4,095 research outputs found
A Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the preferences
and behaviors of users with respect to a set of items (e.g., movies, books,
academic papers). Typically, the latent factors are assumed to be static and,
given these factors, the observed preferences and behaviors of users are
assumed to be generated without order. These assumptions limit the explorative
and predictive capabilities of such models, since users' interests and item
popularity may evolve over time. To address this, we propose dPF, a dynamic
matrix factorization model based on the recent Poisson factorization model for
recommendations. dPF models the time evolving latent factors with a Kalman
filter and the actions with Poisson distributions. We derive a scalable
variational inference algorithm to infer the latent factors. Finally, we
demonstrate dPF on 10 years of user click data from arXiv.org, one of the
largest repository of scientific papers and a formidable source of information
about the behavior of scientists. Empirically we show performance improvement
over both static and, more recently proposed, dynamic recommendation models. We
also provide a thorough exploration of the inferred posteriors over the latent
variables.Comment: RecSys 201
A Collaborative Kalman Filter for Time-Evolving Dyadic Processes
We present the collaborative Kalman filter (CKF), a dynamic model for
collaborative filtering and related factorization models. Using the matrix
factorization approach to collaborative filtering, the CKF accounts for time
evolution by modeling each low-dimensional latent embedding as a
multidimensional Brownian motion. Each observation is a random variable whose
distribution is parameterized by the dot product of the relevant Brownian
motions at that moment in time. This is naturally interpreted as a Kalman
filter with multiple interacting state space vectors. We also present a method
for learning a dynamically evolving drift parameter for each location by
modeling it as a geometric Brownian motion. We handle posterior intractability
via a mean-field variational approximation, which also preserves tractability
for downstream calculations in a manner similar to the Kalman filter. We
evaluate the model on several large datasets, providing quantitative evaluation
on the 10 million Movielens and 100 million Netflix datasets and qualitative
evaluation on a set of 39 million stock returns divided across roughly 6,500
companies from the years 1962-2014.Comment: Appeared at 2014 IEEE International Conference on Data Mining (ICDM
- …