3 papers
cs.LG2026
Cross-Attentive Bayesian Low-Rank Adaptation for Multimodal Uncertainty Estimation
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of frozen language models, but most PEFT methods remain deterministic or unimodal, limiting their reliability in…
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…