9 citations · 26 across the 22 of their papers we have counts for
12 papers · 1 filter
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
Mary Isabelle Wisell, Nicholas Jacobs, Aayush Manandhar +1
Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during paramete…
Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift
Aditi Naiknaware, Sanchit Singh, Hajar Homayouni +1
Open-world learning (OWL) requires models that can adapt to evolving environments while reliably detecting out-of-distribution (OOD) inputs. Existing approaches, such as SCONE, ach…
Information Consistent Pruning: How to Efficiently Search for Sparse Networks?
Soheil Gharatappeh, Salimeh Yasaei Sekeh
Iterative magnitude pruning methods (IMPs), proven to be successful in reducing the number of insignificant nodes in over-parameterized deep neural networks (DNNs), have been getti…
Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts
Mary Isabelle Wisell, Salimeh Yasaei Sekeh
Sparse deep neural networks (DNNs) excel in real-world applications like robotics and computer vision, by reducing computational demands that hinder usability. However, recent stud…
Robust Subgraph Learning by Monitoring Early Training Representations
Sepideh Neshatfar, Salimeh Yasaei Sekeh
Graph neural networks (GNNs) have attracted significant attention for their outstanding performance in graph learning and node classification tasks. However, their vulnerability to…
Investigating the Impact of Weight Sharing Decisions on Knowledge Transfer in Continual Learning
Josh Andle, Ali Payani, Salimeh Yasaei-Sekeh
Continual Learning (CL) has generated attention as a method of avoiding Catastrophic Forgetting (CF) in the sequential training of neural networks, improving network efficiency and…