4 citations · 7 across the 6 of their papers we have counts for
6 papers
Censoring-Aware In-Context Learning for Generalized Supplier Lead Time Estimation in Supply Chain Planning
Christopher Wang, Sebastien Ouellet, Behrouz Haji Soleimani +1
Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk management. However, many industrial lead time da…
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…
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…
Efficient Neural Task Adaptation by Maximum Entropy Initialization
Farshid Varno, Behrouz Haji Soleimani, Marzie Saghayi +2
Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples. Prevailing fine-tuning methods could potential…