Search Results for author:
Found 4 papers, 1 papers with code
Rethinking Sharpness-Aware Minimization as Variational Inference
Sharpness-aware minimization (SAM) aims to improve the generalisation of gradient-based learning by seeking out flat minima.
Depth Without the Magic: Inductive Bias of Natural Gradient Descent
In gradient descent, changing how we parametrize the model can lead to drastically different optimization trajectories, giving rise to a surprising range of meaningful inductive biases: identifying sparse classifiers or reconstructing low-rank matrices without explicit regularization.
Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations
This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections.
ILoSA: Interactive Learning of Stiffness and Attractors
Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
