Home Page on Daniel Bethell

Neural networks learn complex internal representations of their inputs, but these representations are usually difficult to inspect directly.

Quantifying uncertainty to reject out-of-distribution/adversarially attacked inputs in crucial for deploying deep learning models in the real-world.

Robots cannot navigate safely if their perception misses hazards.

Peer review is supposed to reward merit and novel research, but the data suggests the playing field at the Top AI conferences isn’t perfectly level.

Safe reinforcement learning is essential for safety-critical domains, but current methods are often rely on prior knowledge.

This tutorial dives into conformal prediction, covering basics, advanced methods with Python code, and resources to help you apply CP in your next project!

Kolmogorov-Arnold Networks have been promised to be the next big thing in deep learning.

Conformal Prediction is an amazingly powerful tool in safety-critical applications, but current state-of-the-art conformal prediction algorithms tend to overestimate their prediction sets/intervals.