, Stephen Clark, Douglas Brown +1

We present a variety of methods for training complex-valued word embeddings, based on the classical Skip-gram model, with a straightforward adaptation simply replacing the real-val…

cs.CL2017

Latent Variable Dialogue Models and their Diversity

Kris Cao, Stephen Clark

We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the `boring output' issue of deterministic dialo…

cs.CL2014

The Frobenius anatomy of word meanings II: possessive relative pronouns

Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke

Within the categorical compositional distributional model of meaning, we provide semantic interpretations for the subject and object roles of the possessive relative pronoun `whose…

quant-ph2026

Learning to Prepare Molecular Ground States with Transformer Models

Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit +14

Quantum state preparation is a key component of many quantum algorithms. Performing this step efficiently is essential for realizing practical quantum advantage in quantum chemistr…

math.SP2016

Characterization of self-adjoint extensions for discrete symplectic systems

Petr Zemánek, Stephen Clark

All self-adjoint extensions of minimal linear relation associated with the discrete symplectic system are characterized. Especially, for the scalar case on a finite discrete interv…

math.SP2010

Boundary Data Maps for Schrodinger Operators on a Compact Interval

Stephen Clark, Fritz Gesztesy, Marius Mitrea

We provide a systematic study of boundary data maps, that is, 2 \times 2 matrix-valued Dirichlet-to-Neumann and more generally, Robin-to-Robin maps, associated with one-dimensional…

math.SP2010

Weyl-Titchmarsh Theory and Borg-Marchenko-type Uniqueness Results for CMV Operators with Matrix-Valued Verblunsky Coefficients

Stephen Clark, Fritz Gesztesy, Maxim Zinchenko

We prove local and global versions of Borg-Marchenko-type uniqueness theorems for half-lattice and full-lattice CMV operators (CMV for Cantero, Moral, and Velazquez) with matrix-va…

math.SP2010

Minimal Rank Decoupling of Full-Lattice CMV Operators with Scalar- and Matrix-Valued Verblunsky Coefficients

Stephen Clark, Fritz Gesztesy, Maxim Zinchenko

Relations between half- and full-lattice CMV operators with scalar- and matrix-valued Verblunsky coefficients are investigated. In particular, the decoupling of full-lattice CMV op…

cs.AI2018

Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input

Angeliki Lazaridou, Karl Moritz Hermann, Karl Tuyls +1

The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent…

cs.LG2022

The Conceptual VAE

Razin A. Shaikh, Sara Sabrina Zemljic, Sean Tull +1

In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual d…

cs.CL2020

Grounded Language Learning Fast and Slow

Felix Hill, Olivier Tieleman, Tamara von Glehn +3

Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot…

cs.CL2018

Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

Jean Maillard, Stephen Clark

Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines…

math.SP2016

On discrete symplectic systems: Associated maximal and minimal linear relations and nonhomogeneous problems

Stephen Clark, Petr Zemánek

In this paper we characterize the definiteness of the discrete symplectic system, study a nonhomogeneous discrete symplectic system, and introduce the minimal and maximal linear re…

cs.LG2025

Property Classification of Vacation Rental Properties during Covid-19

Favour Yahdii Aghaebe, Dustin Foley, Eric Atwell +1

This study advocates for employing clustering techniques to classify vacation rental properties active during the Covid pandemic to identify inherent patterns and behaviours. The d…

physics.soc-ph2020

Who voted for a No Deal Brexit? A Composition Model of Great Britains 2019 European Parliamentary Elections

Stephen Clark

The purpose of this paper is to use the votes cast at the 2019 European elections held in United Kingdom to re-visit the analysis conducted subsequent to its 2016 European Union re…