Principled Curriculum Learning using Parameter Continuation Methods
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911082227957760 |
|---|---|
| author | Pathak, Harsh Nilesh Paffenroth, Randy |
| author_facet | Pathak, Harsh Nilesh Paffenroth, Randy |
| contents | In this work, we propose a parameter continuation method for the optimization of neural networks. There is a close connection between parameter continuation, homotopies, and curriculum learning. The methods we propose here are theoretically justified and practically effective for several problems in deep neural networks. In particular, we demonstrate better generalization performance than state-of-the-art optimization techniques such as ADAM for supervised and unsupervised learning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22089 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Principled Curriculum Learning using Parameter Continuation Methods Pathak, Harsh Nilesh Paffenroth, Randy Machine Learning Artificial Intelligence In this work, we propose a parameter continuation method for the optimization of neural networks. There is a close connection between parameter continuation, homotopies, and curriculum learning. The methods we propose here are theoretically justified and practically effective for several problems in deep neural networks. In particular, we demonstrate better generalization performance than state-of-the-art optimization techniques such as ADAM for supervised and unsupervised learning tasks. |
| title | Principled Curriculum Learning using Parameter Continuation Methods |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.22089 |