Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

Bayesian inference for model selection centres on comparing competing hypotheses by evaluating how well each model explains observed data, accounting for prior beliefs about parameters. The ...

A neural network is a machine learning model originally inspired by how the human brain works (Courtesy: Shutterstock/Jackie Niam) Precision measurements of theoretical parameters are a core element ...

Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability*, particularly for reasoning and planning (known as System 2 abilities) has been lacking.

Large language models (LLMs) have made significant strides in artificial intelligence (AI) natural language generation. Models such as GPT-3, Megatron-Turing, Chinchilla, PaLM-2, Falcon, and Llama 2 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...