365 research outputs found
Dilated Deep Residual Network for Image Denoising
Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoising have many layers. In such cases, the models involve a large
amount of parameters and are computationally expensive to train. In this paper,
we develop a dilated residual CNN for Gaussian image denoising. Compared with
the recently proposed residual denoiser, our method can achieve comparable
performance with less computational cost. Specifically, we enlarge receptive
field by adopting dilated convolution in residual network, and the dilation
factor is set to a certain value. We utilize appropriate zero padding to make
the dimension of the output the same as the input. It has been proven that the
expansion of receptive field can boost the CNN performance in image
classification, and we further demonstrate that it can also lead to competitive
performance for denoising problem. Moreover, we present a formula to calculate
receptive field size when dilated convolution is incorporated. Thus, the change
of receptive field can be interpreted mathematically. To validate the efficacy
of our approach, we conduct extensive experiments for both gray and color image
denoising with specific or randomized noise levels. Both of the quantitative
measurements and the visual results of denoising are promising comparing with
state-of-the-art baselines.Comment: camera ready, 8 pages, accepted to IEEE ICTAI 201
Deep Neural Machine Translation with Linear Associative Unit
Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art
Neural Machine Translation (NMT) with their capability in modeling complex
functions and capturing complex linguistic structures. However NMT systems with
deep architecture in their encoder or decoder RNNs often suffer from severe
gradient diffusion due to the non-linear recurrent activations, which often
make the optimization much more difficult. To address this problem we propose
novel linear associative units (LAU) to reduce the gradient propagation length
inside the recurrent unit. Different from conventional approaches (LSTM unit
and GRU), LAUs utilizes linear associative connections between input and output
of the recurrent unit, which allows unimpeded information flow through both
space and time direction. The model is quite simple, but it is surprisingly
effective. Our empirical study on Chinese-English translation shows that our
model with proper configuration can improve by 11.7 BLEU upon Groundhog and the
best reported results in the same setting. On WMT14 English-German task and a
larger WMT14 English-French task, our model achieves comparable results with
the state-of-the-art.Comment: 10 pages, ACL 201
Memory-enhanced Decoder for Neural Machine Translation
We propose to enhance the RNN decoder in a neural machine translator (NMT)
with external memory, as a natural but powerful extension to the state in the
decoding RNN. This memory-enhanced RNN decoder is called \textsc{MemDec}. At
each time during decoding, \textsc{MemDec} will read from this memory and write
to this memory once, both with content-based addressing. Unlike the unbounded
memory in previous work\cite{RNNsearch} to store the representation of source
sentence, the memory in \textsc{MemDec} is a matrix with pre-determined size
designed to better capture the information important for the decoding process
at each time step. Our empirical study on Chinese-English translation shows
that it can improve by BLEU upon Groundhog and BLEU upon on Moses,
yielding the best performance achieved with the same training set.Comment: 11 page
KINEMATIC ANALYSIS OF SHOT PUT IN ELITE ATHLETES – A CASE STUDY
This paper presented the application of biomechanics in the shot put. Three elite shot-putters was video recorded. By planar analysis, the following kinematic data have been discussed: (1) the loss of distance in performances, (2) the swinging span of the leg, (3) the height of the shot before the last effort, (4) the waving manner of the swinging arm, and (5) the influence of the differences between the velocity angle of the released shot and its optimum angle. The effects of the measured values of above parameters on performances and their mechanic causes were analyzed. The results of this study provided the information for improvement of performance in athletes
Structuring, Integrating and Innovating: The Training Mode of Master of Public Administration (MPA)—Based on the Example of Chongqing
Master of Public Administration (MPA) is a specialized degree especially for governments and non-governmental public sectors to train high-level, high quality, professional public administration talents. This paper analyses the background of the training mode of MPA and determines the implication and consisting factors, and concludes the current situation of the training mode of MPA. Through the SWOT analysis of samples in universities in Chongqing, it confirms the major factors and interaction pattern and constructs the SPAC systematic framework. Then it analysis the systematic mode and dynamic innovation of MPA and verifies the feasibility of them based on the example of Southwest University
Innovation and Development in Basic Level Party Construction for Universities With “Four Building” System
Basic level party organizations in universities are thefoundation and guarantee for educational principlesand policies of the party. This paper expounds therole of basic level party organizations in collegesand universities, including the guidance of educationmanagement, coordinating and balancing the interestsand providing service guarantee. Current situation andproblems of basic level party construction in collegesand universities are analyzed with the actual surveydata. Main problems are that team construction isnot harmonious, scientific and effective managementsystem is scarce, which reasons include the influenceof social environment, the backward of strategicplanning, and the faultiness of incentive mechanism.Accordingly, on the basis of systematic analysis, weput forward the “four building”, namely “to build theright strategic concept and values, to build the partymembers’ long-term education training mode, to build ascientific performance evaluation model and build openand clear communication platform” so as to promotethe innovation and development of basic level partyconstruction in colleges and universities. Finally, baseon the perspective of strategic management, we shape“1+4” strategic framework of the basic level partyconstruction in colleges and universities and stressthe “four construction” system integration to realizethe innovation and development of basic level partyconstruction
Deep Semantic Role Labeling with Self-Attention
Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end
SRL with recurrent neural networks (RNN) has gained increasing attention.
However, it remains a major challenge for RNNs to handle structural information
and long range dependencies. In this paper, we present a simple and effective
architecture for SRL which aims to address these problems. Our model is based
on self-attention which can directly capture the relationships between two
tokens regardless of their distance. Our single model achieves F on
the CoNLL-2005 shared task dataset and F on the CoNLL-2012 shared task
dataset, which outperforms the previous state-of-the-art results by and
F score respectively. Besides, our model is computationally
efficient, and the parsing speed is 50K tokens per second on a single Titan X
GPU.Comment: Accepted by AAAI-201
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