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Found 6 papers, 1 papers with code
PAC Learnability under Explanation-Preserving Graph Perturbations
It is shown that the sample complexity of explanation-assisted learning can be arbitrarily smaller than explanation-agnostic learning.
CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning
In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer handle new tasks.
HurriCast: An Automatic Framework Using Machine Learning and Statistical Modeling for Hurricane Forecasting
Hurricanes present major challenges in the U. S. due to their devastating impacts.
Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation
Evaluating with 20, 480 input problems, we show that Smartfluidnet achieves 1. 46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.
Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation
In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution.
A Preliminary Study of Neural Network-based Approximation for HPC Applications
Machine learning, as a tool to learn and model complicated (non)linear relationships between input and output data sets, has shown preliminary success in some HPC problems.
