Country
Non-StochasticControlwithBanditFeedback
For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm.
54f7125dee9b8b3dc798bb9a082b09e2-Paper-Conference.pdf
Theresultingsearch is costly, and designing suitable scores or tests that capture prior knowledge is difficult.
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.
