Where ELMs Have Proven Effective Compared to DNNs

 

Domains Where ELMs Have Proven Effective Compared to DNNs

Quantitative Finance

ELMs excel in time-sensitive financial tasks—including option pricing, stock prediction, implied volatility modeling, and solving financial PDEs. A recent 2025 study reports ELMs offer higher computational speed, comparable accuracy, and stronger generalization compared to DNNs, Gaussian processes, and logistic regression. They also outperform Physics-Informed Neural Networks (PINNs) in solving PDEs relevant to finance.arXiv+1

Energy Disaggregation

In the task of energy disaggregation on the UK-DALE dataset, ELMs outperformed not just traditional models like Factorial Hidden Markov Models (FHMMs) but also deep neural networks (DNNs, CNNs, LSTMs)—demonstrating both better generalization and performance under unseen conditions.MDPI

Real-Time Document Image Classification

A layered method using a deep CNN as feature extractor, followed by ELM as classifier, outperformed previous deep-learning-based methods on the Tobacco-3482 dataset—achieving a 25% relative error reduction, with training of the ELM taking just 1.176 seconds, and inference for 2,482 images only 3.066 seconds.arXiv

Handwritten Digit / Image Classification

Variants of ELM—such as Deep Convolutional ELM (DC-ELM) and Local Receptive Field ELM (LRF-ELM)—have shown competitive accuracy with significantly faster training on datasets like MNIST and USPS. Some variants approached deep network accuracy while maintaining ELM’s speed advantage.PMC

Small Tabular & Vector Data Tasks

On more modest-sized datasets (like Sonar and Bank datasets), ELMs outperformed classical backpropagation-trained networks with faster training. In other scenarios with similar performance, the speed and simplicity of ELMs remained compelling.ACM Digital LibrarySpringerLink

Where ELMs Fall Short Compared to Deep Learning

Image classification tasks on large-scale, complex datasets (e.g., ImageNet) remain a clear outlier where ELMs—including LRF-ELM variants—are consistently inferior to modern deep neural networks, both in accuracy and overall effectiveness.ACM Digital LibrarySpringerLink

Summary Table

Application AreaELM Advantage Over DNNsNotes
Quantitative FinanceFaster training/inference, similar accuracyIdeal for PDEs, pricing, high-speed tasks
Energy DisaggregationBetter generalization, improved accuracyOutperforms LSTM/CNN in performance
Document Image Classification (real-time)More accurate and efficient than prior CNN approachesExcellent for real-time, production settings
Handwritten Digit/Image (small-scale)Competitive accuracy, much faster to trainGood for resource- or time-constrained use
Small Tabular/VectorsOften surpasses classical networks in some datasetsLightweight and easy to train
Large-Scale Image ClassificationConsistently worse performanceNot competitive for deep vision tasks

Reddit Insight (on ELM structure)

A helpful breakdown from a user on r/learnmachinelearning:

“An ELM (Extreme Learning Machine) is a simple neural network with randomly initialized and non-trainable weights. The only thing you 'train' is the last layer, which is a basic linear regression. Thus, it can be trained super quickly even without any numerical optimization like gradient descent.”Reddit

This randomness and minimal training are where ELM’s speed and simplicity stem from.

Final Takeaway

ELMs shine in settings where speed, simplicity, and strong generalization on modest datasets are critical. They're particularly compelling in domains like finance, energy analytics, real-time image tasks, and small-to-medium scale datasets.

However, for deep vision tasks with huge, complex datasets, traditional DNNs remain the gold standard in terms of accuracy and modeling ability. (- As far as is known.)

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