Action Anticipation with RBF Kernelized Feature Mapping RNN. - Yuge Shi; Basura Fernando; Richard Hartley 001 (ECCV (10), 2018)

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. - Yuge Shi; Siddharth Narayanaswamy; Brooks Paige; Philip H. S. Torr (NeurIPS, 2019)

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. - Yuge Shi; Brooks Paige; Philip H. S. Torr; N. Siddharth 001 (ICLR, 2021)

Learning Multimodal VAEs through Mutual Supervision. - Tom Joy; Yuge Shi; Philip H. S. Torr; Tom Rainforth; Sebastian M. Schmon; Siddharth Narayanaswamy (ICLR, 2022)

Gradient Matching for Domain Generalization. - Yuge Shi; Jeffrey Seely; Philip H. S. Torr; Siddharth Narayanaswamy; Awni Y. Hannun; Nicolas Usunier; Gabriel Synnaeve (ICLR, 2022)

Adversarial Masking for Self-Supervised Learning. - Yuge Shi; N. Siddharth 001; Philip H. S. Torr; Adam R. Kosiorek (ICML, 2022)

How robust are pre-trained models to distribution shift? - Yuge Shi; Imant Daunhawer; Julia E. Vogt; Philip H. S. Torr; Amartya Sanyal (CoRR, 2022)

How robust is unsupervised representation learning to distribution shift? - Yuge Shi; Imant Daunhawer; Julia E. Vogt; Philip H. S. Torr; Amartya Sanyal (ICLR, 2023)

Tuning Computer Vision Models With Task Rewards. - André Susano Pinto; Alexander Kolesnikov 003; Yuge Shi; Lucas Beyer; Xiaohua Zhai (ICML, 2023)

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael D. Dennis; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (ICML, 2024)

Memory Consolidation Enables Long-Context Video Understanding. - Ivana Balazevic; Yuge Shi; Pinelopi Papalampidi; Rahma Chaabouni 001; Skanda Koppula; Olivier J. Hénaff (ICML, 2024)

Genie: Generative Interactive Environments. - Jake Bruce; Michael D. Dennis; Ashley Edwards; Jack Parker-Holder; Yuge Shi; Edward Hughes 001; Matthew Lai; Aditi Mavalankar; Richie Steigerwald; Chris Apps; Yusuf Aytar; Sarah Bechtle; et al. (ICML, 2024)

Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael Dennis 001; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (CoRR, 2024)