pwc-archive/papers-with-abstracts huggingface dataset:
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Authors: Yourui Huangfu, Jian Wang, Rong Li, Chen Xu, Xianbin Wang, Huazi Zhang, Jun Wang
Date: 2019-01-14 00:00:00
Tasks: Caption Generation, Language Modeling, Language Modelling, Machine Translation, Prediction, Text Summarization, Time Series, Time Series Analysis, Time Series Prediction, Translation

Accurate prediction of fading channel in future is essential to realize
adaptive transmission and other methods that can save power and provide gains.
In practice, wireless channel model can be regarded as a new language model,
and the time-varying channel can be seen as mumbling in this language, which is
too complex to understand, to say nothing of prediction. Fortunately, neural
networks have been proved efficient in learning language models in recent
times, moreover, sequence-to-sequence (seq2seq) models can provide the state of
the art performance in various tasks such as machine translation, image caption
generation, and text summarization. Predicting channel with neural networks
seems promising, however, vanilla neural networks cannot deal with
complex-valued inputs while channel state information (CSI) is in complex
domain. In this paper, we present a powerful method to understand and predict
complex-valued channel by utilizing seq2seq models, the results show that
seq2seq models are also expert in time series prediction, and realistic channel
prediction with comparable or superior performance relative to channel
estimation is attainable.
Unsupervised Neural Machine Translation with SMT as Posterior Regularization
Authors: Shuo Ren, Zhirui Zhang, Shujie Liu, Ming Zhou, Shuai Ma
Date: 2019-01-14 00:00:00
Tasks: Machine Translation, NMT, Translation, Unsupervised Machine Translation

Without real bilingual corpus available, unsupervised Neural Machine
Translation (NMT) typically requires pseudo parallel data generated with the
back-translation method for the model training. However, due to weak
supervision, the pseudo data inevitably contain noises and errors that will be
accumulated and reinforced in the subsequent training process, leading to bad
translation performance. To address this issue, we introduce phrase based
Statistic Machine Translation (SMT) models which are robust to noisy data, as
posterior regularizations to guide the training of unsupervised NMT models in
the iterative back-translation process. Our method starts from SMT models built
with pre-trained language models and word-level translation tables inferred
from cross-lingual embeddings. Then SMT and NMT models are optimized jointly
and boost each other incrementally in a unified EM framework. In this way, (1)
the negative effect caused by errors in the iterative back-translation process
can be alleviated timely by SMT filtering noises from its phrase tables;
meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in
SMT. Experiments conducted on en-fr and en-de translation tasks show that our
method outperforms the strong baseline and achieves new state-of-the-art
unsupervised machine translation performance.
Fast and Robust Multi-Person 3D Pose Estimation from Multiple Views
Authors: Junting Dong, Wen Jiang, Qi-Xing Huang, Hujun Bao, Xiaowei Zhou
Date: 2019-01-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Multi-Person Pose Estimation, 3D Pose Estimation, Pose Estimation

This paper addresses the problem of 3D pose estimation for multiple people in
a few calibrated camera views. The main challenge of this problem is to find
the cross-view correspondences among noisy and incomplete 2D pose predictions.
Most previous methods address this challenge by directly reasoning in 3D using
a pictorial structure model, which is inefficient due to the huge state space.
We propose a fast and robust approach to solve this problem. Our key idea is to
use a multi-way matching algorithm to cluster the detected 2D poses in all
views. Each resulting cluster encodes 2D poses of the same person across
different views and consistent correspondences across the keypoints, from which
the 3D pose of each person can be effectively inferred. The proposed convex
optimization based multi-way matching algorithm is efficient and robust against
missing and false detections, without knowing the number of people in the
scene. Moreover, we propose to combine geometric and appearance cues for
cross-view matching. The proposed approach achieves significant performance
gains from the state-of-the-art (96.3% vs. 90.6% and 96.9% vs. 88% on the
Campus and Shelf datasets, respectively), while being efficient for real-time
applications.
Search Efficient Binary Network Embedding
Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
Date: 2019-01-14 00:00:00
Tasks: Attribute, Network Embedding, Node Classification, Node Clustering

Traditional network embedding primarily focuses on learning a continuous vector representation for each node, preserving network structure and/or node content information, such that off-the-shelf machine learning algorithms can be easily applied to the vector-format node representations for network analysis. However, the learned continuous vector representations are inefficient for large-scale similarity search, which often involves finding nearest neighbors measured by distance or similarity in a continuous vector space. In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network. BinaryNE learns binary node representations through a stochastic gradient descent based online learning algorithm. The learned binary encoding not only reduces memory usage to represent each node, but also allows fast bit-wise comparisons to support faster node similarity search than using Euclidean distance or other distance measures. Extensive experiments and comparisons demonstrate that BinaryNE not only delivers more than 25 times faster search speed, but also provides comparable or better search quality than traditional continuous vector based network embedding methods. The binary codes learned by BinaryNE also render competitive performance on node classification and node clustering tasks. The source code of this paper is available at https://github.com/daokunzhang/BinaryNE.
Attributed Network Embedding via Subspace Discovery
Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
Date: 2019-01-14 00:00:00
Tasks: Attribute, Clustering, Link Prediction, Network Embedding, Node Classification, Node Clustering

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks. The source code of this paper is available at https://github.com/daokunzhang/attri2vec.
Vehicles Detection Based on Background Modeling
Authors: Mohamed Shehata, Reda Abo-Al-Ez, Farid Zaghlool, Mohamed Taha Abou-Kreisha
Date: 2019-01-13 00:00:00
Tasks: vehicle detection

Background image subtraction algorithm is a common approach which detects
moving objects in a video sequence by finding the significant difference
between the video frames and the static background model. This paper presents a
developed system which achieves vehicle detection by using background image
subtraction algorithm based on blocks followed by deep learning data validation
algorithm. The main idea is to segment the image into equal size blocks, to
model the static reference background image (SRBI), by calculating the variance
between each block pixels and each counterpart block pixels in the adjacent
frame, the system implemented into four different methods: Absolute Difference,
Image Entropy, Exclusive OR (XOR) and Discrete Cosine Transform (DCT). The
experimental results showed that the DCT method has the highest vehicle
detection accuracy.
Gradient Regularized Budgeted Boosting
Authors: Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng
Date: 2019-01-13 00:00:00

As machine learning transitions increasingly towards real world applications
controlling the test-time cost of algorithms becomes more and more crucial.
Recent work, such as the Greedy Miser and Speedboost, incorporate test-time
budget constraints into the training procedure and learn classifiers that
provably stay within budget (in expectation). However, so far, these algorithms
are limited to the supervised learning scenario where sufficient amounts of
labeled data are available. In this paper we investigate the common scenario
where labeled data is scarce but unlabeled data is available in abundance. We
propose an algorithm that leverages the unlabeled data (through Laplace
smoothing) and learns classifiers with budget constraints. Our model, based on
gradient boosted regression trees (GBRT), is, to our knowledge, the first
algorithm for semi-supervised budgeted learning.
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Authors: Zhaoyang Xu, Xingru Huang, Carlos Fernández Moro, Béla Bozóky, Qianni Zhang
Date: 2019-01-13 00:00:00
Tasks: Translation

Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques.
Gradient Boosted Feature Selection
Authors: Zhixiang Eddie Xu, Gao Huang, Kilian Q. Weinberger, Alice X. Zheng
Date: 2019-01-13 00:00:00
Tasks: feature selection

A feature selection algorithm should ideally satisfy four conditions:
reliably extract relevant features; be able to identify non-linear feature
interactions; scale linearly with the number of features and dimensions; allow
the incorporation of known sparsity structure. In this work we propose a novel
feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which
satisfies all four of these requirements. The algorithm is flexible, scalable,
and surprisingly straight-forward to implement as it is based on a modification
of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and
show that it matches or out-performs other state of the art feature selection
algorithms. Yet it scales to larger data set sizes and naturally allows for
domain-specific side information.
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Authors: Kasun Bandara, Peibei Shi, Christoph Bergmeir, Hansika Hewamalage, Quoc Tran, Brian Seaman
Date: 2019-01-13 00:00:00
Tasks: Time Series, Time Series Analysis

Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the non-linear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting framework, we also propose a systematic pre-processing framework to overcome the challenges in the E-commerce business. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart.com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming state-of-the-art techniques.
Neumann Networks for Inverse Problems in Imaging
Authors: Davis Gilton, Greg Ongie, Rebecca Willett
Date: 2019-01-13 00:00:00
Tasks: compressed sensing, Deblurring

Many challenging image processing tasks can be described by an ill-posed linear inverse problem: deblurring, deconvolution, inpainting, compressed sensing, and superresolution all lie in this framework. Traditional inverse problem solvers minimize a cost function consisting of a data-fit term, which measures how well an image matches the observations, and a regularizer, which reflects prior knowledge and promotes images with desirable properties like smoothness. Recent advances in machine learning and image processing have illustrated that it is often possible to learn a regularizer from training data that can outperform more traditional regularizers. We present an end-to-end, data-driven method of solving inverse problems inspired by the Neumann series, which we call a Neumann network. Rather than unroll an iterative optimization algorithm, we truncate a Neumann series which directly solves the linear inverse problem with a data-driven nonlinear regularizer. The Neumann network architecture outperforms traditional inverse problem solution methods, model-free deep learning approaches, and state-of-the-art unrolled iterative methods on standard datasets. Finally, when the images belong to a union of subspaces and under appropriate assumptions on the forward model, we prove there exists a Neumann network configuration that well-approximates the optimal oracle estimator for the inverse problem and demonstrate empirically that the trained Neumann network has the form predicted by theory.
Modeling neural dynamics during speech production using a state space variational autoencoder
Authors: Pengfei Sun, David A. Moses, Edward Chang
Date: 2019-01-13 00:00:00

Characterizing the neural encoding of behavior remains a challenging task in
many research areas due in part to complex and noisy spatiotemporal dynamics of
evoked brain activity. An important aspect of modeling these neural encodings
involves separation of robust, behaviorally relevant signals from background
activity, which often contains signals from irrelevant brain processes and
decaying information from previous behavioral events. To achieve this
separation, we develop a two-branch State Space Variational AutoEncoder (SSVAE)
model to individually describe the instantaneous evoked foreground signals and
the context-dependent background signals. We modeled the spontaneous
speech-evoked brain dynamics using smoothed Gaussian mixture models. By
applying the proposed SSVAE model to track ECoG dynamics in one participant
over multiple hours, we find that the model can predict speech-related dynamics
more accurately than other latent factor inference algorithms. Our results
demonstrate that separately modeling the instantaneous speech-evoked and slow
context-dependent brain dynamics can enhance tracking performance, which has
important implications for the development of advanced neural encoding and
decoding models in various neuroscience sub-disciplines.
A Machine-Synesthetic Approach To DDoS Network Attack Detection
Authors: Yuri Monakhov, Oleg Nikitin, Anna Kuznetsova, Alexey Kharlamov, Alexandr Amochkin
Date: 2019-01-13 00:00:00
Tasks: Anomaly Detection, General Classification, image-classification, Image Classification

In the authors' opinion, anomaly detection systems, or ADS, seem to be the
most perspective direction in the subject of attack detection, because these
systems can detect, among others, the unknown (zero-day) attacks. To detect
anomalies, the authors propose to use machine synesthesia. In this case,
machine synesthesia is understood as an interface that allows using image
classification algorithms in the problem of detecting network anomalies, making
it possible to use non-specialized image detection methods that have recently
been widely and actively developed. The proposed approach is that the network
traffic data is "projected" into the image. It can be seen from the
experimental results that the proposed method for detecting anomalies shows
high results in the detection of attacks. On a large sample, the value of the
complex efficiency indicator reaches 97%.
Context Oriented Software Middleware
Authors: Basel Magableh
Date: 2019-01-13 00:00:00

Our middleware approach, Context-Oriented Software Middleware (COSM),
supports context-dependent software with self-adaptability and dependability in
a mobile computing environment. The COSM-middleware is a generic and
platform-independent adaptation engine, which performs a runtime composition of
the software's context-dependent behaviours based on the execution contexts.
Our middleware distinguishes between the context-dependent and
context-independent functionality of software systems. This enables the
COSM-middleware to adapt the application behaviour by composing a set of
context-oriented components, that implement the context-dependent functionality
of the software. Accordingly, the software dependability is achieved by
considering the functionality of the COSM-middleware and the adaptation
impact/costs. The COSM-middleware uses a dynamic policy-based engine to
evaluate the adaptation outputs and verify the fitness of the adaptation output
with the application's objectives, goals and the architecture quality
attributes. These capabilities are demonstrated through an empirical evaluation
of a case study implementation.
A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architecture
Authors: Basel Magableh
Date: 2019-01-13 00:00:00
Tasks: Decision Making, Q-Learning

One desired aspect of microservices architecture is the ability to self-adapt
its own architecture and behaviour in response to changes in the operational
environment. To achieve the desired high levels of self-adaptability, this
research implements the distributed microservices architectures model, as
informed by the MAPE-K model. The proposed architecture employs a multi
adaptation agents supported by a centralised controller, that can observe the
environment and execute a suitable adaptation action. The adaptation planning
is managed by a deep recurrent Q-network (DRQN). It is argued that such
integration between DRQN and MDP agents in a MAPE-K model offers distributed
microservice architecture with self-adaptability and high levels of
availability and scalability. Integrating DRQN into the adaptation process
improves the effectiveness of the adaptation and reduces any adaptation risks,
including resources over-provisioning and thrashing. The performance of DRQN is
evaluated against deep Q-learning and policy gradient algorithms including: i)
deep q-network (DQN), ii) dulling deep Q-network (DDQN), iii) a policy gradient
neural network (PGNN), and iv) deep deterministic policy gradient (DDPG). The
DRQN implementation in this paper manages to outperform the above mentioned
algorithms in terms of total reward, less adaptation time, lower error rates,
plus faster convergence and training times. We strongly believe that DRQN is
more suitable for driving the adaptation in distributed services-oriented
architecture and offers better performance than other dynamic decision-making
algorithms.
Neural network gradient-based learning of black-box function interfaces
Authors: Alon Jacovi, Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Jonathan Berant
Date: 2019-01-13 00:00:00
Proceeding: ICLR 2019 5

Deep neural networks work well at approximating complicated functions when
provided with data and trained by gradient descent methods. At the same time,
there is a vast amount of existing functions that programmatically solve
different tasks in a precise manner eliminating the need for training. In many
cases, it is possible to decompose a task to a series of functions, of which
for some we may prefer to use a neural network to learn the functionality,
while for others the preferred method would be to use existing black-box
functions. We propose a method for end-to-end training of a base neural network
that integrates calls to existing black-box functions. We do so by
approximating the black-box functionality with a differentiable neural network
in a way that drives the base network to comply with the black-box function
interface during the end-to-end optimization process. At inference time, we
replace the differentiable estimator with its external black-box
non-differentiable counterpart such that the base network output matches the
input arguments of the black-box function. Using this "Estimate and Replace"
paradigm, we train a neural network, end to end, to compute the input to
black-box functionality while eliminating the need for intermediate labels. We
show that by leveraging the existing precise black-box function during
inference, the integrated model generalizes better than a fully differentiable
model, and learns more efficiently compared to RL-based methods.