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cs.LG2026
Possibilistic Predictive Uncertainty for Deep Learning
Yao Ni, Jeremie Houssineau, Yew-Soon Ong +1
Deep neural networks achieve impressive results across diverse applications, yet their overconfidence on unseen inputs necessitates reliable epistemic uncertainty modeling. Existin…
cs.LG2024
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
Yao Ni, Shan Zhang, Piotr Koniusz
Parameter-Efficient Fine-Tuning (PEFT) effectively adapts pre-trained transformers to downstream tasks. However, the optimization of tasks performance often comes at the cost of ge…
cs.LG2024★ 1 cited
CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned Normalization
Yao Ni, Piotr Koniusz
Generative Adversarial Networks (GANs) significantly advanced image generation but their performance heavily depends on abundant training data. In scenarios with limited data, GANs…