all AI news
Meta-Learning Dynamics Forecasting Using Task Inference. (arXiv:2102.10271v4 [cs.LG] UPDATED)
June 17, 2022, 1:11 a.m. | Rui Wang, Robin Walters, Rose Yu
cs.LG updates on arXiv.org arxiv.org
Current deep learning models for dynamics forecasting struggle with
generalization. They can only forecast in a specific domain and fail when
applied to systems with different parameters, external forces, or boundary
conditions. We propose a model-based meta-learning method called DyAd which can
generalize across heterogeneous domains by partitioning them into different
tasks. DyAd has two parts: an encoder which infers the time-invariant hidden
features of the task with weak supervision, and a forecaster which learns the
shared dynamics of the …
arxiv dynamics forecasting inference learning lg meta meta-learning
More from arxiv.org / cs.LG updates on arXiv.org
Generalized Schr\"odinger Bridge Matching
1 day, 6 hours ago |
arxiv.org
Tight bounds on Pauli channel learning without entanglement
1 day, 6 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Analyst - Associate
@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India
Staff Data Engineer (Data Platform)
@ Coupang | Seoul, South Korea
AI/ML Engineering Research Internship
@ Keysight Technologies | Santa Rosa, CA, United States
Sr. Director, Head of Data Management and Reporting Execution
@ Biogen | Cambridge, MA, United States
Manager, Marketing - Audience Intelligence (Senior Data Analyst)
@ Delivery Hero | Singapore, Singapore