Summary of Minimum Number Of Neurons in Fully Connected Layers Of a Given Neural Network (the First Approximation), by Oleg I.berngardt
Minimum number of neurons in fully connected layers of a given neural network (the first approximation)
by Oleg I.Berngardt
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces an innovative algorithm for finding the optimal number of neurons in fully connected layers of a neural network, without requiring multiple training sessions. The approach is based on training a wide initial network using cross-validation over at least two folds, and then employs truncated singular value decomposition autoencoders to identify the minimum number of neurons necessary for inference-only mode. This method efficiently searches for the optimal solution, ensuring accurate predictions while minimizing computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the right number of “brain cells” in a computer network that solves a problem without needing multiple tries. It uses a clever technique to first train the network and then find the best configuration for making predictions. This makes the process faster and more efficient, which is important for many applications where speed and accuracy matter. |
Keywords
» Artificial intelligence » Inference » Neural network
