Oceania
Learning to Teach with Dynamic Loss Functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu
Neural Information Processing Systems http://nips.cc/
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
Marco Ciccone, Marco Gallieri, Jonathan Masci, Christian Osendorfer, Faustino Gomez
Recently, several researchers have started to view VDNNs from a dynamical systems perspective.
Transfer of Deep Reactive Policies for MDP Planning
Aniket (Nick) Bajpai, Sankalp Garg, None
The planners use representation-specific algorithms, thus allowing themselves to be run on all domains that can be expressed in the representation.
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
Crucially, the neural model uses miniKanren's
Representation Learning of Compositional Data
Marta Avalos, Richard Nock, Cheng Soon Ong, Julien Rouar, Ke Sun
We consider the problem of learning a low dimensional representation for compositional data.
MetaReg: Towards Domain Generalization using Meta-Regularization
Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa
Existing machine learning algorithms including deep neural networks achieve good performance in cases where the training and the test data are sampled from the same distribution. While this is a reasonable assumption to make, it might not hold true in practice.
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot, Franck Gabriel, Clement Hongler
Neural Information Processing Systems http://nips.cc/
Bilevel learning of the Group Lasso structure
Jordan Frecon, Saverio Salzo, Massimiliano Pontil
We are only aware of few approaches devoted to infer the Group Lasso structure.
On Fast Leverage Score Sampling and Optimal Learning
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, Lorenzo Rosasco
Neural Information Processing Systems http://nips.cc/
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D. Toderici
Most recent methods for learning-based, lossy image compression adopt an approach based on transform coding [1]. In this approach, image compression is achieved by first mapping pixel data into a quantized latent representation and then losslessly compressing the latents.