11,747 research outputs found
Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
Distributed computing systems are well-known to suffer from the problem of
slow or failed nodes; these are referred to as stragglers. Straggler mitigation
(for distributed matrix computations) has recently been investigated from the
standpoint of erasure coding in several works. In this work we present a
strategy for distributed matrix-vector multiplication based on convolutional
coding. Our scheme can be decoded using a low-complexity peeling decoder. The
recovery process enjoys excellent numerical stability as compared to
Reed-Solomon coding based approaches (which exhibit significant problems owing
their badly conditioned decoding matrices). Finally, our schemes are better
matched to the practically important case of sparse matrix-vector
multiplication as compared to many previous schemes. Extensive simulation
results corroborate our findings
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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