| Generative adversarial networks: An overview A Creswell, T White, V Dumoulin, K Arulkumaran, B Sengupta, ... IEEE signal processing magazine 35 (1), 53-65, 2018 | 4151 | 2018 |
| Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 975 | 2021 |
| Inverting the generator of a generative adversarial network A Creswell, AA Bharath IEEE transactions on neural networks and learning systems 30 (7), 1967-1974, 2018 | 395 | 2018 |
| Selection-inference: Exploiting large language models for interpretable logical reasoning A Creswell, M Shanahan, I Higgins arXiv preprint arXiv:2205.09712, 2022 | 281 | 2022 |
| Can language models learn from explanations in context? AK Lampinen, I Dasgupta, SCY Chan, K Matthewson, MH Tessler, ... arXiv preprint arXiv:2204.02329, 2022 | 245 | 2022 |
| Denoising adversarial autoencoders A Creswell, AA Bharath IEEE transactions on neural networks and learning systems 30 (4), 968-984, 2018 | 162 | 2018 |
| Language models show human-like content effects on reasoning I Dasgupta, AK Lampinen, SCY Chan, A Creswell, D Kumaran, ... arXiv preprint arXiv:2207.07051, 2022 | 153 | 2022 |
| Solving math word problems with process-and outcome-based feedback J Uesato, N Kushman, R Kumar, F Song, N Siegel, L Wang, A Creswell, ... arXiv preprint arXiv:2211.14275, 2022 | 123 | 2022 |
| Faithful reasoning using large language models A Creswell, M Shanahan arXiv preprint arXiv:2208.14271, 2022 | 114 | 2022 |
| On denoising autoencoders trained to minimise binary cross-entropy A Creswell, K Arulkumaran, AA Bharath arXiv preprint arXiv:1708.08487, 2017 | 105 | 2017 |
| Cyprien de Masson d’Autume JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... | 95 | 2021 |
| An explicitly relational neural network architecture M Shanahan, K Nikiforou, A Creswell, C Kaplanis, D Barrett, M Garnelo International Conference on Machine Learning, 8593-8603, 2020 | 76 | 2020 |
| Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition R Kabra, D Zoran, G Erdogan, L Matthey, A Creswell, M Botvinick, ... Advances in Neural Information Processing Systems 34, 20146-20159, 2021 | 73 | 2021 |
| Adversarial training for sketch retrieval A Creswell, AA Bharath European Conference on Computer Vision, 798-809, 2016 | 71 | 2016 |
| Cyprien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, HF Song, J Aslanides, ... Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William S. Isaac …, 2021 | 66 | 2021 |
| Adversarial information factorization A Creswell, Y Mohamied, B Sengupta, AA Bharath arXiv preprint arXiv:1711.05175, 2017 | 52 | 2017 |
| Image synthesis with a convolutional capsule generative adversarial network C Bass, T Dai, B Billot, K Arulkumaran, A Creswell, C Clopath, V De Paola, ... | 31 | 2019 |
| Unsupervised object-based transition models for 3d partially observable environments A Creswell, R Kabra, C Burgess, M Shanahan Advances in neural information processing systems 34, 27344-27355, 2021 | 25 | 2021 |
| Improving sampling from generative autoencoders with markov chains A Creswell, K Arulkumaran, AA Bharath arXiv preprint arXiv:1610.09296, 2016 | 21* | 2016 |
| Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data A Creswell, A Pouplin, AA Bharath IET Computer Vision 12 (8), 1105-1111, 2018 | 20 | 2018 |
