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Found 6 papers, 3 papers with code
Variational Learning is Effective for Large Deep Networks
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
Bridging the Gap Between Target Networks and Functional Regularization
However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values.
Bridging the Gap Between Target Networks and Functional Regularization
Our findings emphasize that Functional Regularization can be used as a drop-in replacement for Target Networks and result in performance improvement.
Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
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.
Hyperbolic Manifold Regression
Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative models.
Manifold Structured Prediction
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure.
