Papers
Analog Memories in a Balanced Rate-Based Network of E-I Neurons
Dylan Festa, Guillaume Hennequin, Mate Lengyel
Orbit Regularization
Renato Negrinho, Andre Martins
Learning with Pseudo-Ensembles
Philip Bachman, Ouais Alsharif, Doina Precup
General Stochastic Networks for Classification
Matthias Zöhrer, Franz Pernkopf
The Large Margin Mechanism for Differentially Private Maximization
Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
Algorithm selection by rational metareasoning as a model of human strategy selection
Falk Lieder, Dillon Plunkett, Jessica B Hamrick et al.
Information-based learning by agents in unbounded state spaces
Shariq A Mobin, James A Arnemann, Fritz Sommer
Self-Paced Learning with Diversity
Lu Jiang, Deyu Meng, Shoou-I Yu et al.
Variational Gaussian Process State-Space Models
Roger Frigola, Yutian Chen, Carl Edward Rasmussen
Spectral k-Support Norm Regularization
Andrew M McDonald, Massimiliano Pontil, Dimitris Stamos
Asynchronous Anytime Sequential Monte Carlo
Brooks Paige, Frank Wood, Arnaud Doucet et al.
Multi-Resolution Cascades for Multiclass Object Detection
Mohammad Saberian, Nuno Vasconcelos
Tree-structured Gaussian Process Approximations
Thang D Bui, Richard E Turner
Efficient learning by implicit exploration in bandit problems with side observations
Tomáš Kocák, Gergely Neu, Michal Valko et al.
Pre-training of Recurrent Neural Networks via Linear Autoencoders
Luca Pasa, Alessandro Sperduti
Rounding-based Moves for Metric Labeling
M. Pawan Kumar
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Florian Stimberg, Andreas Ruttor, Manfred Opper
Structure learning of antiferromagnetic Ising models
Guy Bresler, David Gamarnik, Devavrat Shah
Unsupervised learning of an efficient short-term memory network
Pietro Vertechi, Wieland Brendel, Christian K. Machens
Projective dictionary pair learning for pattern classification
Shuhang Gu, Lei Zhang, Wangmeng Zuo et al.
Projecting Markov Random Field Parameters for Fast Mixing
Xianghang Liu, Justin Domke
Learning on graphs using Orthonormal Representation is Statistically Consistent
Rakesh Shivanna, Chiranjib Bhattacharyya
Automated Variational Inference for Gaussian Process Models
Trung V Nguyen, Edwin V. Bonilla
Distributed Balanced Clustering via Mapping Coresets
Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi et al.
Sparse Polynomial Learning and Graph Sketching
Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G Dimakis et al.