7 papers
LoRSA: Toward Generalizable Parameter-Efficient Fine-Tuning for Biomedical Downstream Tasks
Saed Moradi, Benyamin Ghojogh, M. Hadi Sepanj +2
Parameter-efficient fine-tuning enables the adaptation of vision foundation models to biomedical tasks under limited computational resources, but a single low-rank update can const…
Self-Supervised Learning by Curvature Alignment
Benyamin Ghojogh, M. Hadi Sepanj, Paul Fieguth
Self-supervised learning (SSL) has recently advanced through non-contrastive methods that couple an invariance term with variance, covariance, or redundancy-reduction penalties. Wh…
Kernel VICReg for Self-Supervised Learning in Reproducing Kernel Hilbert Space
M. Hadi Sepanj, Benyamin Ghojogh, Saed Moradi +1
Self-supervised learning (SSL) has emerged as a powerful paradigm for representation learning by optimizing geometric objectives, such as invariance to augmentations, variance pres…
On the Relation of State Space Models and Hidden Markov Models
Aydin Ghojogh, M. Hadi Sepanj, Benyamin Ghojogh
State Space Models (SSMs) and Hidden Markov Models (HMMs) are foundational frameworks for modeling sequential data with latent variables and are widely used in signal processing, c…
Self-Supervised Learning Using Nonlinear Dependence
M. Hadi Sepanj, Benyamin Ghojogh, Paul Fieguth
Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily…
RadarSeq: A Temporal Vision Framework for User Churn Prediction via Radar Chart Sequences
Sina Najafi, M. Hadi Sepanj, Fahimeh Jafari
Predicting user churn in non-subscription gig platforms, where disengagement is implicit, poses unique challenges due to the absence of explicit labels and the dynamic nature of us…