62 citations · 145 across the 34 of their papers we have counts for
40 papers
PhysVid: Physics Aware Local Conditioning for Generative Video Models
Saurabh Pathak, Elahe Arani, Mykola Pechenizkiy +1
Generative video models achieve high visual fidelity but often violate basic physical principles, limiting reliability in real-world settings. Prior attempts to inject physics rely…
BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
Kishaan Jeeveswaran, Prashant Bhat, Bahram Zonooz +1
The ability of deep neural networks to continually learn and adapt to a sequence of tasks has remained challenging due to catastrophic forgetting of previously learned tasks. Human…
Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal
Naresh Kumar Gurulingan, Bahram Zonooz, Elahe Arani
Multi-task learning has the potential to improve generalization by maximizing positive transfer between tasks while reducing task interference. Fully achieving this potential is hi…
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Humans excel at continually acquiring, consolidating, and retaining information from an ever-changing environment, whereas artificial neural networks (ANNs) exhibit catastrophic fo…
LSFSL: Leveraging Shape Information in Few-shot Learning
Deepan Chakravarthi Padmanabhan, Shruthi Gowda, Elahe Arani +1
Few-shot learning (FSL) techniques seek to learn the underlying patterns in data using fewer samples, analogous to how humans learn from limited experience. In this limited-data sc…
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks
Vijaya Raghavan T. Ramkumar, Elahe Arani, Bahram Zonooz
Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in c…