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Found 6 papers, 2 papers with code
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
With those metrics we identify tasks which are challenging for recent ML methods and propose these tasks as future challenges for the community.
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
The experiments also show several advantages of CAPE, such as its increased ability to generalize to unseen PDE parameters without large increases inference time and parameter count.
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation
In this paper, we propose a new formalism of knowledge distillation for regression problems.
An Empirical Study of the Effects of Sample-Mixing Methods for Efficient Training of Generative Adversarial Networks
In this paper, we investigated the effect of sample mixing methods, that is, Mixup, CutMix, and newly proposed Smoothed Regional Mix (SRMix), to alleviate this problem.
milIE: Modular & Iterative Multilingual Open Information Extraction
In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Existing biased and unbiased MD simulations, however, are prone to miss either rare events or extrapolative regions -- areas of the configurational space where unreliable predictions are made.
