VERSES AI Research Blog – “Deep Learning (DL), enables many incredible capabilities such as image recognition, natural language processing, and content generation and results are fast and accurate. However, it uses a technique called Maximum Likelihood Estimation (MLE) and so the outputs tend to be the most likely prediction based on their training data (hence maximum likelihood) and exclude a confidence, or certainty, qualifier on the accuracy of the answer. Uncertainty and probability estimation, which are critical for conditions that are complex or unpredictable such as autonomous vehicles, financial modeling, health diagnostics, and high risk decision-making, is something that DL does quite poorly.

A recent paper by VERSES Research Lab titled Gradient-free variational learning with conditional mixture networks (accepted at NeurIPS 2024 Bayesian Decision-Making and Uncertainty Workshop, and invited to give a lightning talk) explores a novel method called CAVI-CMN (Coordinate Ascent Variational Inference for Conditional Mixture Networks) which tackles the significant challenge of making machine learning models more reliable by allowing them to understand what they don’t know.”