2 citations · 2 across the 5 of their papers we have counts for
6 papers
Fractional State Space Transition for Long Sequence Modeling
Ivan Kobyzev, Abbas Ghaddar, Ali Nasiri-Sarvi +2
State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for long-context performance. Mos…
MonoLoss: A Training Objective for Interpretable Monosemantic Representations
Ali Nasiri-Sarvi, Anh Tien Nguyen, Hassan Rivaz +2
Sparse autoencoders (SAEs) decompose polysemantic neural representations, where neurons respond to multiple unrelated concepts, into monosemantic features that capture single, inte…
SPARC: Concept-Aligned Sparse Autoencoders for Cross-Model and Cross-Modal Interpretability
Ali Nasiri-Sarvi, Hassan Rivaz, Mahdi S. Hosseini
Understanding how different AI models encode the same high-level concepts, such as objects or attributes, remains challenging because each model typically produces its own isolated…
Ultrasound Image Generation using Latent Diffusion Models
Benoit Freiche, Anthony El-Khoury, Ali Nasiri-Sarvi +5
Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potenti…
Vision Mamba for Classification of Breast Ultrasound Images
Ali Nasiri-Sarvi, Mahdi S. Hosseini, Hassan Rivaz
Mamba-based models, VMamba and Vim, are a recent family of vision encoders that offer promising performance improvements in many computer vision tasks. This paper compares Mamba-ba…
Vim4Path: Self-Supervised Vision Mamba for Histopathology Images
Ali Nasiri-Sarvi, Vincent Quoc-Huy Trinh, Hassan Rivaz +1
Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the…