5 papers
From Isolation to Integration: Building an Adaptive Expert Forest for Pre-Trained Model-based Class-Incremental Learning
Ruiqi Liu, Boyu Diao, Hangda Liu +3
Class-Incremental Learning (CIL) requires models to learn new classes without forgetting old ones. A common method is to freeze a pre-trained model and train a new, lightweight ada…
Low-redundancy Distillation for Continual Learning
RuiQi Liu, Boyu Diao, Libo Huang +4
Continual learning (CL) aims to learn new tasks without erasing previous knowledge. However, current CL methods primarily emphasize improving accuracy while often neglecting traini…
A Nonlinear Hash-based Optimization Method for SpMV on GPUs
Chen Yan, Boyu Diao, Hangda Liu +2
Sparse matrix-vector multiplication (SpMV) is a fundamental operation with a wide range of applications in scientific computing and artificial intelligence. However, the large scal…
Efficient Continual Learning through Frequency Decomposition and Integration
Ruiqi Liu, Boyu Diao, Libo Huang +4
Continual learning (CL) aims to learn new tasks while retaining past knowledge, addressing the challenge of forgetting during task adaptation. Rehearsal-based methods, which replay…
Gensor: A Graph-based Construction Tensor Compilation Method for Deep Learning
Hangda Liu, Boyu Diao, Yu Yang +3
High-performance deep learning depends on efficient tensor programs. In recent years, automatic tensor program optimization, also known as tensor compilation, has emerged as the pr…