all AI news
QuasiNet: a neural network with trainable product layers
Feb. 27, 2024, 5:44 a.m. | Krist\'ina Malinovsk\'a, Slavom\'ir Holenda, \v{L}udov\'it Malinovsk\'y
cs.LG updates on arXiv.org arxiv.org
Abstract: Classical neural networks achieve only limited convergence in hard problems such as XOR or parity when the number of hidden neurons is small. With the motivation to improve the success rate of neural networks in these problems, we propose a new neural network model inspired by existing neural network models with so called product neurons and a learning rule derived from classical error backpropagation, which elegantly solves the problem of mutually exclusive situations. Unlike existing …
abstract arxiv convergence cs.ai cs.lg cs.ne hidden motivation network networks neural network neural networks neurons product rate small success type xor
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Analyst (Digital Business Analyst)
@ Activate Interactive Pte Ltd | Singapore, Central Singapore, Singapore