# Principled Curriculum Learning using Parameter Continuation Methods

arXiv: https://arxiv.org/abs/2507.22089 · HTML: https://mlsanity.com/paper/2507.22089/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions](https://mlsanity.com/paper/1901.01753/) (2019)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [Noisy Activation Functions](https://mlsanity.com/paper/1603.00391/) (2016)
- [Curriculum Dropout](https://mlsanity.com/paper/1703.06229/) (2017)
- [Efficient Super Resolution For Large-Scale Images Using Attentional GAN](https://mlsanity.com/paper/1812.04821/) (2018)
- [Rethinking the Relationship between Recurrent and Non-Recurrent Neural Networks: A Study in Sparsity](https://mlsanity.com/paper/2404.00880/) (2024)
- [Solo Connection: A Parameter Efficient Fine-Tuning Technique for Transformers](https://mlsanity.com/paper/2507.14353/) (2025)

## Built on by (most cited first, 1 of 1)

- [CalcSeg: Confidence-aware 3D Latent Context Curriculum Learning For Myocardial Scar Segmentation From Single-Stack LGE-CMRs](https://mlsanity.com/paper/2608.20305/) (2026)

## Abstract (arXiv)

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.