1 code implementation • 27 Feb 2024 • Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl, Gian Maria Marconi, Clement Bazan, Rio Yokota, Iryna Gurevych, Daniel Cremers, Mohammad Emtiyaz Khan, Thomas Möllenhoff
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
no code implementations • 21 Oct 2022 • Alexandre Piche, Valentin Thomas, Joseph Marino, Rafael Pardinas, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan
However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values.
1 code implementation • 4 Jun 2021 • Alexandre Piché, Valentin Thomas, Rafael Pardinas, Joseph Marino, Gian Maria Marconi, Christopher Pal, Mohammad Emtiyaz Khan
Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.
1 code implementation • 25 Feb 2021 • Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, Carlo Ciliberto
Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map.
no code implementations • 28 May 2020 • Gian Maria Marconi, Lorenzo Rosasco, Carlo Ciliberto
Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative models.
no code implementations • NeurIPS 2018 • Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure.