all AI news
SVD Perspectives for Augmenting DeepONet Flexibility and Interpretability. (arXiv:2204.12670v3 [cs.LG] UPDATED)
Nov. 8, 2022, 2:12 a.m. | Simone Venturi, Tiernan Casey
cs.LG updates on arXiv.org arxiv.org
Deep operator networks (DeepONets) are powerful architectures for fast and
accurate emulation of complex dynamics. As their remarkable generalization
capabilities are primarily enabled by their projection-based attribute, we
investigate connections with low-rank techniques derived from the singular
value decomposition (SVD). We demonstrate that some of the concepts behind
proper orthogonal decomposition (POD)-neural networks can improve DeepONet's
design and training phases. These ideas lead us to a methodology extension that
we name SVD-DeepONet. Moreover, through multiple SVD analyses, we find that …
More from arxiv.org / cs.LG updates on arXiv.org
Testing the Segment Anything Model on radiology data
1 day, 10 hours ago |
arxiv.org
Calorimeter shower superresolution
1 day, 10 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US