161,552 research outputs found
REPP-H: runtime estimation of power and performance on heterogeneous data centers
Modern data centers increasingly demand improved performance with minimal power consumption. Managing the power and performance requirements of the applications is challenging because these data centers, incidentally or intentionally, have to deal with server architecture heterogeneity [19], [22]. One critical challenge that data centers have to face is how to manage system power and performance given the different application behavior across multiple different architectures.This work has been supported by the EU FP7 program (Mont-Blanc 2, ICT-610402), by the
Ministerio de Economia (CAP-VII, TIN2015-65316-P), and the Generalitat de Catalunya (MPEXPAR, 2014-SGR-1051).
The material herein is based in part upon work supported by the US NSF, grant numbers ACI-1535232 and CNS-1305220.Peer ReviewedPostprint (author's final draft
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Training convolutional networks (CNN's) that fit on a single GPU with
minibatch stochastic gradient descent has become effective in practice.
However, there is still no effective method for training large CNN's that do
not fit in the memory of a few GPU cards, or for parallelizing CNN training. In
this work we show that a simple hard mixture of experts model can be
efficiently trained to good effect on large scale hashtag (multilabel)
prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991,
Collobert et. al. 2003), but in the past, researchers have had to devise
sophisticated methods to deal with data fragmentation. We show empirically that
modern weakly supervised data sets are large enough to support naive
partitioning schemes where each data point is assigned to a single expert.
Because the experts are independent, training them in parallel is easy, and
evaluation is cheap for the size of the model. Furthermore, we show that we can
use a single decoding layer for all the experts, allowing a unified feature
embedding space. We demonstrate that it is feasible (and in fact relatively
painless) to train far larger models than could be practically trained with
standard CNN architectures, and that the extra capacity can be well used on
current datasets.Comment: Appearing in CVPR 201
An Alarm System For Segmentation Algorithm Based On Shape Model
It is usually hard for a learning system to predict correctly on rare events
that never occur in the training data, and there is no exception for
segmentation algorithms. Meanwhile, manual inspection of each case to locate
the failures becomes infeasible due to the trend of large data scale and
limited human resource. Therefore, we build an alarm system that will set off
alerts when the segmentation result is possibly unsatisfactory, assuming no
corresponding ground truth mask is provided. One plausible solution is to
project the segmentation results into a low dimensional feature space; then
learn classifiers/regressors to predict their qualities. Motivated by this, in
this paper, we learn a feature space using the shape information which is a
strong prior shared among different datasets and robust to the appearance
variation of input data.The shape feature is captured using a Variational
Auto-Encoder (VAE) network that trained with only the ground truth masks.
During testing, the segmentation results with bad shapes shall not fit the
shape prior well, resulting in large loss values. Thus, the VAE is able to
evaluate the quality of segmentation result on unseen data, without using
ground truth. Finally, we learn a regressor in the one-dimensional feature
space to predict the qualities of segmentation results. Our alarm system is
evaluated on several recent state-of-art segmentation algorithms for 3D medical
segmentation tasks. Compared with other standard quality assessment methods,
our system consistently provides more reliable prediction on the qualities of
segmentation results.Comment: Accepted to ICCV 2019 (10 pages, 4 figures
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Forecasting audience increase on YouTube
User profiles constructed on Social Web platforms are often motivated by the need to maximise user reputation within a community. Subscriber, or follower, counts are an indicator of the influence and standing that the user has, where greater values indicate a greater perception or regard for what the user has to say or share. However, at present there lacks an understanding of the factors that lead to an increase in such audience levels, and how a user’s behaviour can a!ect their reputation. In this paper we attempt to fill this gap, by examining data collected from YouTube over regular time intervals. We explore the correlation between the subscriber counts and several behaviour features - extracted from both the user’s profile and the content they have shared. Through the use of a Multiple Linear Regression model we are able to forecast the audience levels that users will yield based on observed behaviour. Combining such a model with an exhaustive feature selection process, we yield statistically significant performance over a baseline model containing all features
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