84,472 research outputs found
Perceptually-Driven Video Coding with the Daala Video Codec
The Daala project is a royalty-free video codec that attempts to compete with
the best patent-encumbered codecs. Part of our strategy is to replace core
tools of traditional video codecs with alternative approaches, many of them
designed to take perceptual aspects into account, rather than optimizing for
simple metrics like PSNR. This paper documents some of our experiences with
these tools, which ones worked and which did not. We evaluate which tools are
easy to integrate into a more traditional codec design, and show results in the
context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital
Image Processing (ADIP), 201
Nomographic Functions: Efficient Computation in Clustered Gaussian Sensor Networks
In this paper, a clustered wireless sensor network is considered that is
modeled as a set of coupled Gaussian multiple-access channels. The objective of
the network is not to reconstruct individual sensor readings at designated
fusion centers but rather to reliably compute some functions thereof. Our
particular attention is on real-valued functions that can be represented as a
post-processed sum of pre-processed sensor readings. Such functions are called
nomographic functions and their special structure permits the utilization of
the interference property of the Gaussian multiple-access channel to reliably
compute many linear and nonlinear functions at significantly higher rates than
those achievable with standard schemes that combat interference. Motivated by
this observation, a computation scheme is proposed that combines a suitable
data pre- and post-processing strategy with a nested lattice code designed to
protect the sum of pre-processed sensor readings against the channel noise.
After analyzing its computation rate performance, it is shown that at the cost
of a reduced rate, the scheme can be extended to compute every continuous
function of the sensor readings in a finite succession of steps, where in each
step a different nomographic function is computed. This demonstrates the
fundamental role of nomographic representations.Comment: to appear in IEEE Transactions on Wireless Communication
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Programming language processing (similar to natural language processing) is a
hot research topic in the field of software engineering; it has also aroused
growing interest in the artificial intelligence community. However, different
from a natural language sentence, a program contains rich, explicit, and
complicated structural information. Hence, traditional NLP models may be
inappropriate for programs. In this paper, we propose a novel tree-based
convolutional neural network (TBCNN) for programming language processing, in
which a convolution kernel is designed over programs' abstract syntax trees to
capture structural information. TBCNN is a generic architecture for programming
language processing; our experiments show its effectiveness in two different
program analysis tasks: classifying programs according to functionality, and
detecting code snippets of certain patterns. TBCNN outperforms baseline
methods, including several neural models for NLP.Comment: Accepted at AAAI-1
Building Program Vector Representations for Deep Learning
Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1
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