4 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
Key features of mental illnesses are reflected in speech. Our research focuses on designing a multimodal deep learning structure that automatically extracts salient features from r…