Yuge Shi

According to our database, Yuge Shi authored at least 13 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Open-Endedness is Essential for Artificial Superhuman Intelligence.

Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

CoRR, 2024

Genie: Generative Interactive Environments.

Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, Yusuf Aytar, Sarah Bechtle, Feryal M. P. Behbahani, Stephanie C. Y. Chan, Nicolas Heess, Lucy Gonzalez, Simon Osindero, Sherjil Ozair, Scott E. Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Memory Consolidation Enables Long-Context Video Understanding.

Ivana Balazevic, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence.

Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Tuning Computer Vision Models With Task Rewards.

André Susano Pinto, Alexander Kolesnikov, Yuge Shi, Lucas Beyer, Xiaohua Zhai

Proceedings of the International Conference on Machine Learning, 2023

How robust is unsupervised representation learning to distribution shift?

Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Adversarial Masking for Self-Supervised Learning.

Yuge Shi, N. Siddharth, Philip H. S. Torr, Adam R. Kosiorek

Proceedings of the International Conference on Machine Learning, 2022

Gradient Matching for Domain Generalization.

Yuge Shi, Jeffrey Seely, Philip H. S. Torr, Siddharth Narayanaswamy, Awni Y. Hannun, Nicolas Usunier, Gabriel Synnaeve

Proceedings of the Tenth International Conference on Learning Representations, 2022

Learning Multimodal VAEs through Mutual Supervision.

Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models.

Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth

Proceedings of the 9th International Conference on Learning Representations, 2021

2019

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models.

Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Action Anticipation with RBF Kernelized Feature Mapping RNN.

Yuge Shi, Basura Fernando, Richard Hartley

Proceedings of the Computer Vision - ECCV 2018, 2018