482 research outputs found
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation
With recent breakthroughs in artificial neural networks, deep generative
models have become one of the leading techniques for computational creativity.
Despite very promising progress on image and short sequence generation,
symbolic music generation remains a challenging problem since the structure of
compositions are usually complicated. In this study, we attempt to solve the
melody generation problem constrained by the given chord progression. This
music meta-creation problem can also be incorporated into a plan recognition
system with user inputs and predictive structural outputs. In particular, we
explore the effect of explicit architectural encoding of musical structure via
comparing two sequential generative models: LSTM (a type of RNN) and WaveNet
(dilated temporal-CNN). As far as we know, this is the first study of applying
WaveNet to symbolic music generation, as well as the first systematic
comparison between temporal-CNN and RNN for music generation. We conduct a
survey for evaluation in our generations and implemented Variable Markov Oracle
in music pattern discovery. Experimental results show that to encode structure
more explicitly using a stack of dilated convolution layers improved the
performance significantly, and a global encoding of underlying chord
progression into the generation procedure gains even more.Comment: 8 pages, 13 figure
Reading Scene Text in Deep Convolutional Sequences
We develop a Deep-Text Recurrent Network (DTRN) that regards scene text
reading as a sequence labelling problem. We leverage recent advances of deep
convolutional neural networks to generate an ordered high-level sequence from a
whole word image, avoiding the difficult character segmentation problem. Then a
deep recurrent model, building on long short-term memory (LSTM), is developed
to robustly recognize the generated CNN sequences, departing from most existing
approaches recognising each character independently. Our model has a number of
appealing properties in comparison to existing scene text recognition methods:
(i) It can recognise highly ambiguous words by leveraging meaningful context
information, allowing it to work reliably without either pre- or
post-processing; (ii) the deep CNN feature is robust to various image
distortions; (iii) it retains the explicit order information in word image,
which is essential to discriminate word strings; (iv) the model does not depend
on pre-defined dictionary, and it can process unknown words and arbitrary
strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence
(AAAI-16), 201
Use of definite clause grammars
Call number: LD2668 .R4 CMSC 1987 C53Master of ScienceComputing and Information Science
Generalization bound for estimating causal effects from observational network data
Estimating causal effects from observational network data is a significant
but challenging problem. Existing works in causal inference for observational
network data lack an analysis of the generalization bound, which can
theoretically provide support for alleviating the complex confounding bias and
practically guide the design of learning objectives in a principled manner. To
fill this gap, we derive a generalization bound for causal effect estimation in
network scenarios by exploiting 1) the reweighting schema based on joint
propensity score and 2) the representation learning schema based on Integral
Probability Metric (IPM). We provide two perspectives on the generalization
bound in terms of reweighting and representation learning, respectively.
Motivated by the analysis of the bound, we propose a weighting regression
method based on the joint propensity score augmented with representation
learning. Extensive experimental studies on two real-world networks with
semi-synthetic data demonstrate the effectiveness of our algorithm
Tetrakis(1-ethyl-3-methylimidazolium) β-hexacosaoxidooctamolybdate
The title compound, (C6H11N2)4[Mo8O26] or (emim)4[β-Mo8O26] (emim is 1-ethyl-3-methylimidazolium), was obtained from the ionic liquid [emim]BF4. The asymmetric unit contains two [emim]+ cations and one-half of the [β-Mo8O26]4− tetraanion, which occupies a special position on an inversion centre. The β-[Mo8O26]4− tetraanion features eight distorted MoO6 coordination octahedra linked together through bridging O atoms
DRIVE: Dockerfile Rule Mining and Violation Detection
A Dockerfile defines a set of instructions to build Docker images, which can
then be instantiated to support containerized applications. Recent studies have
revealed a considerable amount of quality issues with Dockerfiles. In this
paper, we propose a novel approach DRIVE (Dockerfiles Rule mIning and Violation
dEtection) to mine implicit rules and detect potential violations of such rules
in Dockerfiles. DRIVE firstly parses Dockerfiles and transforms them to an
intermediate representation. It then leverages an efficient sequential pattern
mining algorithm to extract potential patterns. With heuristic-based reduction
and moderate human intervention, potential rules are identified, which can then
be utilized to detect potential violations of Dockerfiles. DRIVE identifies 34
semantic rules and 19 syntactic rules including 9 new semantic rules which have
not been reported elsewhere. Extensive experiments on real-world Dockerfiles
demonstrate the efficacy of our approach
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