5 citations · 21 across the 20 of their papers we have counts for
10 papers · 1 filter
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 Representation Learning to Aid Semi-Supervised Meta Learning
Atik Faysal, Mohammad Rostami, Huaxia Wang +2
Few-shot learning or meta-learning leverages the data scarcity problem in machine learning. Traditionally, training data requires a multitude of samples and labeling for supervised…
Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization
Zizhao Hu, Mohammad Rostami
Learning new tasks accumulatively without forgetting remains a critical challenge in continual learning. Generative experience replay addresses this challenge by synthesizing pseud…
Robust Internal Representations for Domain Generalization
Mohammad Rostami
This paper which is part of the New Faculty Highlights Invited Speaker Program of AAAI'23, serves as a comprehensive survey of my research in transfer learning by utilizing embeddi…
History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion
Mehrnoosh Mirtaheri, Mohammad Rostami, Aram Galstyan
Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received i…
Cognitively Inspired Cross-Modal Data Generation Using Diffusion Models
Zizhao Hu, Mohammad Rostami
Most existing cross-modal generative methods based on diffusion models use guidance to provide control over the latent space to enable conditional generation across different modal…