3 citations · 9 across the 5 of their papers we have counts for
7 papers · 1 filter
One-shot Learning for Temporal Knowledge Graphs
Mehrnoosh Mirtaheri, Mohammad Rostami, Xiang Ren +2
Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times. This obs…
Unsupervised Model Adaptation for Continual Semantic Segmentation
Serban Stan, Mohammad Rostami
We develop an algorithm for adapting a semantic segmentation model that is trained using a labeled source domain to generalize well in an unlabeled target domain. A similar problem…
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment
Alex Gabourie, Mohammad Rostami, Philip Pope +2
We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the…
Generative Continual Concept Learning
Mohammad Rostami, Soheil Kolouri, James McClelland +1
After learning a concept, humans are also able to continually generalize their learned concepts to new domains by observing only a few labeled instances without any interference wi…
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly
Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspir…
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Phillip Pope, Soheil Kolouri, Mohammad Rostrami +2
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will i…