Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.01238 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916465950588928 |
|---|---|
| author | Lo, Andy |
| author_facet | Lo, Andy |
| contents | Dropout is a common regularisation technique in deep learning that improves generalisation. Even though it introduces sparsity and thus potential for higher throughput, it usually cannot bring speed-ups on GPUs due to its unstructured nature. In this project, I experiment with SparseDrop, a structured, hardware-friendly variant of dropout that can exploit such sparsity. I provide a CUDA implementation of SparseDrop, achieving speed-ups against its dense counterpart even at low sparsity levels. The empirical results demonstrate that SparseDrop provides similar, or sometimes even better, regularisation properties as standard dropout. This suggests its potential as a drop-in replacement to standard dropout with faster training speeds. The source code is available at https://github.com/andylolu2/sparse-dropout |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01238 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Sparse Training with Structured Dropout Lo, Andy Machine Learning Dropout is a common regularisation technique in deep learning that improves generalisation. Even though it introduces sparsity and thus potential for higher throughput, it usually cannot bring speed-ups on GPUs due to its unstructured nature. In this project, I experiment with SparseDrop, a structured, hardware-friendly variant of dropout that can exploit such sparsity. I provide a CUDA implementation of SparseDrop, achieving speed-ups against its dense counterpart even at low sparsity levels. The empirical results demonstrate that SparseDrop provides similar, or sometimes even better, regularisation properties as standard dropout. This suggests its potential as a drop-in replacement to standard dropout with faster training speeds. The source code is available at https://github.com/andylolu2/sparse-dropout |
| title | Efficient Sparse Training with Structured Dropout |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.01238 |