# Embarrassingly Parallel Independent Training of Multi-Layer Perceptrons with Heterogeneous Architectures

arXiv: https://arxiv.org/abs/2206.08369 · HTML: https://mlsanity.com/paper/2206.08369/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Proving the Lottery Ticket Hypothesis: Pruning is All You Need](https://mlsanity.com/paper/2002.00585/) (2020)

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

- [Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!](https://mlsanity.com/paper/2207.06651/) (2022)

## Abstract (arXiv)

The definition of a Neural Network architecture is one of the most critical and challenging tasks to perform. In this paper, we propose ParallelMLPs. ParallelMLPs is a procedure to enable the training of several independent Multilayer Perceptron Neural Networks with a different number of neurons and activation functions in parallel by exploring the principle of locality and parallelization capabilities of modern CPUs and GPUs. The core idea of this technique is to use a Modified Matrix Multiplication that replaces an ordinal matrix multiplication by two simple matrix operations that allow separate and independent paths for gradient flowing, which can be used in other scenarios. We have assessed our algorithm in simulated datasets varying the number of samples, features and batches using 10,000 different models. We achieved a training speedup from 1 to 4 orders of magnitude if compared to the sequential approach.