10,206 research outputs found
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many
real-world applications. However, existing methods mainly focus on networks
with single-typed nodes/edges and cannot scale well to handle large networks.
Many real-world networks consist of billions of nodes and edges of multiple
types, and each node is associated with different attributes. In this paper, we
formalize the problem of embedding learning for the Attributed Multiplex
Heterogeneous Network and propose a unified framework to address this problem.
The framework supports both transductive and inductive learning. We also give
the theoretical analysis of the proposed framework, showing its connection with
previous works and proving its better expressiveness. We conduct systematical
evaluations for the proposed framework on four different genres of challenging
datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results
demonstrate that with the learned embeddings from the proposed framework, we
can achieve statistically significant improvements (e.g., 5.99-28.23% lift by
F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link
prediction. The framework has also been successfully deployed on the
recommendation system of a worldwide leading e-commerce company, Alibaba Group.
Results of the offline A/B tests on product recommendation further confirm the
effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn
A dual framework for low-rank tensor completion
One of the popular approaches for low-rank tensor completion is to use the
latent trace norm regularization. However, most existing works in this
direction learn a sparse combination of tensors. In this work, we fill this gap
by proposing a variant of the latent trace norm that helps in learning a
non-sparse combination of tensors. We develop a dual framework for solving the
low-rank tensor completion problem. We first show a novel characterization of
the dual solution space with an interesting factorization of the optimal
solution. Overall, the optimal solution is shown to lie on a Cartesian product
of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian
optimization framework for proposing computationally efficient trust region
algorithm. The experiments illustrate the efficacy of the proposed algorithm on
several real-world datasets across applications.Comment: Aceepted to appear in Advances of Nueral Information Processing
Systems (NIPS), 2018. A shorter version appeared in the NIPS workshop on
Synergies in Geometric Data Analysis 201
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