Rethinking Semi-Supervised Learning in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; N. Siddharth 001; Tom Rainforth (CoRR, 2020)

Capturing Label Characteristics in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; Siddharth Narayanaswamy; Tom Rainforth (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)

Sample-Dependent Adaptive Temperature Scaling for Improved Calibration. - Tom Joy; Francesco Pinto; Ser-Nam Lim; Philip H. S. Torr; Puneet K. Dokania (AAAI, 2023)

Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration. - Kemal Oksuz; Tom Joy; Puneet K. Dokania (CVPR, 2023)

What Makes and Breaks Safety Fine-tuning? A Mechanistic Study. - Samyak Jain; Ekdeep Singh Lubana; Kemal Oksuz; Tom Joy; Philip Torr 001; Amartya Sanyal; Puneet K. Dokania (NeurIPS, 2024)

MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection. - Kemal Oksuz; Selim Kuzucu; Tom Joy; Puneet K. Dokania (Trans. Mach. Learn. Res., 2024)