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Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients
Jun Sun, Tianyi Chen, Georgios Giannakis, Zaiyue Yang
Neural Information Processing Systems http://nips.cc/
58a799d16fb0c1f2014e98f4ba972b25-Paper-Conference.pdf
RL that utilize function approximation to generalize observational data to unknown states/actions. The goal of this paper is to study the sample complexity of policy-based RL, which is arguably the simplest setting for RL with function approximation (Kearns et al., 1999; Kakade, 2003).
Identification and Estimation of Causal Effects from Dependent Data
Nevertheless, in somesettings,suchassocialnetworks,infectiousdiseasemodeling,andreasoning with spatial and temporal data, this assumption is false.
Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps Christopher J. Kymn
These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation.
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets.
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Such complexpatterns can be crucial when amodel is trained on MTS and might need ahuge amount of training samples to be captured by amachine learningalgorithm.