1 citations · 1 across the 9 of their papers we have counts for
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Sharpness-Aware Geometric Defense for Robust Out-Of-Distribution Detection
Jeng-Lin Li, Ming-Ching Chang, Wei-Chao Chen
Out-of-distribution (OOD) detection ensures safe and reliable model deployment. Contemporary OOD algorithms using geometry projection can detect OOD or adversarial samples from cle…
Continual Learning with Support Boundary Experience Blending
Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience replay (ER), stores past exemplars bu…
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection
Jeng-Lin Li, Ming-Ching Chang, Wei-Chao Chen
Detecting out-of-distribution (OOD) samples is crucial for trustworthy AI in real-world applications. Leveraging recent advances in representation learning and latent embeddings, V…
A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang +1
Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal cha…
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests
Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
The absence of training data and their distribution changes in federated learning (FL) can significantly undermine model performance, especially in cross-silo scenarios. To address…