pwc-archive/papers-with-abstracts huggingface dataset:
Parsimonious HMMs for Offline Handwritten Chinese Text Recognition
Authors: Wenchao Wang, Jun Du, Zi-Rui Wang
Date: 2018-08-13 00:00:00
Tasks: Handwritten Chinese Text Recognition

Recently, hidden Markov models (HMMs) have achieved promising results for
offline handwritten Chinese text recognition. However, due to the large
vocabulary of Chinese characters with each modeled by a uniform and fixed
number of hidden states, a high demand of memory and computation is required.
In this study, to address this issue, we present parsimonious HMMs via the
state tying which can fully utilize the similarities among different Chinese
characters. Two-step algorithm with the data-driven question-set is adopted to
generate the tied-state pool using the likelihood measure. The proposed
parsimonious HMMs with both Gaussian mixture models (GMMs) and deep neural
networks (DNNs) as the emission distributions not only lead to a compact model
but also improve the recognition accuracy via the data sharing for the tied
states and the confusion decreasing among state classes. Tested on ICDAR-2013
competition database, in the best configured case, the new parsimonious DNN-HMM
can yield a relative character error rate (CER) reduction of 6.2%, 25%
reduction of model size and 60% reduction of decoding time over the
conventional DNN-HMM. In the compact setting case of average 1-state HMM, our
parsimonious DNN-HMM significantly outperforms the conventional DNN-HMM with a
relative CER reduction of 35.5%.
DenseRAN for Offline Handwritten Chinese Character Recognition
Authors: Wenchao Wang, Jianshu Zhang, Jun Du, Zi-Rui Wang, Yixing Zhu
Date: 2018-08-13 00:00:00
Tasks: Decoder, Offline Handwritten Chinese Character Recognition

Recently, great success has been achieved in offline handwritten Chinese
character recognition by using deep learning methods. Chinese characters are
mainly logographic and consist of basic radicals, however, previous research
mostly treated each Chinese character as a whole without explicitly considering
its internal two-dimensional structure and radicals. In this study, we propose
a novel radical analysis network with densely connected architecture (DenseRAN)
to analyze Chinese character radicals and its two-dimensional structures
simultaneously. DenseRAN first encodes input image to high-level visual
features by employing DenseNet as an encoder. Then a decoder based on recurrent
neural networks is employed, aiming at generating captions of Chinese
characters by detecting radicals and two-dimensional structures through
attention mechanism. The manner of treating a Chinese character as a
composition of two-dimensional structures and radicals can reduce the size of
vocabulary and enable DenseRAN to possess the capability of recognizing unseen
Chinese character classes, only if the corresponding radicals have been seen in
training set. Evaluated on ICDAR-2013 competition database, the proposed
approach significantly outperforms whole-character modeling approach with a
relative character error rate (CER) reduction of 18.54%. Meanwhile, for the
case of recognizing 3277 unseen Chinese characters in CASIA-HWDB1.2 database,
DenseRAN can achieve a character accuracy of about 41% while the traditional
whole-character method has no capability to handle them.
Learning Explanations from Language Data
Authors: David Harbecke, Robert Schwarzenberg, Christoph Alt
Date: 2018-08-13 00:00:00
Proceeding: WS 2018 11

PatternAttribution is a recent method, introduced in the vision domain, that
explains classifications of deep neural networks. We demonstrate that it also
generates meaningful interpretations in the language domain.
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
Authors: Daniil Sorokin, Iryna Gurevych
Date: 2018-08-13 00:00:00
Proceeding: COLING 2018 8
Tasks: Knowledge Base Question Answering, Question Answering, Semantic Parsing

The most approaches to Knowledge Base Question Answering are based on
semantic parsing. In this paper, we address the problem of learning vector
representations for complex semantic parses that consist of multiple entities
and relations. Previous work largely focused on selecting the correct semantic
relations for a question and disregarded the structure of the semantic parse:
the connections between entities and the directions of the relations. We
propose to use Gated Graph Neural Networks to encode the graph structure of the
semantic parse. We show on two data sets that the graph networks outperform all
baseline models that do not explicitly model the structure. The error analysis
confirms that our approach can successfully process complex semantic parses.
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Authors: Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
Date: 2018-08-13 00:00:00
Proceeding: NAACL 2019 6
Tasks: Knowledge Graph Completion, Link Prediction

In this paper, we introduce an embedding model, named CapsE, exploring a
capsule network to model relationship triples (subject, relation, object). Our
CapsE represents each triple as a 3-column matrix where each column vector
represents the embedding of an element in the triple. This 3-column matrix is
then fed to a convolution layer where multiple filters are operated to generate
different feature maps. These feature maps are reconstructed into corresponding
capsules which are then routed to another capsule to produce a continuous
vector. The length of this vector is used to measure the plausibility score of
the triple. Our proposed CapsE obtains better performance than previous
state-of-the-art embedding models for knowledge graph completion on two
benchmark datasets WN18RR and FB15k-237, and outperforms strong search
personalization baselines on SEARCH17.
Towards Audio to Scene Image Synthesis using Generative Adversarial Network
Authors: Chia-Hung Wan, Shun-Po Chuang, Hung-Yi Lee
Date: 2018-08-13 00:00:00
Tasks: Generative Adversarial Network, Image Generation

Humans can imagine a scene from a sound. We want machines to do so by using
conditional generative adversarial networks (GANs). By applying the techniques
including spectral norm, projection discriminator and auxiliary classifier,
compared with naive conditional GAN, the model can generate images with better
quality in terms of both subjective and objective evaluations. Almost
three-fourth of people agree that our model have the ability to generate images
related to sounds. By inputting different volumes of the same sound, our model
output different scales of changes based on the volumes, showing that our model
truly knows the relationship between sounds and images to some extent.
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Authors: Hélène Plisnier, Denis Steckelmacher, Tim Brys, Diederik M. Roijers, Ann Nowé
Date: 2018-08-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Many currently deployed Reinforcement Learning agents work in an environment
shared with humans, be them co-workers, users or clients. It is desirable that
these agents adjust to people's preferences, learn faster thanks to their help,
and act safely around them. We argue that most current approaches that learn
from human feedback are unsafe: rewarding or punishing the agent a-posteriori
cannot immediately prevent it from wrong-doing. In this paper, we extend Policy
Gradient to make it robust to external directives, that would otherwise break
the fundamentally on-policy nature of Policy Gradient. Our technique, Directed
Policy Gradient (DPG), allows a teacher or backup policy to override the agent
before it acts undesirably, while allowing the agent to leverage human advice
or directives to learn faster. Our experiments demonstrate that DPG makes the
agent learn much faster than reward-based approaches, while requiring an order
of magnitude less advice.
Live Video Comment Generation Based on Surrounding Frames and Live Comments
Authors: Damai Dai
Date: 2018-08-13 00:00:00
Tasks: Comment Generation

In this paper, we propose the task of live comment generation. Live comments
are a new form of comments on videos, which can be regarded as a mixture of
comments and chats. A high-quality live comment should be not only relevant to
the video, but also interactive with other users. In this work, we first
construct a new dataset for live comment generation. Then, we propose a novel
end-to-end model to generate the human-like live comments by referring to the
video and the other users' comments. Finally, we evaluate our model on the
constructed dataset. Experimental results show that our method can
significantly outperform the baselines.
Language Style Transfer from Sentences with Arbitrary Unknown Styles
Authors: Yanpeng Zhao, Wei Bi, Deng Cai, Xiaojiang Liu, Kewei Tu, Shuming Shi
Date: 2018-08-13 00:00:00
Tasks: Sentence, Sentence ReWriting, Style Transfer

Language style transfer is the problem of migrating the content of a source
sentence to a target style. In many of its applications, parallel training data
are not available and source sentences to be transferred may have arbitrary and
unknown styles. First, each sentence is encoded into its content and style
latent representations. Then, by recombining the content with the target style,
we decode a sentence aligned in the target domain. To adequately constrain the
encoding and decoding functions, we couple them with two loss functions. The
first is a style discrepancy loss, enforcing that the style representation
accurately encodes the style information guided by the discrepancy between the
sentence style and the target style. The second is a cycle consistency loss,
which ensures that the transferred sentence should preserve the content of the
original sentence disentangled from its style. We validate the effectiveness of
our model in three tasks: sentiment modification of restaurant reviews, dialog
response revision with a romantic style, and sentence rewriting with a
Shakespearean style.
A Transfer Learning based Feature-Weak-Relevant Method for Image Clustering
Authors: Bo Dong, Xinnian Wang
Date: 2018-08-13 00:00:00
Tasks: Clustering, Image Clustering, Transfer Learning

Image clustering is to group a set of images into disjoint clusters in a way
that images in the same cluster are more similar to each other than to those in
other clusters, which is an unsupervised or semi-supervised learning process.
It is a crucial and challenging task in machine learning and computer vision.
The performances of existing image clustering methods have close relations with
features used for clustering, even if unsupervised coding based methods have
improved the performances a lot. To reduce the effect of clustering features,
we propose a feature-weak-relevant method for image clustering. The proposed
method converts an unsupervised clustering process into an alternative
iterative process of unsupervised learning and transfer learning. The
clustering process firstly starts up from handcrafted features based image
clustering to estimate an initial label for every image, and secondly use a
proposed sampling strategy to choose images with reliable labels to feed a
transfer-learning model to learn representative features that can be used for
next round of unsupervised learning. In this manner, image clustering is
iteratively optimized. What's more, the handcrafted features are used to boot
up the clustering process, and just have a little effect on the final
performance; therefore, the proposed method is feature-weak-relevant.
Experimental results on six kinds of public available datasets show that the
proposed method outperforms state of the art methods and depends less on the
employed features at the same time.
Regularizing Neural Machine Translation by Target-bidirectional Agreement
Authors: Zhirui Zhang, Shuangzhi Wu, Shujie Liu, Mu Li, Ming Zhou, Tong Xu
Date: 2018-08-13 00:00:00
Tasks: Machine Translation, NMT, Translation

Although Neural Machine Translation (NMT) has achieved remarkable progress in
the past several years, most NMT systems still suffer from a fundamental
shortcoming as in other sequence generation tasks: errors made early in
generation process are fed as inputs to the model and can be quickly amplified,
harming subsequent sequence generation. To address this issue, we propose a
novel model regularization method for NMT training, which aims to improve the
agreement between translations generated by left-to-right (L2R) and
right-to-left (R2L) NMT decoders. This goal is achieved by introducing two
Kullback-Leibler divergence regularization terms into the NMT training
objective to reduce the mismatch between output probabilities of L2R and R2L
models. In addition, we also employ a joint training strategy to allow L2R and
R2L models to improve each other in an interactive update process. Experimental
results show that our proposed method significantly outperforms
state-of-the-art baselines on Chinese-English and English-German translation
tasks.
Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction
Authors: Yatao Zhong, Bicheng Xu, Guang-Tong Zhou, Luke Bornn, Greg Mori
Date: 2018-08-13 00:00:00
Tasks: Activity Prediction, Point Processes

Numerous powerful point process models have been developed to understand
temporal patterns in sequential data from fields such as health-care,
electronic commerce, social networks, and natural disaster forecasting. In this
paper, we develop novel models for learning the temporal distribution of human
activities in streaming data (e.g., videos and person trajectories). We propose
an integrated framework of neural networks and temporal point processes for
predicting when the next activity will happen. Because point processes are
limited to taking event frames as input, we propose a simple yet effective
mechanism to extract features at frames of interest while also preserving the
rich information in the remaining frames. We evaluate our model on two
challenging datasets. The results show that our model outperforms traditional
statistical point process approaches significantly, demonstrating its
effectiveness in capturing the underlying temporal dynamics as well as the
correlation within sequential activities. Furthermore, we also extend our model
to a joint estimation framework for predicting the timing, spatial location,
and category of the activity simultaneously, to answer the when, where, and
what of activity prediction.
A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM
Authors: Guilherme França, Daniel P. Robinson, René Vidal
Date: 2018-08-13 00:00:00

Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth and unconstrained problems. In this paper we extend this perspective to nonsmooth and constrained problems by obtaining differential inclusions associated to novel accelerated variants of the alternating direction method of multipliers (ADMM). Through a Lyapunov analysis, we derive rates of convergence for these dynamical systems in different settings that illustrate an interesting tradeoff between decaying versus constant damping strategies. We also obtain modified equations capturing fine-grained details of these methods, which have improved stability and preserve the leading order convergence rates. An extension to general nonlinear equality and inequality constraints in connection with singular perturbation theory is provided.
Faster and More Robust Mesh-based Algorithms for Obstacle k-Nearest Neighbour
Authors: Shizhe Zhao, Daniel D. Harabor, David Taniar
Date: 2018-08-13 00:00:00

We are interested in the problem of finding $k$ nearest neighbours in the
plane and in the presence of polygonal obstacles ($\textit{OkNN}$). Widely used
algorithms for OkNN are based on incremental visibility graphs, which means
they require costly and online visibility checking and have worst-case
quadratic running time. Recently $\mathbf{Polyanya}$, a fast point-to-point
pathfinding algorithm was proposed which avoids the disadvantages of visibility
graphs by searching over an alternative data structure known as a navigation
mesh. Previously, we adapted $\mathbf{Polyanya}$ to multi-target scenarios by
developing two specialised heuristic functions: the $\mathbf{Interval
heuristic}$ $h_v$ and the $\mathbf{Target heuristic}$ $h_t$. Though these
methods outperform visibility graph algorithms by orders of magnitude in all
our experiments they are not robust: $h_v$ expands many redundant nodes when
the set of neighbours is small while $h_t$ performs poorly when the set of
neighbours is large. In this paper, we propose new algorithms and heuristics
for OkNN which perform well regardless of neighbour density.
Confidence penalty, annealing Gaussian noise and zoneout for biLSTM-CRF networks for named entity recognition
Authors: Antonio Jimeno Yepes
Date: 2018-08-13 00:00:00
Tasks: Decoder, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named entity recognition (NER) is used to identify relevant entities in text.
A bidirectional LSTM (long short term memory) encoder with a neural conditional
random fields (CRF) decoder (biLSTM-CRF) is the state of the art methodology.
In this work, we have done an analysis of several methods that intend to
optimize the performance of networks based on this architecture, which in some
cases encourage overfitting avoidance. These methods target exploration of
parameter space, regularization of LSTMs and penalization of confident output
distributions. Results show that the optimization methods improve the
performance of the biLSTM-CRF NER baseline system, setting a new state of the
art performance for the CoNLL-2003 Spanish set with an F1 of 87.18.
3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes
Authors: Yiran Zhong, Yuchao Dai, Hongdong Li
Date: 2018-08-13 00:00:00
Tasks: 3D geometry

This paper is concerned with the problem of how to better exploit 3D
geometric information for dense semantic image labeling. Existing methods often
treat the available 3D geometry information (e.g., 3D depth-map) simply as an
additional image channel besides the R-G-B color channels, and apply the same
technique for RGB image labeling. In this paper, we demonstrate that directly
performing 3D convolution in the framework of a residual connected 3D voxel
top-down modulation network can lead to superior results. Specifically, we
propose a 3D semantic labeling method to label outdoor street scenes whenever a
dense depth map is available. Experiments on the "Synthia" and "Cityscape"
datasets show our method outperforms the state-of-the-art methods, suggesting
such a simple 3D representation is effective in incorporating 3D geometric
information.
Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains
Authors: Thach Le Nguyen, Severin Gsponer, Iulia Ilie, Georgiana Ifrim
Date: 2018-08-12 00:00:00
Tasks: All, Classification, feature selection, General Classification, Time Series, Time Series Analysis, Time Series Classification

The time series classification literature has expanded rapidly over the last
decade, with many new classification approaches published each year. The
research focus has mostly been on improving the accuracy and efficiency of
classifiers, while their interpretability has been somewhat neglected.
Classifier interpretability has become a critical constraint for many
application domains and the introduction of the 'right to explanation' GDPR EU
legislation in May 2018 is likely to further emphasize the importance of
explainable learning algorithms. In this work we analyse the state-of-the-art
for time series classification, and propose new algorithms that aim to maintain
the classifier accuracy and efficiency, but keep interpretability as a key
design constraint. We present new time series classification algorithms that
advance the state-of-the-art by implementing the following three key ideas: (1)
Multiple resolutions of symbolic approximations: we combine symbolic
representations obtained using different parameters; (2) Multiple domain
representations: we combine symbolic approximations in time (e.g., SAX) and
frequency (e.g., SFA) domains; (3) Efficient navigation of a huge
symbolic-words space: we adapt a symbolic sequence classifier named SEQL, to
make it work with multiple domain representations (e.g., SAX-SEQL, SFA-SEQL),
and use its greedy feature selection strategy to effectively filter the best
features for each representation. We show that a multi-resolution multi-domain
linear classifier, SAX-SFA-SEQL, achieves a similar accuracy to the
state-of-the-art COTE ensemble, and to a recent deep learning method (FCN), but
uses a fraction of the time required by either COTE or FCN. We discuss the
accuracy, efficiency and interpretability of our proposed algorithms. To
further analyse the interpretability aspect of our classifiers, we present a
case study on an ecology benchmark.
Scene-LSTM: A Model for Human Trajectory Prediction
Authors: Huynh Manh, Gita Alaghband
Date: 2018-08-12 00:00:00
Tasks: model, Prediction, Trajectory Prediction

We develop a human movement trajectory prediction system that incorporates
the scene information (Scene-LSTM) as well as human movement trajectories
(Pedestrian movement LSTM) in the prediction process within static crowded
scenes. We superimpose a two-level grid structure (scene is divided into grid
cells each modeled by a scene-LSTM, which are further divided into smaller
sub-grids for finer spatial granularity) and explore common human trajectories
occurring in the grid cell (e.g., making a right or left turn onto sidewalks
coming out of an alley; or standing still at bus/train stops). Two coupled LSTM
networks, Pedestrian movement LSTMs (one per target) and the corresponding
Scene-LSTMs (one per grid-cell) are trained simultaneously to predict the next
movements. We show that such common path information greatly influences
prediction of future movement. We further design a scene data filter that holds
important non-linear movement information. The scene data filter allows us to
select the relevant parts of the information from the grid cell's memory
relative to a target's state. We evaluate and compare two versions of our
method with the Linear and several existing LSTM-based methods on five crowded
video sequences from the UCY [1] and ETH [2] datasets. The results show that
our method reduces the location displacement errors compared to related methods
and specifically about 80% reduction compared to social interaction methods.