However, whereas most autoencoder architectures encode adiscrete, fixed representation of latent variables, VAEs encode acontinuous, probabilistic representation of that latent space. This enables a VAE to not only accurately reconstruct the exact original input, but also use variational inference to gen...
VQ-VAE [76] has shown promising capability in representing data as dis- crete tokens. VQGAN [54] further models the prior distri- bution of the latent space via a transformer with the GAN training. If VQGAN is directly applied onto videos, it will ig...
The one architecture dimension where you we have public information about GPT-4 is the length of its context window, which has increased from 2048 for GPT-3 to 8192 and 32768 for different versions of GPT-4. The context window is the text prompt you put in to get an answer out, so fo...
Thus, the proposed Self-Supervised Neural Topic Model (SSTM) differs from existing models in the following facets: (1). Unlike VAE-based approaches, which utilize improper prior distribution (such as Gaussian and Logistic-Normal distributions) in latent topic space, the SSTM models topics with ...
Thus, the proposed Self-Supervised Neural Topic Model (SSTM) differs from existing models in the following facets: (1). Unlike VAE-based approaches, which utilize improper prior distribution (such as Gaussian and Logistic-Normal distributions) in latent topic space, the SSTM models topics with ...
Regarding the social dimension of Industry 4.0, several benefits for employees are named, such as enhanced human learning through intelligent assistance systems as well as human machine interfaces that lead to increased employee satisfaction in industrial workplaces [8,23]. However, current literature ...