19,953 research outputs found
The Deep Weight Prior
Bayesian inference is known to provide a general framework for incorporating
prior knowledge or specific properties into machine learning models via
carefully choosing a prior distribution. In this work, we propose a new type of
prior distributions for convolutional neural networks, deep weight prior (DWP),
that exploit generative models to encourage a specific structure of trained
convolutional filters e.g., spatial correlations of weights. We define DWP in
the form of an implicit distribution and propose a method for variational
inference with such type of implicit priors. In experiments, we show that DWP
improves the performance of Bayesian neural networks when training data are
limited, and initialization of weights with samples from DWP accelerates
training of conventional convolutional neural networks.Comment: TL;DR: The deep weight prior learns a generative model for kernels of
convolutional neural networks, that acts as a prior distribution while
training on new dataset
Recommender Systems with Characterized Social Regularization
Social recommendation, which utilizes social relations to enhance recommender
systems, has been gaining increasing attention recently with the rapid
development of online social network. Existing social recommendation methods
are based on the fact that users preference or decision is influenced by their
social friends' behaviors. However, they assume that the influences of social
relation are always the same, which violates the fact that users are likely to
share preference on diverse products with different friends. In this paper, we
present a novel CSR (short for Characterized Social Regularization) model by
designing a universal regularization term for modeling variable social
influence. Our proposed model can be applied to both explicit and implicit
iteration. Extensive experiments on a real-world dataset demonstrate that CSR
significantly outperforms state-of-the-art social recommendation methods.Comment: to appear in CIKM 201
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