Your session has been flagged for unusual activity.
Please confirm you're human to continue.
A generative model that learns a probabilistic latent space, letting it both compress data and sample new examples from it.
Kingma & Welling (2014) introduced the variational autoencoder, which trains an encoder to map data into a continuous probabilistic latent space and a decoder to reconstruct it — enabling both compression and the generation of new samples. The key contribution was a practical way to train such models with gradient descent (the reparameterisation trick).
VAEs influenced later generative methods and underpin parts of modern systems such as latent diffusion image generators.
Primary, peer-reviewed and archival sources for this definition.
Run the same prompt across ChatGPT, Claude, Gemini and Grok — grounded in your own sources, cross-checked against each other.
Try MultipleChat FreeContinue learning
See the AI subscription that replaces separate ChatGPT, Claude, Gemini and Grok accounts.
Multi-model AIRun the same prompt through ChatGPT, Claude, Gemini and Grok before trusting one answer.
GuideChoose the best model for writing, research, coding, documents, images and business work.
Buyer intentOne app for several frontier AI models, side-by-side comparison and model collaboration.
PlatformA professional workspace for using several AI models, comparing answers and creating deliverables.
Core conceptUse several frontier models together for stronger answers and fewer blind spots.
ComparisonCompare several AI answers side by side when the output matters.
WorkflowBuild a professional AI stack for documents, research, writing, images and automation.
Free toolsStart with free tools, then upgrade when multi-model workflows save real time.
