Deep Learning
Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
PINNs are, however, sharing the same weakness with coordinate-based MLPs (or INRs), which hinders the application of PINNs/INRs to more diverse applications; for a new data instance (e.g., a new PDE for PINNs or a new image for INRs), training a new neural network (typically from
Supplementary Material for A TISS: Autoregressive Transformers for Indoor Scene Synthesis Despoina Paschalidou
A TISS with various transformer models that consider ordering.
548a482d4496ce109cddfbeae5defa7d-Supplemental-Conference.pdf
In this section, we provide PIE-G's detailed settings. In this paper, we use the ready-mademodels from the following link: ResNet: https://github.com/pytorch/vision; PIE-G with MoCo-v2 also achieves competitive sample efficiency. The episode lengths in Drawer World tasks are 200 steps with 4 action repeat. Table 9: Compare with RRL.RRL barely generalize to the new environmentsintheDMC-GB.
