all AI news
Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks. (arXiv:1909.09153v2 [cs.LG] UPDATED)
Sept. 2, 2022, 1:12 a.m. | Denis Kleyko, Mansour Kheffache, E. Paxon Frady, Urban Wiklund, Evgeny Osipov
cs.LG updates on arXiv.org arxiv.org
The deployment of machine learning algorithms on resource-constrained edge
devices is an important challenge from both theoretical and applied points of
view. In this article, we focus on resource-efficient randomly connected neural
networks known as Random Vector Functional Link (RVFL) networks since their
simple design and extremely fast training time make them very attractive for
solving many applied classification tasks. We propose to represent input
features via the density-based encoding known in the area of stochastic
computing and use the …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Data Scientist
@ Motive | India - Remote
Senior Perception Engineer
@ NVIDIA | US, CA, Santa Clara
Business Data Analyst, Finance and Treasury Data Repositories, Senior Associate
@ State Street | Krakow, Poland
Junior AI Engineer (Internship)
@ Sony | SEU - Italy - Roma
Manager, Data Science 3
@ PayPal | USA - Pennsylvania - Virtual