Computer Science > Machine Learning
arXiv:2409.04434 (cs)
[Submitted on 6 Sep 2024 (v1), last revised 27 Feb 2025 (this version, v3)]
Title:Accelerating Training with Neuron Interaction and Nowcasting Networks
View a PDF of the paper titled Accelerating Training with Neuron Interaction and Nowcasting Networks, by Boris Knyazev and 4 other authors
View PDFHTML (experimental)Abstract:Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However, learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.
| Comments: | ICLR 2025, code isthis https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2409.04434 [cs.LG] |
| (orarXiv:2409.04434v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2409.04434 arXiv-issued DOI via DataCite |
Submission history
From: Boris Knyazev [view email][v1] Fri, 6 Sep 2024 17:55:49 UTC (3,820 KB)
[v2] Thu, 3 Oct 2024 17:57:59 UTC (1,005 KB)
[v3] Thu, 27 Feb 2025 19:52:21 UTC (1,036 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled Accelerating Training with Neuron Interaction and Nowcasting Networks, by Boris Knyazev and 4 other authors
Current browse context:
cs.LG
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
IArxiv Recommender(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.