| Noisy networks for exploration M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ... arXiv preprint arXiv:1706.10295, 2017 | 644 | 2017 |
| Automated curriculum learning for neural networks A Graves, MG Bellemare, J Menick, R Munos, K Kavukcuoglu international conference on machine learning, 1311-1320, 2017 | 370 | 2017 |
| Rigging the lottery: Making all tickets winners U Evci, T Gale, J Menick, PS Castro, E Elsen International Conference on Machine Learning, 2943-2952, 2020 | 148 | 2020 |
| Generating high fidelity images with subscale pixel networks and multidimensional upscaling J Menick, N Kalchbrenner arXiv preprint arXiv:1812.01608, 2018 | 90 | 2018 |
| Multiplicative interactions and where to find them SM Jayakumar, WM Czarnecki, J Menick, J Schwarz, J Rae, S Osindero, ... | 54 | 2020 |
| 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 | 49 | 2021 |
| Multimodal few-shot learning with frozen language models M Tsimpoukelli, JL Menick, S Cabi, SM Eslami, O Vinyals, F Hill Advances in Neural Information Processing Systems 34, 200-212, 2021 | 38 | 2021 |
| Improving language models by retrieving from trillions of tokens S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ... arXiv preprint arXiv:2112.04426, 2021 | 14 | 2021 |
| Noisy networks for exploration. arXiv 2017 M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ... arXiv preprint arXiv:1706.10295, 0 | 14 | |
| Generating images with sparse representations C Nash, J Menick, S Dieleman, PW Battaglia arXiv preprint arXiv:2103.03841, 2021 | 12 | 2021 |
| Associative compression networks for representation learning A Graves, J Menick, A Oord arXiv preprint arXiv:1804.02476, 2018 | 8 | 2018 |
| Associative compression networks A Graves, J Menick, A van den Oord arXiv preprint arXiv:1804.02476, 2018 | 8 | 2018 |
| A practical sparse approximation for real time recurrent learning J Menick, E Elsen, U Evci, S Osindero, K Simonyan, A Graves arXiv preprint arXiv:2006.07232, 2020 | 7 | 2020 |
| Flamingo: a visual language model for few-shot learning JB Alayrac, J Donahue, P Luc, A Miech, I Barr, Y Hasson, K Lenc, ... arXiv preprint arXiv:2204.14198, 2022 | 2 | 2022 |
| Teaching language models to support answers with verified quotes J Menick, M Trebacz, V Mikulik, J Aslanides, F Song, M Chadwick, ... arXiv preprint arXiv:2203.11147, 2022 | 2 | 2022 |
| Practical real time recurrent learning with a sparse approximation J Menick, E Elsen, U Evci, S Osindero, K Simonyan, A Graves International Conference on Learning Representations, 2020 | 2 | 2020 |
| Data compression using jointly trained encoder, decoder, and prior neural networks JL Menick, AB Graves US Patent App. 16/767,010, 2021 | 1 | 2021 |
| Reduced computation real time recurrent learning JL Menick, EK Elsen, K Simonyan US Patent App. 17/169,083, 2021 | | 2021 |
| Training machine learning models using task selection policies to increase learning progress M Gendron-Bellemare, JL Menick, AB Graves, K Kavukcuoglu, R Munos US Patent App. 17/159,961, 2021 | | 2021 |
| Noisy neural network layers with noise parameters MG Azar, M Fortunato, B Piot, OC Pietquin, JL Menick, V Mnih, C Blundell, ... US Patent App. 17/020,248, 2021 | | 2021 |
