all AI news
VC Theoretical Explanation of Double Descent. (arXiv:2205.15549v2 [stat.ML] UPDATED)
Aug. 23, 2022, 1:12 a.m. | Eng Hock Lee, Vladimir Cherkassky
cs.LG updates on arXiv.org arxiv.org
There has been growing interest in generalization performance of large
multilayer neural networks that can be trained to achieve zero training error,
while generalizing well on test data. This regime is known as 'second descent'
and it appears to contradict the conventional view that optimal model
complexity should reflect an optimal balance between underfitting and
overfitting, i.e., the bias-variance trade-off. This paper presents a
VC-theoretical analysis of double descent and shows that it can be fully
explained by classical VC-generalization …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer
@ GPTZero | Toronto, Canada
ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)
@ HelloBetter | Remote
Doctoral Researcher (m/f/div) in Automated Processing of Bioimages
@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena
Seeking Developers and Engineers for AI T-Shirt Generator Project
@ Chevon Hicks | Remote
Senior Applied Data Scientist
@ dunnhumby | London
Principal Data Architect - Azure & Big Data
@ MGM Resorts International | Home Office - US, NV