250 research outputs found
Bayesian Probabilistic Matrix Factorization: A User Frequency Analysis
Matrix factorization (MF) has become a common approach to collaborative
filtering, due to ease of implementation and scalability to large data sets.
Two existing drawbacks of the basic model is that it does not incorporate side
information on either users or items, and assumes a common variance for all
users. We extend the work of constrained probabilistic matrix factorization by
deriving the Gibbs updates for the side feature vectors for items
(Salakhutdinov and Minh, 2008). We show that this Bayesian treatment to the
constrained PMF model outperforms simple MAP estimation. We also consider
extensions to heteroskedastic precision introduced in the literature
(Lakshminarayanan, Bouchard, and Archambeau, 2011). We show that this tends
result in overfitting for deterministic approximation algorithms (ex:
Variational inference) when the observed entries in the user / item matrix are
distributed in an non-uniform manner. In light of this, we propose a truncated
precision model. Our experimental results suggest that this model tends to
delay overfitting
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
We show that matrix completion with trace-norm regularization can be
significantly hurt when entries of the matrix are sampled non-uniformly. We
introduce a weighted version of the trace-norm regularizer that works well also
with non-uniform sampling. Our experimental results demonstrate that the
weighted trace-norm regularization indeed yields significant gains on the
(highly non-uniformly sampled) Netflix dataset.Comment: 9 page
The More You Know: Using Knowledge Graphs for Image Classification
One characteristic that sets humans apart from modern learning-based computer
vision algorithms is the ability to acquire knowledge about the world and use
that knowledge to reason about the visual world. Humans can learn about the
characteristics of objects and the relationships that occur between them to
learn a large variety of visual concepts, often with few examples. This paper
investigates the use of structured prior knowledge in the form of knowledge
graphs and shows that using this knowledge improves performance on image
classification. We build on recent work on end-to-end learning on graphs,
introducing the Graph Search Neural Network as a way of efficiently
incorporating large knowledge graphs into a vision classification pipeline. We
show in a number of experiments that our method outperforms standard neural
network baselines for multi-label classification.Comment: CVPR 201
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