3 papers
cs.LG2026
Cross-Modal Bayesian Low-Rank Adaptation for Uncertainty-Aware Multimodal Learning
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
Large pre-trained language models are increasingly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT), but existing PEFT methods are typically deterministic a…
cs.LG2026
Beyond Feature Fusion: Contextual Bayesian PEFT for Multimodal Uncertainty Estimation
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
We introduce CoCo-LoRA, a multimodal, uncertainty-aware parameter-efficient fine-tuning method for text prediction tasks accompanied by audio context. Existing PEFT approaches such…
cs.LG2026
Joint-Centric Dual Contrastive Alignment with Structure-Preserving and Information-Balanced Regularization
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
We propose HILBERT (HIerarchical Long-sequence Balanced Embedding with Reciprocal contrastive Training), a cross-attentive multimodal framework for learning document-level audio-te…