4,428 research outputs found
The Convergence of Sparsified Gradient Methods
Distributed training of massive machine learning models, in particular deep
neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace.
Several families of communication-reduction methods, such as quantization,
large-batch methods, and gradient sparsification, have been proposed. To date,
gradient sparsification methods - where each node sorts gradients by magnitude,
and only communicates a subset of the components, accumulating the rest locally
- are known to yield some of the largest practical gains. Such methods can
reduce the amount of communication per step by up to three orders of magnitude,
while preserving model accuracy. Yet, this family of methods currently has no
theoretical justification.
This is the question we address in this paper. We prove that, under analytic
assumptions, sparsifying gradients by magnitude with local error correction
provides convergence guarantees, for both convex and non-convex smooth
objectives, for data-parallel SGD. The main insight is that sparsification
methods implicitly maintain bounds on the maximum impact of stale updates,
thanks to selection by magnitude. Our analysis and empirical validation also
reveal that these methods do require analytical conditions to converge well,
justifying existing heuristics.Comment: NIPS 2018 - Advances in Neural Information Processing Systems;
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Distributed Private Online Learning for Social Big Data Computing over Data Center Networks
With the rapid growth of Internet technologies, cloud computing and social
networks have become ubiquitous. An increasing number of people participate in
social networks and massive online social data are obtained. In order to
exploit knowledge from copious amounts of data obtained and predict social
behavior of users, we urge to realize data mining in social networks. Almost
all online websites use cloud services to effectively process the large scale
of social data, which are gathered from distributed data centers. These data
are so large-scale, high-dimension and widely distributed that we propose a
distributed sparse online algorithm to handle them. Additionally,
privacy-protection is an important point in social networks. We should not
compromise the privacy of individuals in networks, while these social data are
being learned for data mining. Thus we also consider the privacy problem in
this article. Our simulations shows that the appropriate sparsity of data would
enhance the performance of our algorithm and the privacy-preserving method does
not significantly hurt the performance of the proposed algorithm.Comment: ICC201
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
This paper presents a generic Bayesian framework that enables any deep
learning model to actively learn from targeted crowds. Our framework inherits
from recent advances in Bayesian deep learning, and extends existing work by
considering the targeted crowdsourcing approach, where multiple annotators with
unknown expertise contribute an uncontrolled amount (often limited) of
annotations. Our framework leverages the low-rank structure in annotations to
learn individual annotator expertise, which then helps to infer the true labels
from noisy and sparse annotations. It provides a unified Bayesian model to
simultaneously infer the true labels and train the deep learning model in order
to reach an optimal learning efficacy. Finally, our framework exploits the
uncertainty of the deep learning model during prediction as well as the
annotators' estimated expertise to minimize the number of required annotations
and annotators for optimally training the deep learning model.
We evaluate the effectiveness of our framework for intent classification in
Alexa (Amazon's personal assistant), using both synthetic and real-world
datasets. Experiments show that our framework can accurately learn annotator
expertise, infer true labels, and effectively reduce the amount of annotations
in model training as compared to state-of-the-art approaches. We further
discuss the potential of our proposed framework in bridging machine learning
and crowdsourcing towards improved human-in-the-loop systems
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