Minyoung Kim

CoRR, 2024

A Hierarchical Bayesian Model for Few-Shot Meta Learning.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

BayesDLL: Bayesian Deep Learning Library.

[BibT_eX]

[DOI]

CoRR, 2023

A Hierarchical Bayesian Model for Deep Few-Shot Meta Learning.

[BibT_eX]

[DOI]

CoRR, 2023

FedHB: Hierarchical Bayesian Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

FedL2P: Federated Learning to Personalize.

[BibT_eX]

[DOI]

Ferenc Huszar

Nicholas D. Lane

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

BayesTune: Bayesian Sparse Deep Model Fine-tuning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach.

[BibT_eX]

[DOI]

Da Li

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Fisher SAM: Information Geometry and Sharpness Aware Minimisation.

[BibT_eX]

[DOI]

Da Li

Shell Xu Hu

Proceedings of the International Conference on Machine Learning, 2022

Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Variational Continual Proxy-Anchor for Deep Metric Learning.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Gaussian Process Meta Few-shot Classifier Learning via Linear Discriminant Laplace Approximation.

[BibT_eX]

[DOI]

CoRR, 2021

Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach.

[BibT_eX]

[DOI]

CoRR, 2021

Learning Disentangled Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach.

[BibT_eX]

[DOI]

Ricardo Guerrero

Proceedings of the MM '21: ACM Multimedia Conference, Virtual Event, China, October 20, 2021

2020

Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach.

[BibT_eX]

[DOI]

Ricardo Guerrero

CoRR, 2020

Recursive Inference for Variational Autoencoders.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

2019

Efficient Deep Gaussian Process Models for Variable-Sized Input.

[BibT_eX]

[DOI]

CoRR, 2019

Relevance Factor VAE: Learning and Identifying Disentangled Factors.

[BibT_eX]

[DOI]

CoRR, 2019

Efficient Deep Gaussian Process Models for Variable-Sized Inputs.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2019

Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Variational Inference for Gaussian Process Models for Survival Analysis.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2011

Central Subspace Dimensionality Reduction Using Covariance Operators.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2011

Sequence classification via large margin hidden Markov models.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., 2011

2010

Hidden Conditional Ordinal Random Fields for Sequence Classification.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Structured Output Ordinal Regression for Dynamic Facial Emotion Intensity Prediction.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision, 2010

2009

Discriminative Learning for Dynamic State Prediction.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2008

Dimensionality reduction using covariance operator inverse regression.

[BibT_eX]

[DOI]

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

Face tracking and recognition with visual constraints in real-world videos.

[BibT_eX]

[DOI]

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2007

A recursive method for discriminative mixture learning.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning, 2007

Conditional State Space Models for Discriminative Motion Estimation.

[BibT_eX]

[DOI]

Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

Discriminative Learning of Dynamical Systems for Motion Tracking.

[BibT_eX]

[DOI]

Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 2007

2006

Discriminative Learning of Mixture of Bayesian Network Classifiers for Sequence Classification.

[BibT_eX]

[DOI]