282,110 research outputs found
Benchmark Analysis of Representative Deep Neural Network Architectures
This work presents an in-depth analysis of the majority of the deep neural
networks (DNNs) proposed in the state of the art for image recognition. For
each DNN multiple performance indices are observed, such as recognition
accuracy, model complexity, computational complexity, memory usage, and
inference time. The behavior of such performance indices and some combinations
of them are analyzed and discussed. To measure the indices we experiment the
use of DNNs on two different computer architectures, a workstation equipped
with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson
TX1 board. This experimentation allows a direct comparison between DNNs running
on machines with very different computational capacity. This study is useful
for researchers to have a complete view of what solutions have been explored so
far and in which research directions are worth exploring in the future; and for
practitioners to select the DNN architecture(s) that better fit the resource
constraints of practical deployments and applications. To complete this work,
all the DNNs, as well as the software used for the analysis, are available
online.Comment: Will appear in IEEE Acces
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
Monocular depth estimation is the task of obtaining a measure of distance for
each pixel using a single image. It is an important problem in computer vision
and is usually solved using neural networks. Though recent works in this area
have shown significant improvement in accuracy, the state-of-the-art methods
tend to require massive amounts of memory and time to process an image. The
main purpose of this work is to improve the performance of the latest solutions
with no decrease in accuracy. To this end, we introduce the Double Refinement
Network architecture. The proposed method achieves state-of-the-art results on
the standard benchmark RGB-D dataset NYU Depth v2, while its frames per second
rate is significantly higher (up to 18 times speedup per image at batch size 1)
and the RAM usage per image is lower
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features
Due to the recent advances in the area of deep learning, it has been
demonstrated that a deep neural network, trained on a huge amount of data, can
recognize cardiac arrhythmias better than cardiologists. Moreover,
traditionally feature extraction was considered an integral part of ECG pattern
recognition; however, recent findings have shown that deep neural networks can
carry out the task of feature extraction directly from the data itself. In
order to use deep neural networks for their accuracy and feature extraction,
high volume of training data is required, which in the case of independent
studies is not pragmatic. To arise to this challenge, in this work, the
identification and classification of four ECG patterns are studied from a
transfer learning perspective, transferring knowledge learned from the image
classification domain to the ECG signal classification domain. It is
demonstrated that feature maps learned in a deep neural network trained on
great amounts of generic input images can be used as general descriptors for
the ECG signal spectrograms and result in features that enable classification
of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in
classifying near 7000 instances by ten-fold cross validation.Comment: Accepted and presented for IEEE Biomedical Circuits and Systems
(BioCAS) on 17th-19th October 2018 in Ohio, US
On combinatorial optimisation in analysis of protein-protein interaction and protein folding networks
Abstract: Protein-protein interaction networks and protein folding networks represent prominent research topics at the intersection of bioinformatics and network science. In this paper, we present a study of these networks from combinatorial optimisation point of view. Using a combination of classical heuristics and stochastic optimisation techniques, we were able to identify several interesting combinatorial properties of biological networks of the COSIN project. We obtained optimal or near-optimal solutions to maximum clique and chromatic number problems for these networks. We also explore patterns of both non-overlapping and overlapping cliques in these networks. Optimal or near-optimal solutions to partitioning of these networks into non-overlapping cliques and to maximum independent set problem were discovered. Maximal cliques are explored by enumerative techniques. Domination in these networks is briefly studied, too. Applications and extensions of our findings are discussed
Practical Block-wise Neural Network Architecture Generation
Convolutional neural networks have gained a remarkable success in computer
vision. However, most usable network architectures are hand-crafted and usually
require expertise and elaborate design. In this paper, we provide a block-wise
network generation pipeline called BlockQNN which automatically builds
high-performance networks using the Q-Learning paradigm with epsilon-greedy
exploration strategy. The optimal network block is constructed by the learning
agent which is trained sequentially to choose component layers. We stack the
block to construct the whole auto-generated network. To accelerate the
generation process, we also propose a distributed asynchronous framework and an
early stop strategy. The block-wise generation brings unique advantages: (1) it
performs competitive results in comparison to the hand-crafted state-of-the-art
networks on image classification, additionally, the best network generated by
BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing
auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of
the search space in designing networks which only spends 3 days with 32 GPUs,
and (3) moreover, it has strong generalizability that the network built on
CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201
Computational Capacity and Energy Consumption of Complex Resistive Switch Networks
Resistive switches are a class of emerging nanoelectronics devices that
exhibit a wide variety of switching characteristics closely resembling
behaviors of biological synapses. Assembled into random networks, such
resistive switches produce emerging behaviors far more complex than that of
individual devices. This was previously demonstrated in simulations that
exploit information processing within these random networks to solve tasks that
require nonlinear computation as well as memory. Physical assemblies of such
networks manifest complex spatial structures and basic processing capabilities
often related to biologically-inspired computing. We model and simulate random
resistive switch networks and analyze their computational capacities. We
provide a detailed discussion of the relevant design parameters and establish
the link to the physical assemblies by relating the modeling parameters to
physical parameters. More globally connected networks and an increased network
switching activity are means to increase the computational capacity linearly at
the expense of exponentially growing energy consumption. We discuss a new
modular approach that exhibits higher computational capacities and energy
consumption growing linearly with the number of networks used. The results show
how to optimize the trade-off between computational capacity and energy
efficiency and are relevant for the design and fabrication of novel computing
architectures that harness random assemblies of emerging nanodevices
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
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