pwc-archive/papers-with-abstracts huggingface dataset:
Subword Encoding in Lattice LSTM for Chinese Word Segmentation
Authors: Jie Yang, Yue Zhang, Shuailong Liang
Date: 2018-10-30 00:00:00
Proceeding: NAACL 2019 6
Tasks: Chinese Word Segmentation, Word Embeddings

We investigate a lattice LSTM network for Chinese word segmentation (CWS) to
utilize words or subwords. It integrates the character sequence features with
all subsequences information matched from a lexicon. The matched subsequences
serve as information shortcut tunnels which link their start and end characters
directly. Gated units are used to control the contribution of multiple input
links. Through formula derivation and comparison, we show that the lattice LSTM
is an extension of the standard LSTM with the ability to take multiple inputs.
Previous lattice LSTM model takes word embeddings as the lexicon input, we
prove that subword encoding can give the comparable performance and has the
benefit of not relying on any external segmentor. The contribution of lattice
LSTM comes from both lexicon and pretrained embeddings information, we find
that the lexicon information contributes more than the pretrained embeddings
information through controlled experiments. Our experiments show that the
lattice structure with subword encoding gives competitive or better results
with previous state-of-the-art methods on four segmentation benchmarks.
Detailed analyses are conducted to compare the performance of word encoding and
subword encoding in lattice LSTM. We also investigate the performance of
lattice LSTM structure under different circumstances and when this model works
or fails.
DSKG: A Deep Sequential Model for Knowledge Graph Completion
Authors: Lingbing Guo, Qingheng Zhang, Weiyi Ge, Wei Hu, Yuzhong Qu
Date: 2018-10-30 00:00:00
Tasks: Knowledge Graph Completion, Relation

Knowledge graph (KG) completion aims to fill the missing facts in a KG, where
a fact is represented as a triple in the form of $(subject, relation, object)$.
Current KG completion models compel two-thirds of a triple provided (e.g.,
$subject$ and $relation$) to predict the remaining one. In this paper, we
propose a new model, which uses a KG-specific multi-layer recurrent neural
network (RNN) to model triples in a KG as sequences. It outperformed several
state-of-the-art KG completion models on the conventional entity prediction
task for many evaluation metrics, based on two benchmark datasets and a more
difficult dataset. Furthermore, our model is enabled by the sequential
characteristic and thus capable of predicting the whole triples only given one
entity. Our experiments demonstrated that our model achieved promising
performance on this new triple prediction task.
Exploring Neural Methods for Parsing Discourse Representation Structures
Authors: Rik van Noord, Lasha Abzianidze, Antonio Toral, Johan Bos
Date: 2018-10-30 00:00:00
Proceeding: TACL 2018 1
Tasks: DRS Parsing, Semantic Parsing

Neural methods have had several recent successes in semantic parsing, though
they have yet to face the challenge of producing meaning representations based
on formal semantics. We present a sequence-to-sequence neural semantic parser
that is able to produce Discourse Representation Structures (DRSs) for English
sentences with high accuracy, outperforming traditional DRS parsers. To
facilitate the learning of the output, we represent DRSs as a sequence of flat
clauses and introduce a method to verify that produced DRSs are well-formed and
interpretable. We compare models using characters and words as input and see
(somewhat surprisingly) that the former performs better than the latter. We
show that eliminating variable names from the output using De Bruijn-indices
increases parser performance. Adding silver training data boosts performance
even further.
Improved Network Robustness with Adversary Critic
Authors: Alexander Matyasko, Lap-Pui Chau
Date: 2018-10-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Adversarial Attack

Ideally, what confuses neural network should be confusing to humans. However,
recent experiments have shown that small, imperceptible perturbations can
change the network prediction. To address this gap in perception, we propose a
novel approach for learning robust classifier. Our main idea is: adversarial
examples for the robust classifier should be indistinguishable from the regular
data of the adversarial target. We formulate a problem of learning robust
classifier in the framework of Generative Adversarial Networks (GAN), where the
adversarial attack on classifier acts as a generator, and the critic network
learns to distinguish between regular and adversarial images. The classifier
cost is augmented with the objective that its adversarial examples should
confuse the adversary critic. To improve the stability of the adversarial
mapping, we introduce adversarial cycle-consistency constraint which ensures
that the adversarial mapping of the adversarial examples is close to the
original. In the experiments, we show the effectiveness of our defense. Our
method surpasses in terms of robustness networks trained with adversarial
training. Additionally, we verify in the experiments with human annotators on
MTurk that adversarial examples are indeed visually confusing. Codes for the
project are available at https://github.com/aam-at/adversary_critic.
Neural Nearest Neighbors Networks
Authors: Tobias Plötz, Stefan Roth
Date: 2018-10-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Denoising, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.
Rain Removal in Traffic Surveillance: Does it Matter?
Authors: Chris H. Bahnsen, Thomas B. Moeslund
Date: 2018-10-30 00:00:00
Tasks: Instance Segmentation, Rain Removal, Segmentation, Semantic Segmentation

Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.
Nonlinear Prediction of Multidimensional Signals via Deep Regression with Applications to Image Coding
Authors: Xi Zhang, Xiaolin Wu
Date: 2018-10-30 00:00:00
Tasks: Prediction, regression

Deep convolutional neural networks (DCNN) have enjoyed great successes in
many signal processing applications because they can learn complex, non-linear
causal relationships from input to output. In this light, DCNNs are well suited
for the task of sequential prediction of multidimensional signals, such as
images, and have the potential of improving the performance of traditional
linear predictors. In this research we investigate how far DCNNs can push the
envelop in terms of prediction precision. We propose, in a case study, a
two-stage deep regression DCNN framework for nonlinear prediction of
two-dimensional image signals. In the first-stage regression, the proposed deep
prediction network (PredNet) takes the causal context as input and emits a
prediction of the present pixel. Three PredNets are trained with the regression
objectives of minimizing $\ell_1$, $\ell_2$ and $\ell_\infty$ norms of
prediction residuals, respectively. The second-stage regression combines the
outputs of the three PredNets to generate an even more precise and robust
prediction. The proposed deep regression model is applied to lossless
predictive image coding, and it outperforms the state-of-the-art linear
predictors by appreciable margin.
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
Authors: Yi-Chen Chen, Chia-Hao Shen, Sung-Feng Huang, Hung-Yi Lee, Lin-shan Lee
Date: 2018-10-30 00:00:00
Tasks: speech-recognition, Speech Recognition, Unsupervised Speech Recognition

Producing a large amount of annotated speech data for training ASR systems
remains difficult for more than 95% of languages all over the world which are
low-resourced. However, we note human babies start to learn the language by the
sounds of a small number of exemplar words without hearing a large amount of
data. We initiate some preliminary work in this direction in this paper. Audio
Word2Vec is used to obtain embeddings of spoken words which carry phonetic
information extracted from the signals. An autoencoder is used to generate
embeddings of text words based on the articulatory features for the phoneme
sequences. Both sets of embeddings for spoken and text words describe similar
phonetic structures among words in their respective latent spaces. A mapping
relation from the audio embeddings to text embeddings actually gives the
word-level ASR. This can be learned by aligning a small number of spoken words
and the corresponding text words in the embedding spaces. In the initial
experiments only 200 annotated spoken words and one hour of speech data without
annotation gave a word accuracy of 27.5%, which is low but a good starting
point.
Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification
Authors: Haowen Luo
Date: 2018-10-30 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Convolutional neural networks (CNNs) attained a good performance in
hyperspectral sensing image (HSI) classification, but CNNs consider spectra as
orderless vectors. Therefore, considering the spectra as sequences, recurrent
neural networks (RNNs) have been applied in HSI classification, for RNNs is
skilled at dealing with sequential data. However, for a long-sequence task,
RNNs is difficult for training and not as effective as we expected. Besides,
spatial contextual features are not considered in RNNs. In this study, we
propose a Shorten Spatial-spectral RNN with Parallel-GRU (St-SS-pGRU) for HSI
classification. A shorten RNN is more efficient and easier for training than
band-by-band RNN. By combining converlusion layer, the St-SSpGRU model
considers not only spectral but also spatial feature, which results in a better
performance. An architecture named parallel-GRU is also proposed and applied in
St-SS-pGRU. With this architecture, the model gets a better performance and is
more robust.
Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning
Authors: Mahammad Humayoo, Gengzhong Zheng, Xiaoqing Dong, Liming Miao, Shuwei Qiu, Zexun Zhou, Peitao Wang, Zakir Ullah, Naveed Ur Rehman Junejo, Xueqi Cheng
Date: 2018-10-30 00:00:00
Tasks: Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Off-policy learning exhibits greater instability when compared to on-policy learning in reinforcement learning (RL). The difference in probability distribution between the target policy ($\pi$) and the behavior policy (b) is a major cause of instability. High variance also originates from distributional mismatch. The variation between the target policy's distribution and the behavior policy's distribution can be reduced using importance sampling (IS). However, importance sampling has high variance, which is exacerbated in sequential scenarios. We propose a smooth form of importance sampling, specifically relative importance sampling (RIS), which mitigates variance and stabilizes learning. To control variance, we alter the value of the smoothness parameter $\beta\in[0, 1]$ in RIS. We develop the first model-free relative importance sampling off-policy actor-critic (RIS-off-PAC) algorithms in RL using this strategy. Our method uses a network to generate the target policy (actor) and evaluate the current policy ($\pi$) using a value function (critic) based on behavior policy samples. Our algorithms are trained using behavior policy action values in the reward function, not target policy ones. Both the actor and critic are trained using deep neural networks. Our methods performed better than or equal to several state-of-the-art RL benchmarks on OpenAI Gym challenges and synthetic datasets.
Machine Translation between Vietnamese and English: an Empirical Study
Authors: Hong-Hai Phan-Vu, Viet-Trung Tran, Van-Nam Nguyen, Hoang-Vu Dang, Phan-Thuan Do
Date: 2018-10-30 00:00:00
Tasks: Machine Translation, Translation

Machine translation is shifting to an end-to-end approach based on deep
neural networks. The state of the art achieves impressive results for popular
language pairs such as English - French or English - Chinese. However for
English - Vietnamese the shortage of parallel corpora and expensive
hyper-parameter search present practical challenges to neural-based approaches.
This paper highlights our efforts on improving English-Vietnamese translations
in two directions: (1) Building the largest open Vietnamese - English corpus to
date, and (2) Extensive experiments with the latest neural models to achieve
the highest BLEU scores. Our experiments provide practical examples of
effectively employing different neural machine translation models with
low-resource language pairs.
Scale-Invariant Structure Saliency Selection for Fast Image Fusion
Authors: Yixiong Liang, Yuan Mao, Jiazhi Xia, Yao Xiang, Jianfeng Liu
Date: 2018-10-30 00:00:00

In this paper, we present a fast yet effective method for pixel-level
scale-invariant image fusion in spatial domain based on the scale-space theory.
Specifically, we propose a scale-invariant structure saliency selection scheme
based on the difference-of-Gaussian (DoG) pyramid of images to build the
weights or activity map. Due to the scale-invariant structure saliency
selection, our method can keep both details of small size objects and the
integrity information of large size objects in images. In addition, our method
is very efficient since there are no complex operation involved and easy to be
implemented and therefore can be used for fast high resolution images fusion.
Experimental results demonstrate the proposed method yields competitive or even
better results comparing to state-of-the-art image fusion methods both in terms
of visual quality and objective evaluation metrics. Furthermore, the proposed
method is very fast and can be used to fuse the high resolution images in
real-time. Code is available at https://github.com/yiqingmy/Fusion.
3D Traffic Simulation for Autonomous Vehicles in Unity and Python
Authors: Zhijing Jin, Tristan Swedish, Ramesh Raskar
Date: 2018-10-30 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, Position, Reinforcement Learning, Unity

Over the recent years, there has been an explosion of studies on autonomous vehicles. Many collected large amount of data from human drivers. However, compared to the tedious data collection approach, building a virtual simulation of traffic makes the autonomous vehicle research more flexible, time-saving, and scalable. Our work features a 3D simulation that takes in real time position information parsed from street cameras. The simulation can easily switch between a global bird view of the traffic and a local perspective of a car. It can also filter out certain objects in its customized camera, creating various channels for objects of different categories. This provides alternative supervised or unsupervised ways to train deep neural networks. Another advantage of the 3D simulation is its conformation to physical laws. Its naturalness to accelerate and collide prepares the system for potential deep reinforcement learning needs.
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
Authors: Biao Zhang, Deyi Xiong, Jinsong Su, Qian Lin, Huiji Zhang
Date: 2018-10-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Chinese Word Segmentation, Machine Translation, Natural Language Inference, Translation

In this paper, we propose an additionsubtraction twin-gated recurrent network
(ATR) to simplify neural machine translation. The recurrent units of ATR are
heavily simplified to have the smallest number of weight matrices among units
of all existing gated RNNs. With the simple addition and subtraction operation,
we introduce a twin-gated mechanism to build input and forget gates which are
highly correlated. Despite this simplification, the essential non-linearities
and capability of modeling long-distance dependencies are preserved.
Additionally, the proposed ATR is more transparent than LSTM/GRU due to the
simplification. Forward self-attention can be easily established in ATR, which
makes the proposed network interpretable. Experiments on WMT14 translation
tasks demonstrate that ATR-based neural machine translation can yield
competitive performance on English- German and English-French language pairs in
terms of both translation quality and speed. Further experiments on NIST
Chinese-English translation, natural language inference and Chinese word
segmentation verify the generality and applicability of ATR on different
natural language processing tasks.
Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities
Authors: Dong Huang, Chang-Dong Wang, Hongxing Peng, Jian-Huang Lai, Chee-Keong Kwoh
Date: 2018-10-30 00:00:00
Tasks: Clustering

Ensemble clustering has been a popular research topic in data mining and
machine learning. Despite its significant progress in recent years, there are
still two challenging issues in the current ensemble clustering research.
First, most of the existing algorithms tend to investigate the ensemble
information at the object-level, yet often lack the ability to explore the rich
information at higher levels of granularity. Second, they mostly focus on the
direct connections (e.g., direct intersection or pair-wise co-occurrence) in
the multiple base clusterings, but generally neglect the multi-scale indirect
relationship hidden in them. To address these two issues, this paper presents a
novel ensemble clustering approach based on fast propagation of cluster-wise
similarities via random walks. We first construct a cluster similarity graph
with the base clusters treated as graph nodes and the cluster-wise Jaccard
coefficient exploited to compute the initial edge weights. Upon the constructed
graph, a transition probability matrix is defined, based on which the random
walk process is conducted to propagate the graph structural information.
Specifically, by investigating the propagating trajectories starting from
different nodes, a new cluster-wise similarity matrix can be derived by
considering the trajectory relationship. Then, the newly obtained cluster-wise
similarity matrix is mapped from the cluster-level to the object-level to
achieve an enhanced co-association (ECA) matrix, which is able to
simultaneously capture the object-wise co-occurrence relationship as well as
the multi-scale cluster-wise relationship in ensembles. Finally, two novel
consensus functions are proposed to obtain the consensus clustering result.
Extensive experiments on a variety of real-world datasets have demonstrated the
effectiveness and efficiency of our approach.
Gated Hierarchical Attention for Image Captioning
Authors: Qingzhong Wang, Antoni B. Chan
Date: 2018-10-30 00:00:00
Tasks: Decoder, Image Captioning, Reinforcement Learning, Reinforcement Learning (RL), Translation, Visual Question Answering

Attention modules connecting encoder and decoders have been widely applied in
the field of object recognition, image captioning, visual question answering
and neural machine translation, and significantly improves the performance. In
this paper, we propose a bottom-up gated hierarchical attention (GHA) mechanism
for image captioning. Our proposed model employs a CNN as the decoder which is
able to learn different concepts at different layers, and apparently, different
concepts correspond to different areas of an image. Therefore, we develop the
GHA in which low-level concepts are merged into high-level concepts and
simultaneously low-level attended features pass to the top to make predictions.
Our GHA significantly improves the performance of the model that only applies
one level attention, for example, the CIDEr score increases from 0.923 to
0.999, which is comparable to the state-of-the-art models that employ
attributes boosting and reinforcement learning (RL). We also conduct extensive
experiments to analyze the CNN decoder and our proposed GHA, and we find that
deeper decoders cannot obtain better performance, and when the convolutional
decoder becomes deeper the model is likely to collapse during training.
Fully automatic structure from motion with a spline-based environment representation
Authors: Zhi-Rui Wang, Laurent Kneip
Date: 2018-10-30 00:00:00

While the common environment representation in structure from motion is given
by a sparse point cloud, the community has also investigated the use of lines
to better enforce the inherent regularities in man-made surroundings. Following
the potential of this idea, the present paper introduces a more flexible
higher-order extension of points that provides a general model for structural
edges in the environment, no matter if straight or curved. Our model relies on
linked B\'ezier curves, the geometric intuition of which proves great benefits
during parameter initialization and regularization. We present the first fully
automatic pipeline that is able to generate spline-based representations
without any human supervision. Besides a full graphical formulation of the
problem, we introduce both geometric and photometric cues as well as
higher-level concepts such overall curve visibility and viewing angle
restrictions to automatically manage the correspondences in the graph. Results
prove that curve-based structure from motion with splines is able to outperform
state-of-the-art sparse feature-based methods, as well as to model curved edges
in the environment.