Staff profile

I am a PhD student in Computer Science at Durham University. My research is in the field of deep learning with a focus on unsupervised learning, generative modelling, probabilistic modelling, and machine reasoning.

• Deep Learning
• Generative Models
• Machine Reasoning

Conference Paper

• Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. In ICCV '23: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. https://doi.org/10.1109/ICCV51070.2023.01341
• Bond-Taylor, S., Hessey, P., Sasaki, H., Breckon, T., & Willcocks, C. (2022). Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes. In ECCV 2022: Computer Vision – ECCV 2022 (170-188)
• Corona-Figueroa, A., Frawley, J., Bond-Taylor, S., Bethapudi, S., Shum, H. P., & Willcocks, C. G. (2022). MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray. . https://doi.org/10.1109/embc48229.2022.9871757
• Bond-Taylor, S., & Willcocks, C. G. (2021). Gradient Origin Networks.
• Leach, A., Rudden, L. S., Bond-Taylor, S., Brigham, J. C., Degiacomi, M. T., & Willcocks, C. G. (2020). Shape tracing: An extension of sphere tracing for 3D non-convex collision in protein docking. In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE) (49-52). https://doi.org/10.1109/bibe50027.2020.00016

Journal Article

• Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021). Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/tpami.2021.3116668

Other (Print)

• Bond-Taylor, S., & Willcocks, C. G. (in press). ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States. In The Twelfth International Conference on Learning Representations