Experimental and theoretical techniques for quantum-enhanced metrology and optical quantum information processing. - Peter Conway Humphreys (2015)

Deep Learning without Weight Transport. - Mohamed Akrout; Collin Wilson; Peter Conway Humphreys; Timothy P. Lillicrap; Douglas B. Tweed (NeurIPS, 2019)

Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning. - Josh Abramson; Arun Ahuja; Arthur Brussee; Federico Carnevale; Mary Cassin; Felix Fischer 004; Petko Georgiev; Alex Goldin; Tim Harley; Felix Hill; Peter Conway Humphreys; Alden Hung; et al. (CoRR, 2021)

Retrieval-Augmented Reinforcement Learning. - Anirudh Goyal; Abram L. Friesen; Andrea Banino; Theophane Weber; Nan Rosemary Ke; Adrià Puigdomènech Badia; Arthur Guez; Mehdi Mirza; Peter Conway Humphreys; Ksenia Konyushkova; Michal Valko; Simon Osindero; et al. (ICML, 2022)

A data-driven approach for learning to control computers. - Peter Conway Humphreys; David Raposo; Tobias Pohlen; Gregory Thornton; Rachita Chhaparia; Alistair Muldal; Josh Abramson; Petko Georgiev; Adam Santoro; Timothy P. Lillicrap (ICML, 2022)

Large-Scale Retrieval for Reinforcement Learning. - Peter Conway Humphreys; Arthur Guez; Olivier Tieleman; Laurent Sifre; Theophane Weber; Timothy P. Lillicrap (NeurIPS, 2022)

A data-driven approach for learning to control computers. - Peter Conway Humphreys; David Raposo; Toby Pohlen; Gregory Thornton; Rachita Chhaparia; Alistair Muldal; Josh Abramson; Petko Georgiev; Alex Goldin; Adam Santoro; Timothy P. Lillicrap (CoRR, 2022)

Mixture-of-Depths: Dynamically allocating compute in transformer-based language models. - David Raposo; Samuel Ritter; Blake A. Richards; Timothy P. Lillicrap; Peter Conway Humphreys; Adam Santoro (CoRR, 2024)