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4 papers · 2 filters
Can deep learning help you find the perfect match?
Harm de Vries, Jason Yosinski
Is he/she my type or not? The answer to this question depends on the personal preferences of the one asking it. The individual process of obtaining a full answer may generally be d…
GSNs : Generative Stochastic Networks
Guillaume Alain, Yoshua Bengio, Li Yao +4
We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based…
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
Rong Ge, Furong Huang, Chi Jin +1
We analyze stochastic gradient descent for optimizing non-convex functions. In many cases for non-convex functions the goal is to find a reasonable local minimum, and the main conc…
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
Adith Swaminathan, Thorsten Joachims
We develop a learning principle and an efficient algorithm for batch learning from logged bandit feedback. This learning setting is ubiquitous in online systems (e.g., ad placement…