beta-vae: Learning basic visual concepts with a constrained variational framework. I Higgins, L Matthey, A Pal, CP Burgess, X Glorot, MM Botvinick, ...
ICLR (Poster) 3, 2017
5783 2017 Understanding disentangling in CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
1298 2018 Darla: Improving zero-shot transfer in reinforcement learning I Higgins, A Pal, A Rusu, L Matthey, C Burgess, A Pritzel, M Botvinick, ...
International Conference on Machine Learning, 1480-1490, 2017
540 2017 Early visual concept learning with unsupervised deep learning I Higgins, L Matthey, X Glorot, A Pal, B Uria, C Blundell, S Mohamed, ...
arXiv preprint arXiv:1606.05579, 2016
208 2016 International Conference on Learning Representations I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed, ...
ICLR 2017, Toulon, France, 2017
197 2017 Scan: Learning hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Bosnjak, ...
arXiv preprint arXiv:1707.03389, 2017
147 2017 Smaug: Fixing failure modes of preference optimisation with dpo-positive A Pal, D Karkhanis, S Dooley, M Roberts, S Naidu, C White
arXiv preprint arXiv:2402.13228, 2024
90 2024 Understanding disentangling in β CP Burgess, I Higgins, A Pal, L Matthey, N Watters, G Desjardins, ...
arXiv preprint arXiv:1804.03599, 2018
58 2018 Lerchner I Higgins, L Matthey, A Pal, C Burgess, X Glorot, M Botvinick, S Mohamed
A. betavae: Learning basic visual concepts with a constrained variational …, 2016
53 2016 Livebench: A challenging, contamination-free llm benchmark C White, S Dooley, M Roberts, A Pal, B Feuer, S Jain, R Shwartz-Ziv, ...
arXiv preprint arXiv:2406.19314, 2024
42 2024 Giraffe: Adventures in expanding context lengths in llms A Pal, D Karkhanis, M Roberts, S Dooley, A Sundararajan, S Naidu
arXiv preprint arXiv:2308.10882, 2023
37 2023 Scan: learning abstract hierarchical compositional visual concepts I Higgins, N Sonnerat, L Matthey, A Pal, CP Burgess, M Botvinick, ...
arXiv preprint arXiv:1707.03389, 2017
29 2017 Large Language Models Must Be Taught to Know What They Don't Know S Kapoor, N Gruver, M Roberts, K Collins, A Pal, U Bhatt, A Weller, ...
arXiv preprint arXiv:2406.08391, 2024
14 2024 Learning visual concepts using neural networks A Lerchner, I Higgins, N Sonnerat, AT Pal, D Hassabis, ...
US Patent 11,354,823, 2022
13 2022 Training variational autoencoders to generate disentangled latent factors L Matthey-de-l'Endroit, AT Pal, S Mohamed, X Glorot, I Higgins, ...
US Patent 10,373,055, 2019
13 2019 Calibration-Tuning: Teaching Large Language Models to Know What They Don’t Know S Kapoor, N Gruver, M Roberts, A Pal, S Dooley, M Goldblum, A Wilson
Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024 …, 2024
9 2024 vTune: Verifiable Fine-Tuning for LLMs Through Backdooring E Zhang, A Pal, A Potti, M Goldblum
arXiv preprint arXiv:2411.06611, 2024
2024 Learning visual concepts using neural networks A Lerchner, I Higgins, N Sonnerat, AT Pal, D Hassabis, ...
US Patent 11,769,057, 2023
2023 Training variational autoencoders to generate disentangled latent factors L Matthey-de-l'Endroit, AT Pal, S Mohamed, X Glorot, I Higgins, ...
US Patent 10,643,131, 2020
2020 