83,110 research outputs found
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of
parameters, making them difficult to be deployed and updated on devices with
limited resources such as mobile phones and other smart embedded systems. In
this paper, we propose a scalable representation of the network parameters, so
that different applications can select the most suitable bit rate of the
network based on their own storage constraints. Moreover, when a device needs
to upgrade to a high-rate network, the existing low-rate network can be reused,
and only some incremental data are needed to be downloaded. We first
hierarchically quantize the weights of a pre-trained deep neural network to
enforce weight sharing. Next, we adaptively select the bits assigned to each
layer given the total bit budget. After that, we retrain the network to
fine-tune the quantized centroids. Experimental results show that our method
can achieve scalable compression with graceful degradation in the performance.Comment: 5 pages, 4 figures, ACM Multimedia 201
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms
Brain networks in fMRI are typically identified using spatial independent
component analysis (ICA), yet mathematical constraints such as sparse coding
and positivity both provide alternate biologically-plausible frameworks for
generating brain networks. Non-negative Matrix Factorization (NMF) would
suppress negative BOLD signal by enforcing positivity. Spatial sparse coding
algorithms ( Regularized Learning and K-SVD) would impose local
specialization and a discouragement of multitasking, where the total observed
activity in a single voxel originates from a restricted number of possible
brain networks.
The assumptions of independence, positivity, and sparsity to encode
task-related brain networks are compared; the resulting brain networks for
different constraints are used as basis functions to encode the observed
functional activity at a given time point. These encodings are decoded using
machine learning to compare both the algorithms and their assumptions, using
the time series weights to predict whether a subject is viewing a video,
listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects.
For classifying cognitive activity, the sparse coding algorithm of
Regularized Learning consistently outperformed 4 variations of ICA across
different numbers of networks and noise levels (p0.001). The NMF algorithms,
which suppressed negative BOLD signal, had the poorest accuracy. Within each
algorithm, encodings using sparser spatial networks (containing more
zero-valued voxels) had higher classification accuracy (p0.001). The success
of sparse coding algorithms may suggest that algorithms which enforce sparse
coding, discourage multitasking, and promote local specialization may capture
better the underlying source processes than those which allow inexhaustible
local processes such as ICA
Demystifying the Scaling Laws of Dense Wireless Networks: No Linear Scaling in Practice
We optimize the hierarchical cooperation protocol of Ozgur, Leveque and Tse,
which is supposed to yield almost linear scaling of the capacity of a dense
wireless network with the number of users . Exploiting recent results on the
optimality of "treating interference as noise" in Gaussian interference
channels, we are able to optimize the achievable average per-link rate and not
just its scaling law. Our optimized hierarchical cooperation protocol
significantly outperforms the originally proposed scheme. On the negative side,
we show that even for very large , the rate scaling is far from linear, and
the optimal number of stages is less than 4, instead of as required for almost linear scaling. Combining our results and the
fact that, beyond a certain user density, the network capacity is fundamentally
limited by Maxwell laws, as shown by Francheschetti, Migliore and Minero, we
argue that there is indeed no intermediate regime of linear scaling for dense
networks in practice.Comment: 5 pages, 6 figures, ISIT 2014. arXiv admin note: substantial text
overlap with arXiv:1402.181
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