Ondrej Bohdal

According to our database, Ondrej Bohdal authored at least 22 papers between 2020 and 2024.

Collaborative distances:

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

2024

Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks.

Martin Ferianc, Ondrej Bohdal, Timothy M. Hospedales, Miguel R. D. Rodrigues

Trans. Mach. Learn. Res., 2024

LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image Generation.

Donald Shenaj, Ondrej Bohdal, Mete Ozay, Pietro Zanuttigh, Umberto Michieli

CoRR, 2024

Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models.

Raman Dutt, Pedro Sanchez, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M. Hospedales

CoRR, 2024

Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted.

Ruchika Chavhan, Ondrej Bohdal, Yongshuo Zong, Da Li, Timothy M. Hospedales

CoRR, 2024

MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection.

Raman Dutt, Pedro Sanchez, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M. Hospedales

CoRR, 2024

VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal In-Context Learning.

Yongshuo Zong, Ondrej Bohdal, Timothy M. Hospedales

CoRR, 2024

Feed-Forward Latent Domain Adaptation.

Ondrej Bohdal, Da Li, Shell Xu Hu, Timothy M. Hospedales

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models.

Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, Timothy M. Hospedales

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

FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis.

Raman Dutt, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy M. Hospedales

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error.

Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales

Trans. Mach. Learn. Res., 2023

Label Calibration for Semantic Segmentation Under Domain Shift.

Ondrej Bohdal, Da Li, Timothy M. Hospedales

CoRR, 2023

Feed-Forward Source-Free Domain Adaptation via Class Prototypes.

Ondrej Bohdal, Da Li, Timothy M. Hospedales

CoRR, 2023

Impact of Noise on Calibration and Generalisation of Neural Networks.

Martin Ferianc, Ondrej Bohdal, Timothy M. Hospedales, Miguel R. D. Rodrigues

CoRR, 2023

Fairness in AI and Its Long-Term Implications on Society.

Ondrej Bohdal, Timothy M. Hospedales, Philip H. S. Torr, Fazl Barez

CoRR, 2023

PASHA: Efficient HPO and NAS with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Martin Wistuba, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn.

Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy M. Hospedales

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Feed-Forward Source-Free Latent Domain Adaptation via Cross-Attention.

Ondrej Bohdal, Da Li, Shell Xu Hu, Timothy M. Hospedales

CoRR, 2022

PASHA: Efficient HPO with Progressive Resource Allocation.

Ondrej Bohdal, Lukas Balles, Beyza Ermis, Cédric Archambeau, Giovanni Zappella

CoRR, 2022

2021

Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error.

Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales

CoRR, 2021

A Channel Coding Benchmark for Meta-Learning.

Rui Li, Ondrej Bohdal, Rajesh K. Mishra, Hyeji Kim, Da Li, Nicholas D. Lane, Timothy M. Hospedales

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization.

Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Flexible Dataset Distillation: Learn Labels Instead of Images.

Ondrej Bohdal, Yongxin Yang, Timothy M. Hospedales

CoRR, 2020