23 citations · 25 across the 5 of their papers we have counts for
5 papers · 1 filter
GPrune-LLM: Generalization-Aware Structured Pruning for Large Language Models
Xiaoyun Liu, Divya Saxena, Jiannong Cao +3
Structured pruning is widely applied to compress large language models (LLMs), but its performance depends heavily on how neuron importance is estimated. Most existing methods rely…
Overcoming Growth-Induced Forgetting in Task-Agnostic Continual Learning
Yuqing Zhao, Jiannong Cao, Divya Saxena +4
In continual learning (CL), model growth enhances adaptability to new data. However, when model growth is applied improperly, especially in task-agnostic CL, where the entire grown…
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning
Changlin Song, Divya Saxena, Jiannong Cao +1
Federated Learning (FL) is a novel approach that allows for collaborative machine learning while preserving data privacy by leveraging models trained on decentralized devices. Howe…
MGAS: Multi-Granularity Architecture Search for Trade-Off Between Model Effectiveness and Efficiency
Xiaoyun Liu, Divya Saxena, Jiannong Cao +2
Neural architecture search (NAS) has gained significant traction in automating the design of neural networks. To reduce search time, differentiable architecture search (DAS) refram…
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets
Yuqing Zhao, Divya Saxena, Jiannong Cao
Managing heterogeneous datasets that vary in complexity, size, and similarity in continual learning presents a significant challenge. Task-agnostic continual learning is necessary…