Statistical Learning
Learning Energy Networks with Generalized Fenchel-Y oung Losses
We demonstrate our losses on multilabel classification and imitation learning tasks.
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
Moreover, the human detector involves extra memory as well as computational cost.
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Models in these systems are often developed and trained independently, which raises an obvious concern: Can improving a machine-learning model make the overall system worse?
Impression learning Online representation learning with synaptic plasticity Appendices
Our derivation of the update for IL (Eq. 3) is based on an expansion of log We examine the consequences of this bias formula for our specific model. Note that the update term in Eq. (S1) is However, we will show in Appendix C that these updates may have high variance. 'reparameterization trick,' in which a change of variables allows the use of stochastic gradient descent It is worth noting that this'reparameterization' will work only for additive Gaussian noise. As already mentioned, WS can be viewed as a special case of IL. Since WS is a special case of IL, the bias properties of its individual samples are identical.
Overlapping Spaces for Compact Graph Representations
Additionally, we propose an optimization algorithm that automatically learns the optimal configuration.
