We derive a tighter inequality than Cauchy-Schwarz Inequality, leverage it to refine Pearson's

Recentstudieshaveshownthat the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs.

The learner's goal is to identify the optimal arm

Risk-averse reinforcement learning (RL) seeks to provide a risk-averse policy for high-stakes real-world decision problems. These high-stake domains include autonomous driving (Jin et al., 2019; Sharma et al., 2020), robot collision avoidance (Ahmadi et al., 2021; Hakobyan and Y ang, 2021),

To address the above issues, we propose CrossGNN, a linear complexity GNN model to refine the cross-scale and cross-variable interaction for MTS. To deal with the unexpected noise in time dimension, an adaptive multi-scale identifier (AMSI) is leveraged to construct multi-scale time series with reduced noise.