Neural scaling law
Neural scaling law - Wikipedia
A neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.
[2001.08361] Scaling Laws for Neural Language Models - arXiv
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the ...
Explaining neural scaling laws - PNAS
We present a theoretical framework for understanding scaling laws in trained deep neural networks. We identify four related scaling regimes.
Neural Scaling Laws For AGI : r/learnmachinelearning - Reddit
The argument in support of LLM that they will become truly intelligent is based on Neural Scaling Laws . In simple terms, the law basically ...
Explaining Neural Scaling Laws - Google Research
Abstract. The test loss of well-trained neural networks often follows precise power-law scaling relations with either the size of the training dataset or the ...
Explaining Neural Scaling Laws - arXiv
The population loss of trained deep neural networks often follows precise power-law scaling relations with either the size of the training dataset or the number ...
Demystify Transformers: A Guide to Scaling Laws - Medium
The scaling laws of LLMs shed light on how a model's quality evolves with increases in its size, training data volume, and computational resources.
A Dynamical Model of Neural Scaling Laws - Kempner Institute
A Dynamical Model of Neural Scaling Laws · which in most cases of interest will simply be · This model shows how the data structure and ...
Scaling Laws refer to the observed trend that the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as one varies ...
Scaling laws for neural language models - OpenAI
The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of ...
Scaling Laws from the Data Manifold Dimension
The scaling law can be explained if neural models are effectively just performing regression on a data manifold of intrinsic dimension d. This simple theory ...
Neural Scaling Laws. - YouTube
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beating power law scaling via data pruning
Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance ...
Geometric Scaling Law in Real Neuronal Networks
Abstract. We investigate the synapse-resolution connectomes of fruit flies across different developmental stages, revealing a consistent scaling ...
beating power law scaling via data pruning | Research - AI at Meta
Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, ...
[PDF] Explaining neural scaling laws - Semantic Scholar
This work investigates the origins behind “scaling laws” in linear random feature models and provides a taxonomy for different scaling regimes, ...
shehper/scaling_laws: An open-source implementation of Scaling ...
This repository contains an implementation of scaling laws as first found by Kaplan et al in Scaling Laws of Neural Language Models.
Beyond neural scaling laws | Proceedings of the 36th International ...
Here we focus on the scaling of error with dataset size and show how in theory we can break beyond power law scaling and potentially even reduce it to ...
Explaining neural scaling laws - NASA/ADS
The population loss of trained deep neural networks has been empirically observed to improve as a power law in a variety of large models and datasets.
Two minutes NLP — Scaling Laws for Neural Language Models
We delve into a study on the relations between language model performance and parameters like model scale, model shape, and compute budget.