Extensive experiments conducted on ViTs, undefended CNNs, and defended CNNs validate the superiority of our proposed A TT attack method. On average, our approach improves the attack performance by 10.1%

Conducting experiments and collecting data for machine learning models is a complex and expensive endeavor, particularly when confronted with limited information. Typically, extensive experiments to obtain features and labels come with a significant acquisition cost, making it impractical to carry out all of them. Therefore, it becomes crucial to strategically determine what to acquire to maximize the predictive performance while minimizing costs.

In long-term time series forecasting (L TSF) tasks, an increasing number of works have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their underlying dynamics. Recognizing the chaotic nature of real-world data, our model, Attraos, incorporates chaos theory into L TSF, perceiving real-world time series as low-dimensional observations from unknown high-dimensional chaotic dynamical systems. Under the concept of attractor invariance, Attraos utilizes non-parametric Phase Space Reconstruction embedding along with a novel multi-resolution dynamic memory unit to memorize historical dynamical structures, and evolves by a frequency-enhanced local evolution strategy. Detailed theoretical analysis and abundant empirical evidence consistently show that Attraos outperforms various L TSF methods on mainstream L TSF datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.

On the WMT'14 English-German and English-French tasks, our model achieved BLEU scores of 30.95 and 44.27, respectively.

However, the theoretical basis remains unclear.