Outputs (3)

Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling (2024)
Presentation / Conference Contribution
Corona-Figueroa, A., Shum, H. P. H., & Willcocks, C. G. (2024, June). Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington

Self-Regulated Sample Diversity in Large Language Models (2024)
Presentation / Conference Contribution
Liu, M., Frawley, J., Wyer, S., Shum, H. P. H., Uckelman, S. L., Black, S., & Willcocks, C. G. (2024, June). Self-Regulated Sample Diversity in Large Language Models. Presented at NAACL 2024: 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Mexico City

∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States (2024)
Presentation / Conference Contribution
Bond-Taylor, S., & Willcocks, C. G. (2024, May). ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States. Presented at The International Conference on Learning Representations (ICLR), Vienna Austria

This paper introduces ∞-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space, which can model infinite resolution data. By training on randomly sampled subsets of coordinates and denoising content only at those location... Read More about ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States.