New spin on speculative decoding works with any model - now built into Transformers We all know that AI is expensive, but a new set of algorithms developed by researchers at the Weizmann Institute of ...

Just as people from different countries speak different languages, AI models also create various internal "languages"—a ...

This study uses a hybrid deep learning technique to classify asphalt, pavement, and unpaved roads. In real-world circumstances, image data noise can damage image categorization algorithms. This issue ...

AHDM has a dual classifier framework. It trains first neural network based on the encoding features obtained from the autoencoder feature enhancement algorithm to detect small-sample malicious traffic ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...

Traditional diagnostic methods have shortcomings in accuracy and robustness. Therefore, the study integrates variational autoencoders with long short-term memory network models, enhances them using ...

Figure 1. Main algorithm structures. (a) An autoencoder is trained to produce a time series from 1 to 100 percent of a gait cycle (e.g., the within-stride metabolic cost time series according to ...