Loading Now

Summary of Random Search As a Baseline For Sparse Neural Network Architecture Search, by Rezsa Farahani


by Rezsa Farahani

First submitted to arxiv on: 13 Mar 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)

     Abstract of paper      PDF of paper


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
The proposed research introduces a novel approach to learning high-performing sparse neural networks. By applying the Random Search algorithm, researchers aim to find better initialized sparse sub-networks positioned advantageously in the loss landscape. The study compares the post-training performances of found sparse networks at various levels of sparsity against their fully connected parent networks and random sparse configurations. The results show that a significant level of performance can be preserved even when the network is highly sparse, but also highlight that initialized sparse networks found by Random Search do not perform better or converge more efficiently than random counterparts.
Low GrooveSquid.com (original content) Low Difficulty Summary
This research explores ways to create efficient and effective neural networks while reducing computational complexity. By applying the Random Search algorithm, scientists aim to find the best sparse configurations for their models. The study compares how well these models perform compared to their fully connected versions and randomly generated configurations. Surprisingly, even when using a highly sparse network, the model can still achieve impressive results.

Keywords

* Artificial intelligence