Statistical Learning
End-to-end Differentiable Proving
Tim Rocktäschel, Sebastian Riedel
Current state-of-the-art methods for automated Knowledge Base (KB) completion use neural link prediction models to learn distributed vector representations of symbols ( i.e. subsymbolic representations)
Consistent Multitask Learning with Nonlinear Output Relations
Carlo Ciliberto, Alessandro Rudi, Lorenzo Rosasco, Massimiliano Pontil
Improving the efficiency of learning from human supervision is one of the great challenges in machine learning. Multitask learning is one of the key approaches in this sense and it is based on the assumption that different learning problems (i.e.
Multi-Task Learning for Contextual Bandits
Aniket Anand Deshmukh, Urun Dogan, Clay Scott
The reward for each arm is random according to a fixed distribution, and the agent's goal is to maximize
Sparse convolutional coding for neuronal assembly detection
Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht
The spikes highlighted in red are part of a repeating motif.
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Neural Information Processing Systems http://nips.cc/
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke
Neural Information Processing Systems http://nips.cc/
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Marco Cusumano-Towner, Vikash K. Mansinghka
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics.
Clustering Billions of Reads for DNA Data Storage
Cyrus Rashtchian, Konstantin Makarychev, Miklos Racz, Siena Ang, Djordje Jevdjic, Sergey Yekhanin, Luis Ceze, Karin Strauss
Neural Information Processing Systems http://nips.cc/
Geometric Descent Method for Convex Composite Minimization
Shixiang Chen, Shiqian Ma, Wei Liu
The rest of this paper is organized as follows.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe
Neural Information Processing Systems http://nips.cc/