2 citations · 4 across the 9 of their papers we have counts for
4 papers · 1 filter
NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Xiaohan Bi, Binhang Qi, Hailong Sun +3
With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse…
CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging
Zongzhen Yang, Binhang Qi, Hailong Sun +3
Model merging based on task vectors, i.e., the parameter differences between fine-tuned models and a shared base model, provides an efficient way to integrate multiple task-specifi…
Clustering Properties of Self-Supervised Learning
Xi Weng, Jianing An, Xudong Ma +5
Self-supervised learning (SSL) methods via joint embedding architectures have proven remarkably effective at capturing semantically rich representations with strong clustering prop…
Modularizing while Training: A New Paradigm for Modularizing DNN Models
Binhang Qi, Hailong Sun, Hongyu Zhang +2
Deep neural network (DNN) models have become increasingly crucial components in intelligent software systems. However, training a DNN model is typically expensive in terms of both…