all AI news
Lifelong Generative Learning via Knowledge Reconstruction. (arXiv:2201.06418v1 [cs.LG] CROSS LISTED)
Jan. 20, 2022, 2:11 a.m. | Libo Huang, Zhulin An, Xiang Zhi, Yongjun Xu
cs.LG updates on arXiv.org arxiv.org
Generative models often incur the catastrophic forgetting problem when they
are used to sequentially learning multiple tasks, i.e., lifelong generative
learning. Although there are some endeavors to tackle this problem, they suffer
from high time-consumptions or error accumulation. In this work, we develop an
efficient and effective lifelong generative model based on variational
autoencoder (VAE). Unlike the generative adversarial network, VAE enjoys high
efficiency in the training process, providing natural benefits with few
resources. We deduce a lifelong generative model …
More from arxiv.org / cs.LG updates on arXiv.org
Regularization by Texts for Latent Diffusion Inverse Solvers
1 day, 23 hours ago |
arxiv.org
When can transformers reason with abstract symbols?
1 day, 23 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Scientist (m/f/x/d)
@ Symanto Research GmbH & Co. KG | Spain, Germany
Enterprise Data Architect
@ Pathward | Remote
Diagnostic Imaging Information Systems (DIIS) Technologist
@ Nova Scotia Health Authority | Halifax, NS, CA, B3K 6R8
Intern Data Scientist - Residual Value Risk Management (f/m/d)
@ BMW Group | Munich, DE
Analytics Engineering Manager
@ PlayStation Global | United Kingdom, London
Junior Insight Analyst (PR&Comms)
@ Signal AI | Lisbon, Lisbon, Portugal