1 citations · 2 across the 9 of their papers we have counts for
9 papers
Hybrid Learners Do Not Forget: A Brain-Inspired Neuro-Symbolic Approach to Continual Learning
Amin Banayeeanzade, Mohammad Rostami
Continual learning is crucial for creating AI agents that can learn and improve themselves autonomously. A primary challenge in continual learning is to learn new tasks without los…
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup
Yasaman Saadati, Mohammad Rostami, M. Hadi Amini
Traditional Federated Learning (FL) methods encounter significant challenges when dealing with heterogeneous data and providing personalized solutions for non-IID scenarios. Person…
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
Mengxi Wu, Hao Huang, Yi Fang +1
Unsupervised Domain Adaptation (UDA) is crucial for reducing the need for extensive manual data annotation when training deep networks on point cloud data. A significant challenge…
CRISPR: Ensemble Model
Mohammad Rostami, Amin Ghariyazi, Hamed Dashti +2
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a gene editing technology that has revolutionized the fields of biology and medicine. However, one of the chal…
Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay
Mohammad Rostami
We introduce an algorithm for tackling the problem of unsupervised domain adaptation (UDA) in continual learning (CL) scenarios. The primary objective is to maintain model generali…
Unsupervised Domain Adaptation Using Compact Internal Representations
Mohammad Rostami
A major technique for tackling unsupervised domain adaptation involves mapping data points from both the source and target domains into a shared embedding space. The mapping encode…
Unsupervised Federated Domain Adaptation for Segmentation of MRI Images
Navapat Nananukul, Hamid Soltanian-zadeh, Mohammad Rostami
Automatic semantic segmentation of magnetic resonance imaging (MRI) images using deep neural networks greatly assists in evaluating and planning treatments for various clinical app…
Online Continual Domain Adaptation for Semantic Image Segmentation Using Internal Representations
Serban Stan, Mohammad Rostami
Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training…
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation
Mohammad Rostami, Dayuan Jian, Ruitong Sun
Event-based cameras provide accurate and high temporal resolution measurements for performing computer vision tasks in challenging scenarios, such as high-dynamic range environment…