pwc-archive/papers-with-abstracts huggingface dataset:
Denoising Auto-encoder with Recurrent Skip Connections and Residual Regression for Music Source Separation
Authors: Jen-Yu Liu, Yi-Hsuan Yang
Date: 2018-07-05 00:00:00
Tasks: ARC, Denoising, Music Source Separation, regression

Convolutional neural networks with skip connections have shown good
performance in music source separation. In this work, we propose a denoising
Auto-encoder with Recurrent skip Connections (ARC). We use 1D convolution along
the temporal axis of the time-frequency feature map in all layers of the
fully-convolutional network. The use of 1D convolution makes it possible to
apply recurrent layers to the intermediate outputs of the convolution layers.
In addition, we also propose an enhancement network and a residual regression
method to further improve the separation result. The recurrent skip
connections, the enhancement module, and the residual regression all improve
the separation quality. The ARC model with residual regression achieves 5.74
siganl-to-distoration ratio (SDR) in vocals with MUSDB in SiSEC 2018. We also
evaluate the ARC model alone on the older dataset DSD100 (used in SiSEC 2016)
and it achieves 5.91 SDR in vocals.
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
Authors: Septimia Sârbu, Riccardo Volpi, Alexandra Peşte, Luigi Malagò
Date: 2018-07-05 00:00:00
Tasks: Decoder, Variational Inference

In this paper we propose two novel bounds for the log-likelihood based on
Kullback-Leibler and the R\'{e}nyi divergences, which can be used for
variational inference and in particular for the training of Variational
AutoEncoders. Our proposal is motivated by the difficulties encountered in
training VAEs on continuous datasets with high contrast images, such as those
with handwritten digits and characters, where numerical issues often appear
unless noise is added, either to the dataset during training or to the
generative model given by the decoder. The new bounds we propose, which are
obtained from the maximization of the likelihood of an interval for the
observations, allow numerically stable training procedures without the
necessity of adding any extra source of noise to the data.
A Single Shot Text Detector with Scale-adaptive Anchors
Authors: Qi Yuan, Bingwang Zhang, Haojie Li, Zhihui Wang, Zhongxuan Luo
Date: 2018-07-05 00:00:00
Tasks: Computational Efficiency, Text Detection

Currently, most top-performing text detection networks tend to employ
fixed-size anchor boxes to guide the search for text instances. They usually
rely on a large amount of anchors with different scales to discover texts in
scene images, thus leading to high computational cost. In this paper, we
propose an end-to-end box-based text detector with scale-adaptive anchors,
which can dynamically adjust the scales of anchors according to the sizes of
underlying texts by introducing an additional scale regression layer. The
proposed scale-adaptive anchors allow us to use a few number of anchors to
handle multi-scale texts and therefore significantly improve the computational
efficiency. Moreover, compared to discrete scales used in previous methods, the
learned continuous scales are more reliable, especially for small texts
detection. Additionally, we propose Anchor convolution to better exploit
necessary feature information by dynamically adjusting the sizes of receptive
fields according to the learned scales. Extensive experiments demonstrate that
the proposed detector is fast, taking only $0.28$ second per image, while
outperforming most state-of-the-art methods in accuracy.
Chinese Lexical Analysis with Deep Bi-GRU-CRF Network
Authors: Zhenyu Jiao, Shuqi Sun, Ke Sun
Date: 2018-07-05 00:00:00
Tasks: Lexical Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, Part-Of-Speech Tagging

Lexical analysis is believed to be a crucial step towards natural language
understanding and has been widely studied. Recent years, end-to-end lexical
analysis models with recurrent neural networks have gained increasing
attention. In this report, we introduce a deep Bi-GRU-CRF network that jointly
models word segmentation, part-of-speech tagging and named entity recognition
tasks. We trained the model using several massive corpus pre-tagged by our best
Chinese lexical analysis tool, together with a small, yet high-quality human
annotated corpus. We conducted balanced sampling between different corpora to
guarantee the influence of human annotations, and fine-tune the CRF decoding
layer regularly during the training progress. As evaluated by linguistic
experts, the model achieved a 95.5% accuracy on the test set, roughly 13%
relative error reduction over our (previously) best Chinese lexical analysis
tool. The model is computationally efficient, achieving the speed of 2.3K
characters per second with one thread.
Road surface 3d reconstruction based on dense subpixel disparity map estimation
Authors: Rui Fan, Xiao Ai, Naim Dahnoun
Date: 2018-07-05 00:00:00
Tasks: 3D Reconstruction, Computational Efficiency, Disparity Estimation, Stereo Matching, Stereo Matching Hand

Various 3D reconstruction methods have enabled civil engineers to detect damage on a road surface. To achieve the millimetre accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used. However, none of the existing stereo matching algorithms are specially suitable for the reconstruction of the road surface. Hence in this paper, we propose a novel dense subpixel disparity estimation algorithm with high computational efficiency and robustness. This is achieved by first transforming the perspective view of the target frame into the reference view, which not only increases the accuracy of the block matching for the road surface but also improves the processing speed. The disparities are then estimated iteratively using our previously published algorithm where the search range is propagated from three estimated neighbouring disparities. Since the search range is obtained from the previous iteration, errors may occur when the propagated search range is not sufficient. Therefore, a correlation maxima verification is performed to rectify this issue, and the subpixel resolution is achieved by conducting a parabola interpolation enhancement. Furthermore, a novel disparity global refinement approach developed from the Markov Random Fields and Fast Bilateral Stereo is introduced to further improve the accuracy of the estimated disparity map, where disparities are updated iteratively by minimising the energy function that is related to their interpolated correlation polynomials. The algorithm is implemented in C language with a near real-time performance. The experimental results illustrate that the absolute error of the reconstruction varies from 0.1 mm to 3 mm.
Hunting the Ethereum Smart Contract: Color-inspired Inspection of Potential Attacks
Authors: TonTon Hsien-De Huang
Date: 2018-07-05 00:00:00

Blockchain and Cryptocurrencies are gaining unprecedented popularity and
understanding. Meanwhile, Ethereum is gaining a significant popularity in the
blockchain community, mainly due to the fact that it is designed in a way that
enables developers to write smart contract and decentralized applications
(Dapps). This new paradigm of applications opens the door to many possibilities
and opportunities. However, the security of Ethereum smart contracts has not
received much attention; several Ethereum smart contracts malfunctioning have
recently been reported. Unlike many previous works that have applied static and
dynamic analyses to find bugs in smart contracts, we do not attempt to define
and extract any features; instead we focus on reducing the expert's labor
costs. We first present a new in-depth analysis of potential attacks
methodology and then translate the bytecode of solidity into RGB color code.
After that, we transform them to a fixed-sized encoded image. Finally, the
encoded image is fed to convolutional neural network (CNN) for automatic
feature extraction and learning, detecting compiler bugs of Ethereum smart
contract.
Detecting Tiny Moving Vehicles in Satellite Videos
Authors: Wei Ao, Yanwei Fu, Feng Xu
Date: 2018-07-05 00:00:00
Tasks: vehicle detection

In recent years, the satellite videos have been captured by a moving
satellite platform. In contrast to consumer, movie, and common surveillance
videos, satellite video can record the snapshot of the city-scale scene. In a
broad field-of-view of satellite videos, each moving target would be very tiny
and usually composed of several pixels in frames. Even worse, the noise signals
also existed in the video frames, since the background of the video frame has
the subpixel-level and uneven moving thanks to the motion of satellites. We
argue that this is a new type of computer vision task since previous
technologies are unable to detect such tiny vehicles efficiently. This paper
proposes a novel framework that can identify the small moving vehicles in
satellite videos. In particular, we offer a novel detecting algorithm based on
the local noise modeling. We differentiate the potential vehicle targets from
noise patterns by an exponential probability distribution. Subsequently, a
multi-morphological-cue based discrimination strategy is designed to
distinguish correct vehicle targets from a few existing noises further. Another
significant contribution is to introduce a series of evaluation protocols to
measure the performance of tiny moving vehicle detection systematically. We
annotate a satellite video manually and use it to test our algorithms under
different evaluation criterion. The proposed algorithm is also compared with
the state-of-the-art baselines, and demonstrates the advantages of our
framework over the benchmarks.
Privacy-preserving Machine Learning through Data Obfuscation
Authors: Tianwei Zhang, Zecheng He, Ruby B. Lee
Date: 2018-07-05 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

As machine learning becomes a practice and commodity, numerous cloud-based
services and frameworks are provided to help customers develop and deploy
machine learning applications. While it is prevalent to outsource model
training and serving tasks in the cloud, it is important to protect the privacy
of sensitive samples in the training dataset and prevent information leakage to
untrusted third parties. Past work have shown that a malicious machine learning
service provider or end user can easily extract critical information about the
training samples, from the model parameters or even just model outputs.
  In this paper, we propose a novel and generic methodology to preserve the
privacy of training data in machine learning applications. Specifically we
introduce an obfuscate function and apply it to the training data before
feeding them to the model training task. This function adds random noise to
existing samples, or augments the dataset with new samples. By doing so
sensitive information about the properties of individual samples, or
statistical properties of a group of samples, is hidden. Meanwhile the model
trained from the obfuscated dataset can still achieve high accuracy. With this
approach, the customers can safely disclose the data or models to third-party
providers or end users without the need to worry about data privacy. Our
experiments show that this approach can effective defeat four existing types of
machine learning privacy attacks at negligible accuracy cost.
Zipf's law in 50 languages: its structural pattern, linguistic interpretation, and cognitive motivation
Authors: Shuiyuan Yu, Chunshan Xu, Haitao Liu
Date: 2018-07-05 00:00:00

Zipf's law has been found in many human-related fields, including language,
where the frequency of a word is persistently found as a power law function of
its frequency rank, known as Zipf's law. However, there is much dispute whether
it is a universal law or a statistical artifact, and little is known about what
mechanisms may have shaped it. To answer these questions, this study conducted
a large scale cross language investigation into Zipf's law. The statistical
results show that Zipf's laws in 50 languages all share a 3-segment structural
pattern, with each segment demonstrating distinctive linguistic properties and
the lower segment invariably bending downwards to deviate from theoretical
expectation. This finding indicates that this deviation is a fundamental and
universal feature of word frequency distributions in natural languages, not the
statistical error of low frequency words. A computer simulation based on the
dual-process theory yields Zipf's law with the same structural pattern,
suggesting that Zipf's law of natural languages are motivated by common
cognitive mechanisms. These results show that Zipf's law in languages is
motivated by cognitive mechanisms like dual-processing that govern human verbal
behaviors.
Logistic Regression, Neural Networks and Dempster-Shafer Theory: a New Perspective
Authors: Thierry Denoeux
Date: 2018-07-05 00:00:00
Tasks: regression

We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input or higher-level features into Dempster-Shafer mass functions and aggregating them by Dempster's rule of combination. The probabilistic outputs of these classifiers are the normalized plausibilities corresponding to the underlying combined mass function. This mass function is more informative than the output probability distribution. In particular, it makes it possible to distinguish between lack of evidence (when none of the features provides discriminant information) from conflicting evidence (when different features support different classes). This expressivity of mass functions allows us to gain insight into the role played by each input feature in logistic regression, and to interpret hidden unit outputs in multilayer neural networks. It also makes it possible to use alternative decision rules, such as interval dominance, which select a set of classes when the available evidence does not unambiguously point to a single class, thus trading reduced error rate for higher imprecision.
Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm
Authors: Benyamin Ghojogh, Saeed Sharifian
Date: 2018-07-05 00:00:00
Tasks: Metaheuristic Optimization, Sand

Nowadays, metaheuristic optimization algorithms are used to find the global
optima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization
(PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging
behavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny
creature found mostly in coast of Caspian Sea in Iran. In this algorithm,
global optima is modeled as sea edge (coast) to which Gammarus creatures are
willing to move in order to rest from sea waves and forage in sand. Sea waves
satisfy exploration and foraging models exploitation. The strength of sea wave
is determined according to distance of Gammarus from sea edge. The angles of
waves applied on several particles are set randomly helping algorithm not be
stuck in local bests. Meanwhile, the neighborhood of particles change
adaptively resulting in more efficient progress in searching. The proposed
algorithm, although is applicable on any optimization problem, is experimented
for partially shaded solar PV array. Experiments on CEC05 benchmarks, as well
as solar PV array, show the effectiveness of this optimization algorithm.
Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering
Authors: Vikas Yadav, Rebecca Sharp, Mihai Surdeanu
Date: 2018-07-05 00:00:00
Tasks: ARC, Information Retrieval, Question Answering, Retrieval

While increasingly complex approaches to question answering (QA) have been
proposed, the true gain of these systems, particularly with respect to their
expensive training requirements, can be inflated when they are not compared to
adequate baselines. Here we propose an unsupervised, simple, and fast alignment
and information retrieval baseline that incorporates two novel contributions: a
\textit{one-to-many alignment} between query and document terms and
\textit{negative alignment} as a proxy for discriminative information. Our
approach not only outperforms all conventional baselines as well as many
supervised recurrent neural networks, but also approaches the state of the art
for supervised systems on three QA datasets. With only three hyperparameters,
we achieve 47\% P@1 on an 8th grade Science QA dataset, 32.9\% P@1 on a Yahoo!
answers QA dataset and 64\% MAP on WikiQA. We also achieve 26.56\% and 58.36\%
on ARC challenge and easy dataset respectively. In addition to including the
additional ARC results in this version of the paper, for the ARC easy set only
we also experimented with one additional parameter -- number of justifications
retrieved.
Per-decision Multi-step Temporal Difference Learning with Control Variates
Authors: Kristopher De Asis, Richard S. Sutton
Date: 2018-07-05 00:00:00
Tasks: Reinforcement Learning

Multi-step temporal difference (TD) learning is an important approach in
reinforcement learning, as it unifies one-step TD learning with Monte Carlo
methods in a way where intermediate algorithms can outperform either extreme.
They address a bias-variance trade off between reliance on current estimates,
which could be poor, and incorporating longer sampled reward sequences into the
updates. Especially in the off-policy setting, where the agent aims to learn
about a policy different from the one generating its behaviour, the variance in
the updates can cause learning to diverge as the number of sampled rewards used
in the estimates increases. In this paper, we introduce per-decision control
variates for multi-step TD algorithms, and compare them to existing methods.
Our results show that including the control variates can greatly improve
performance on both on and off-policy multi-step temporal difference learning
tasks.
Learning Theory and Algorithms for Revenue Management in Sponsored Search
Authors: Lulu Wang, Huahui Liu, Guanhao Chen, Shaola Ren, Xiaonan Meng, Yi Hu
Date: 2018-07-05 00:00:00
Tasks: Learning Theory, Management

Online advertisement is the main source of revenue for Internet business.
Advertisers are typically ranked according to a score that takes into account
their bids and potential click-through rates(eCTR). Generally, the likelihood
that a user clicks on an ad is often modeled by optimizing for the click
through rates rather than the performance of the auction in which the click
through rates will be used. This paper attempts to eliminate this
dis-connection by proposing loss functions for click modeling that are based on
final auction performance.In this paper, we address two feasible metrics (AUC^R
and SAUC) to evaluate the on-line RPM (revenue per mille) directly rather than
the CTR. And then, we design an explicit ranking function by incorporating the
calibration fac-tor and price-squashed factor to maximize the revenue. Given
the power of deep networks, we also explore an implicit optimal ranking
function with deep model. Lastly, various experiments with two real world
datasets are presented. In particular, our proposed methods perform better than
the state-of-the-art methods with regard to the revenue of the platform.
PortraitGAN for Flexible Portrait Manipulation
Authors: Jiali Duan, Xiaoyuan Guo, Yuhang Song, Chao Yang, C. -C. Jay Kuo
Date: 2018-07-05 00:00:00

Previous methods have dealt with discrete manipulation of facial attributes
such as smile, sad, angry, surprise etc, out of canonical expressions and they
are not scalable, operating in single modality. In this paper, we propose a
novel framework that supports continuous edits and multi-modality portrait
manipulation using adversarial learning. Specifically, we adapt
cycle-consistency into the conditional setting by leveraging additional facial
landmarks information. This has two effects: first cycle mapping induces
bidirectional manipulation and identity preserving; second pairing samples from
different modalities can thus be utilized. To ensure high-quality synthesis, we
adopt texture-loss that enforces texture consistency and multi-level
adversarial supervision that facilitates gradient flow. Quantitative and
qualitative experiments show the effectiveness of our framework in performing
flexible and multi-modality portrait manipulation with photo-realistic effects.
Discrete Sampling using Semigradient-based Product Mixtures
Authors: Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka
Date: 2018-07-04 00:00:00
Tasks: Point Processes

We consider the problem of inference in discrete probabilistic models, that
is, distributions over subsets of a finite ground set. These encompass a range
of well-known models in machine learning, such as determinantal point processes
and Ising models. Locally-moving Markov chain Monte Carlo algorithms, such as
the Gibbs sampler, are commonly used for inference in such models, but their
convergence is, at times, prohibitively slow. This is often caused by
state-space bottlenecks that greatly hinder the movement of such samplers. We
propose a novel sampling strategy that uses a specific mixture of product
distributions to propose global moves and, thus, accelerate convergence.
Furthermore, we show how to construct such a mixture using semigradient
information. We illustrate the effectiveness of combining our sampler with
existing ones, both theoretically on an example model, as well as practically
on three models learned from real-world data sets.
Deep Cross-modality Adaptation via Semantics Preserving Adversarial Learning for Sketch-based 3D Shape Retrieval
Authors: Jiaxin Chen, Yi Fang
Date: 2018-07-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Shape Classification, 3D Shape Retrieval, Metric Learning, Retrieval

Due to the large cross-modality discrepancy between 2D sketches and 3D
shapes, retrieving 3D shapes by sketches is a significantly challenging task.
To address this problem, we propose a novel framework to learn a discriminative
deep cross-modality adaptation model in this paper. Specifically, we first
separately adopt two metric networks, following two deep convolutional neural
networks (CNNs), to learn modality-specific discriminative features based on an
importance-aware metric learning method. Subsequently, we explicitly introduce
a cross-modality transformation network to compensate for the divergence
between two modalities, which can transfer features of 2D sketches to the
feature space of 3D shapes. We develop an adversarial learning based method to
train the transformation model, by simultaneously enhancing the holistic
correlations between data distributions of two modalities, and mitigating the
local semantic divergences through minimizing a cross-modality mean discrepancy
term. Experimental results on the SHREC 2013 and SHREC 2014 datasets clearly
show the superior retrieval performance of our proposed model, compared to the
state-of-the-art approaches.