Adversarial Training for Sketch Retrieval. - Antonia Creswell; Anil Anthony Bharath (ECCV Workshops (1), 2016)

Improving Sampling from Generative Autoencoders with Markov Chains. - Kai Arulkumaran; Antonia Creswell; Anil Anthony Bharath (CoRR, 2016)

Task Specific Adversarial Cost Function. - Antonia Creswell; Anil A. Bharath (CoRR, 2016)

On denoising autoencoders trained to minimise binary cross-entropy. - Antonia Creswell; Kai Arulkumaran; Anil A. Bharath (CoRR, 2017)

LatentPoison - Adversarial Attacks On The Latent Space. - Antonia Creswell; Anil A. Bharath; Biswa Sengupta (CoRR, 2017)

Conditional Autoencoders with Adversarial Information Factorization. - Antonia Creswell; Anil A. Bharath; Biswa Sengupta (CoRR, 2017)

Inverting The Generator Of A Generative Adversarial Network (II). - Antonia Creswell; Anil A. Bharath (CoRR, 2018)

Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data. - Antonia Creswell; Alison Pouplin; Anil A. Bharath (IET Comput. Vis., 2018)

Generative Adversarial Networks: An Overview. - Antonia Creswell; Tom White; Vincent Dumoulin; Kai Arulkumaran; Biswa Sengupta; Anil A. Bharath (IEEE Signal Process. Mag., 2018)

Image Synthesis with a Convolutional Capsule Generative Adversarial Network. - Cher Bass; Tianhong Dai; Benjamin Billot; Kai Arulkumaran; Antonia Creswell; Claudia Clopath; Vincenzo De Paola; Anil Anthony Bharath (MIDL, 2019)

Denoising Adversarial Autoencoders. - Antonia Creswell; Anil Anthony Bharath (IEEE Trans. Neural Networks Learn. Syst., 2019)

Inverting the Generator of a Generative Adversarial Network. - Antonia Creswell; Anil Anthony Bharath (IEEE Trans. Neural Networks Learn. Syst., 2019)

An Explicitly Relational Neural Network Architecture. - Murray Shanahan; Kyriacos Nikiforou; Antonia Creswell; Christos Kaplanis; David G. T. Barrett; Marta Garnelo (ICML, 2020)

AlignNet: Unsupervised Entity Alignment. - Antonia Creswell; Kyriacos Nikiforou; Oriol Vinyals; Andre Saraiva 001; Rishabh Kabra; Loic Matthey; Chris Burgess 001; Malcolm Reynolds; Richard Tanburn; Marta Garnelo; Murray Shanahan (CoRR, 2020)

Unsupervised Object-Based Transition Models For 3D Partially Observable Environments. - Antonia Creswell; Rishabh Kabra; Christopher P. Burgess; Murray Shanahan (NeurIPS, 2021)

SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition. - Rishabh Kabra; Daniel Zoran; Goker Erdogan; Loic Matthey; Antonia Creswell; Matt M. Botvinick; Alexander Lerchner; Christopher P. Burgess (NeurIPS, 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - Jack W. Rae; Sebastian Borgeaud; Trevor Cai; Katie Millican; Jordan Hoffmann; H. Francis Song; John Aslanides; Sarah Henderson; Roman Ring; Susannah Young; Eliza Rutherford; Tom Hennigan; et al. (CoRR, 2021)

Can language models learn from explanations in context? - Andrew K. Lampinen; Ishita Dasgupta 001; Stephanie C. Y. Chan; Kory W. Mathewson; Michael Henry Tessler; Antonia Creswell; James L. McClelland; Jane Wang 001; Felix Hill (EMNLP (Findings), 2022)

Language models show human-like content effects on reasoning. - Ishita Dasgupta 001; Andrew K. Lampinen; Stephanie C. Y. Chan; Antonia Creswell; Dharshan Kumaran; James L. McClelland; Felix Hill (CoRR, 2022)

Faithful Reasoning Using Large Language Models. - Antonia Creswell; Murray Shanahan (CoRR, 2022)

Solving math word problems with process- and outcome-based feedback. - Jonathan Uesato; Nate Kushman; Ramana Kumar; H. Francis Song; Noah Y. Siegel; Lisa Wang; Antonia Creswell; Geoffrey Irving; Irina Higgins (CoRR, 2022)

Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. - Antonia Creswell; Murray Shanahan; Irina Higgins (ICLR, 2023)