pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning
Authors: Li He, Liang Wang, Kaipeng Liu, Bo Wu, Wei-Nan Zhang
Date: 2018-03-20 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sponsored search is an indispensable business model and a major revenue
contributor of almost all the search engines. From the advertisers' side,
participating in ranking the search results by paying for the sponsored search
advertisement to attract more awareness and purchase facilitates their
commercial goal. From the users' side, presenting personalized advertisement
reflecting their propensity would make their online search experience more
satisfactory. Sponsored search platforms rank the advertisements by a ranking
function to determine the list of advertisements to show and the charging price
for the advertisers. Hence, it is crucial to find a good ranking function which
can simultaneously satisfy the platform, the users and the advertisers.
Moreover, advertisements showing positions under different queries from
different users may associate with advertisement candidates of different bid
price distributions and click probability distributions, which requires the
ranking functions to be optimized adaptively to the traffic characteristics. In
this work, we proposed a generic framework to optimize the ranking functions by
deep reinforcement learning methods. The framework is composed of two parts: an
offline learning part which initializes the ranking functions by learning from
a simulated advertising environment, allowing adequate exploration of the
ranking function parameter space without hurting the performance of the
commercial platform. An online learning part which further optimizes the
ranking functions by adapting to the online data distribution. Experimental
results on a large-scale sponsored search platform confirm the effectiveness of
the proposed method.
Automatic Identification of Closely-related Indian Languages: Resources and Experiments
Authors: Ritesh Kumar, Bornini Lahiri, Deepak Alok, Atul Kr. Ojha, Mayank Jain, Abdul Basit, Yogesh Dawer
Date: 2018-03-26 00:00:00
Tasks: Language Identification

In this paper, we discuss an attempt to develop an automatic language
identification system for 5 closely-related Indo-Aryan languages of India,
Awadhi, Bhojpuri, Braj, Hindi and Magahi. We have compiled a comparable corpora
of varying length for these languages from various resources. We discuss the
method of creation of these corpora in detail. Using these corpora, a language
identification system was developed, which currently gives state of the art
accuracy of 96.48\%. We also used these corpora to study the similarity between
the 5 languages at the lexical level, which is the first data-based study of
the extent of closeness of these languages.
Aggression-annotated Corpus of Hindi-English Code-mixed Data
Authors: Ritesh Kumar, Aishwarya N. Reganti, Akshit Bhatia, Tushar Maheshwari
Date: 2018-03-26 00:00:00
Proceeding: LREC 2018 5

As the interaction over the web has increased, incidents of aggression and
related events like trolling, cyberbullying, flaming, hate speech, etc. too
have increased manifold across the globe. While most of these behaviour like
bullying or hate speech have predated the Internet, the reach and extent of the
Internet has given these an unprecedented power and influence to affect the
lives of billions of people. So it is of utmost significance and importance
that some preventive measures be taken to provide safeguard to the people using
the web such that the web remains a viable medium of communication and
connection, in general. In this paper, we discuss the development of an
aggression tagset and an annotated corpus of Hindi-English code-mixed data from
two of the most popular social networking and social media platforms in India,
Twitter and Facebook. The corpus is annotated using a hierarchical tagset of 3
top-level tags and 10 level 2 tags. The final dataset contains approximately
18k tweets and 21k facebook comments and is being released for further research
in the field.
Context-Specific Validation of Data-Driven Models
Authors: Somil Bansal, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia, Claire J. Tomlin
Date: 2018-02-14 00:00:00

With an increasing use of data-driven models to control robotic systems, it
has become important to develop a methodology for validating such models before
they can be deployed to design a controller for the actual system.
Specifically, it must be ensured that the controller designed for a learned
model would perform as expected on the actual physical system. We propose a
context-specific validation framework to quantify the quality of a learned
model based on a distance measure between the closed-loop actual system and the
learned model. We then propose an active sampling scheme to compute a
probabilistic upper bound on this distance in a sample-efficient manner. The
proposed framework validates the learned model against only those behaviors of
the system that are relevant for the purpose for which we intend to use this
model, and does not require any a priori knowledge of the system dynamics.
Several simulations illustrate the practicality of the proposed framework for
validating the models of real-world systems, and consequently, for controller
synthesis.
HomeGuard: A Smart System to Deal with the Emergency Response of Domestic Violence Victims
Authors: Anik Islam, Arifa Akter, Bayzid Ashik Hossain
Date: 2018-03-26 00:00:00

Domestic violence is a silent crisis in the developing and underdeveloped
countries, though developed countries also remain drowned in the curse of it.
In developed countries, victims can easily report and ask help on the contrary
in developing and underdeveloped countries victims hardly report the crimes and
when it's noticed by the authority it's become too late to save or support the
victim. If this kind of problems can be identified at the very beginning of the
event and proper actions can be taken, it'll not only help the victim but also
reduce the domestic violence crimes. This paper proposed a smart system which
can extract victim's situation and provide help according to it. Among of the
developing and underdeveloped countries Bangladesh has been chosen though the
rate of reporting of domestic violence is low, the extreme report collected by
authorities is too high. Case studies collected by different NGO's relating to
domestic violence have been studied and applied to extract possible condition
for the victims.
Deep Sampling Networks
Authors: Bolun Cai, Xiangmin Xu, Kailing Guo, Kui Jia, DaCheng Tao
Date: 2017-12-04 00:00:00
Tasks: Image Compression, Image Super-Resolution, Super-Resolution

Deep convolutional neural networks achieve excellent image up-sampling
performance. However, CNN-based methods tend to restore high-resolution results
highly depending on traditional interpolations (e.g. bicubic). In this paper,
we present a deep sampling network (DSN) for down-sampling and up-sampling
without any cheap interpolation. First, the down-sampling subnetwork is trained
without supervision, thereby preserving more information and producing better
visual effects in the low-resolution image. Second, the up-sampling subnetwork
learns a sub-pixel residual with dense connections to accelerate convergence
and improve performance. DSN's down-sampling subnetwork can be used to generate
photo-realistic low-resolution images and replace traditional down-sampling
method in image processing. With the powerful down-sampling process, the
co-training DSN set a new state-of-the-art performance for image
super-resolution. Moreover, DSN is compatible with existing image codecs to
improve image compression.
A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle
Authors: Michael Teti, William Edward Hahn, Shawn Martin, Christopher Teti, Elan Barenholtz
Date: 2018-03-26 00:00:00
Tasks: Autonomous Driving

Self-driving technology is advancing rapidly --- albeit with significant
challenges and limitations. This progress is largely due to recent developments
in deep learning algorithms. To date, however, there has been no systematic
comparison of how different deep learning architectures perform at such tasks,
or an attempt to determine a correlation between classification performance and
performance in an actual vehicle, a potentially critical factor in developing
self-driving systems. Here, we introduce the first controlled comparison of
multiple deep-learning architectures in an end-to-end autonomous driving task
across multiple testing conditions. We compared performance, under identical
driving conditions, across seven architectures including a fully-connected
network, a simple 2 layer CNN, AlexNet, VGG-16, Inception-V3, ResNet, and an
LSTM by assessing the number of laps each model was able to successfully
complete without crashing while traversing an indoor racetrack. We compared
performance across models when the conditions exactly matched those in training
as well as when the local environment and track were configured differently and
objects that were not included in the training dataset were placed on the track
in various positions. In addition, we considered performance using several
different data types for training and testing including single grayscale and
color frames, and multiple grayscale frames stacked together in sequence. With
the exception of a fully-connected network, all models performed reasonably
well (around or above 80\%) and most very well (~95\%) on at least one input
type but with considerable variation across models and inputs. Overall,
AlexNet, operating on single color frames as input, achieved the best level of
performance (100\% success rate in phase one and 55\% in phase two) while
VGG-16 performed well most consistently across image types.
The Classification of Cropping Patterns Based on Regional Climate Classification Using Decision Tree Approach
Authors: T. A. Munandar, Sumiati
Date: 2018-03-26 00:00:00
Tasks: Classification, General Classification

Nowadays, agricultural field is experiencing problems related to climate
change that result in the changing patterns in cropping season, especially for
paddy and coarse grains, pulses roots and Tuber (CGPRT/Palawija) crops. The
cropping patterns of rice and CGPRT crops highly depend on the availability of
rainfall throughout the year. The changing and shifting of the rainy season
result in the changing cropping seasons. It is important to find out the
cropping patterns of paddy and CGPRT crops based on monthly rainfall pattern in
every area. The Oldeman's method which is usually used in the classification of
of cropping patterns of paddy and CGPRT crops is considered less able to
determine the cropping patterns because it requires to see the rainfall data
throughout the year. This research proposes an alternative solution to
determine the cropping pattern of paddy and CGPRT crops based on the pattern of
rainfall in the area using decision tree approach. There were three algorithms,
namely, J48, RandomTree and REPTree, tested to determine the best algorithm
used in the process of the classification of the cropping pattern in the area.
The results showed that J48 algorithm has a higher classification accuracy than
RandomTree and REPTree for 48%, 42.67% and 38.67%, respectively. Meanwhile, the
results of data testing into the decision tree rule indicate that most of the
areas in DKI Jakarta are suggested to apply the cropping pattern of 1 paddy
cropping and 1 CGRPT cropping (1 PS + 1 PL). While in Banten, there are three
cropping patterns that can be applied, they are, 1 paddy cropping and 1 CGPRT
cropping (1 PS + 1 PL), 3 short-period paddy croppings or 2 paddy croppings and
1 CGPRT cropping (3 short-period PS or 2 PS + 1 PL) and 2 paddy croppings and 1
CGPRT cropping (2 PS + 1 PL).
StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow
Authors: Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, Huan Sun
Date: 2018-03-26 00:00:00
Tasks: Retrieval

Stack Overflow (SO) has been a great source of natural language questions and
their code solutions (i.e., question-code pairs), which are critical for many
tasks including code retrieval and annotation. In most existing research,
question-code pairs were collected heuristically and tend to have low quality.
In this paper, we investigate a new problem of systematically mining
question-code pairs from Stack Overflow (in contrast to heuristically
collecting them). It is formulated as predicting whether or not a code snippet
is a standalone solution to a question. We propose a novel Bi-View Hierarchical
Neural Network which can capture both the programming content and the textual
context of a code snippet (i.e., two views) to make a prediction. On two
manually annotated datasets in Python and SQL domain, our framework
substantially outperforms heuristic methods with at least 15% higher F1 and
accuracy. Furthermore, we present StaQC (Stack Overflow Question-Code pairs),
the largest dataset to date of ~148K Python and ~120K SQL question-code pairs,
automatically mined from SO using our framework. Under various case studies, we
demonstrate that StaQC can greatly help develop data-hungry models for
associating natural language with programming language.
Learning to Explore with Meta-Policy Gradient
Authors: Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng
Date: 2018-03-13 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

The performance of off-policy learning, including deep Q-learning and deep
deterministic policy gradient (DDPG), critically depends on the choice of the
exploration policy. Existing exploration methods are mostly based on adding
noise to the on-going actor policy and can only explore \emph{local} regions
close to what the actor policy dictates. In this work, we develop a simple
meta-policy gradient algorithm that allows us to adaptively learn the
exploration policy in DDPG. Our algorithm allows us to train flexible
exploration behaviors that are independent of the actor policy, yielding a
\emph{global exploration} that significantly speeds up the learning process.
With an extensive study, we show that our method significantly improves the
sample-efficiency of DDPG on a variety of reinforcement learning tasks.
Variational Inference for Policy Gradient
Authors: Tianbing Xu
Date: 2018-02-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

Inspired by the seminal work on Stein Variational Inference and Stein
Variational Policy Gradient, we derived a method to generate samples from the
posterior variational parameter distribution by \textit{explicitly} minimizing
the KL divergence to match the target distribution in an amortize fashion.
Consequently, we applied this varational inference technique into vanilla
policy gradient, TRPO and PPO with Bayesian Neural Network parameterizations
for reinforcement learning problems.
Resilient Monotone Sequential Maximization
Authors: Vasileios Tzoumas, Ali Jadbabaie, George J. Pappas
Date: 2018-03-21 00:00:00
Tasks: Robot Navigation, Scheduling, valid

Applications in machine learning, optimization, and control require the sequential selection of a few system elements, such as sensors, data, or actuators, to optimize the system performance across multiple time steps. However, in failure-prone and adversarial environments, sensors get attacked, data get deleted, and actuators fail. Thence, traditional sequential design paradigms become insufficient and, in contrast, resilient sequential designs that adapt against system-wide attacks, deletions, or failures become important. In general, resilient sequential design problems are computationally hard. Also, even though they often involve objective functions that are monotone and (possibly) submodular, no scalable approximation algorithms are known for their solution. In this paper, we provide the first scalable algorithm, that achieves the following characteristics: system-wide resiliency, i.e., the algorithm is valid for any number of denial-of-service attacks, deletions, or failures; adaptiveness, i.e., at each time step, the algorithm selects system elements based on the history of inflicted attacks, deletions, or failures; and provable approximation performance, i.e., the algorithm guarantees for monotone objective functions a solution close to the optimal. We quantify the algorithm's approximation performance using a notion of curvature for monotone (not necessarily submodular) set functions. Finally, we support our theoretical analyses with simulated experiments, by considering a control-aware sensor scheduling scenario, namely, sensing-constrained robot navigation.
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
Authors: Kuang-Huei Lee, Xiaodong He, Lei Zhang, Linjun Yang
Date: 2017-11-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

In this paper, we study the problem of learning image classification models
with label noise. Existing approaches depending on human supervision are
generally not scalable as manually identifying correct or incorrect labels is
time-consuming, whereas approaches not relying on human supervision are
scalable but less effective. To reduce the amount of human supervision for
label noise cleaning, we introduce CleanNet, a joint neural embedding network,
which only requires a fraction of the classes being manually verified to
provide the knowledge of label noise that can be transferred to other classes.
We further integrate CleanNet and conventional convolutional neural network
classifier into one framework for image classification learning. We demonstrate
the effectiveness of the proposed algorithm on both of the label noise
detection task and the image classification on noisy data task on several
large-scale datasets. Experimental results show that CleanNet can reduce label
noise detection error rate on held-out classes where no human supervision
available by 41.5% compared to current weakly supervised methods. It also
achieves 47% of the performance gain of verifying all images with only 3.2%
images verified on an image classification task. Source code and dataset will
be available at kuanghuei.github.io/CleanNetProject.
Nearly Optimal Robust Subspace Tracking
Authors: Praneeth Narayanamurthy, Namrata Vaswani
Date: 2017-12-17 00:00:00
Proceeding: ICML 2018 7
Tasks: Compressive Sensing

In this work, we study the robust subspace tracking (RST) problem and obtain
one of the first two provable guarantees for it. The goal of RST is to track
sequentially arriving data vectors that lie in a slowly changing
low-dimensional subspace, while being robust to corruption by additive sparse
outliers. It can also be interpreted as a dynamic (time-varying) extension of
robust PCA (RPCA), with the minor difference that RST also requires a short
tracking delay. We develop a recursive projected compressive sensing algorithm
that we call Nearly Optimal RST via ReProCS (ReProCS-NORST) because its
tracking delay is nearly optimal. We prove that NORST solves both the RST and
the dynamic RPCA problems under weakened standard RPCA assumptions, two simple
extra assumptions (slow subspace change and most outlier magnitudes lower
bounded), and a few minor assumptions.
  Our guarantee shows that NORST enjoys a near optimal tracking delay of $O(r
\log n \log(1/\epsilon))$. Its required delay between subspace change times is
the same, and its memory complexity is $n$ times this value. Thus both these
are also nearly optimal. Here $n$ is the ambient space dimension, $r$ is the
subspaces' dimension, and $\epsilon$ is the tracking accuracy. NORST also has
the best outlier tolerance compared with all previous RPCA or RST methods, both
theoretically and empirically (including for real videos), without requiring
any model on how the outlier support is generated. This is possible because of
the extra assumptions it uses.
A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes
Authors: Lingfeng Zhang, Pengfei Dou, Ioannis A. Kakadiaris
Date: 2018-03-25 00:00:00
Tasks: Face Recognition

This paper focuses on improving face recognition performance with a new
signature combining implicit facial features with explicit soft facial
attributes. This signature has two components: the existing patch-based
features and the soft facial attributes. A deep convolutional neural network
adapted from state-of-the-art networks is used to learn the soft facial
attributes. Then, a signature matcher is introduced that merges the
contributions of both patch-based features and the facial attributes. In this
matcher, the matching scores computed from patch-based features and the facial
attributes are combined to obtain a final matching score. The matcher is also
extended so that different weights are assigned to different facial attributes.
The proposed signature and matcher have been evaluated with the UR2D system on
the UHDB31 and IJB-A datasets. The experimental results indicate that the
proposed signature achieve better performance than using only patch-based
features. The Rank-1 accuracy is improved significantly by 4% and 0.37% on the
two datasets when compared with the UR2D system.
On the Local Minima of the Empirical Risk
Authors: Chi Jin, Lydia T. Liu, Rong Ge, Michael. I. Jordan
Date: 2018-03-25 00:00:00
Proceeding: NeurIPS 2018 12

Population risk is always of primary interest in machine learning; however,
learning algorithms only have access to the empirical risk. Even for
applications with nonconvex nonsmooth losses (such as modern deep networks),
the population risk is generally significantly more well-behaved from an
optimization point of view than the empirical risk. In particular, sampling can
create many spurious local minima. We consider a general framework which aims
to optimize a smooth nonconvex function $F$ (population risk) given only access
to an approximation $f$ (empirical risk) that is pointwise close to $F$ (i.e.,
$\|F-f\|_{\infty} \le \nu$). Our objective is to find the
$\epsilon$-approximate local minima of the underlying function $F$ while
avoiding the shallow local minima---arising because of the tolerance
$\nu$---which exist only in $f$. We propose a simple algorithm based on
stochastic gradient descent (SGD) on a smoothed version of $f$ that is
guaranteed to achieve our goal as long as $\nu \le O(\epsilon^{1.5}/d)$. We
also provide an almost matching lower bound showing that our algorithm achieves
optimal error tolerance $\nu$ among all algorithms making a polynomial number
of queries of $f$. As a concrete example, we show that our results can be
directly used to give sample complexities for learning a ReLU unit.
Neural Nets via Forward State Transformation and Backward Loss Transformation
Authors: Bart Jacobs, David Sprunger
Date: 2018-03-25 00:00:00

This article studies (multilayer perceptron) neural networks with an emphasis
on the transformations involved --- both forward and backward --- in order to
develop a semantical/logical perspective that is in line with standard program
semantics. The common two-pass neural network training algorithms make this
viewpoint particularly fitting. In the forward direction, neural networks act
as state transformers. In the reverse direction, however, neural networks
change losses of outputs to losses of inputs, thereby acting like a
(real-valued) predicate transformer. In this way, backpropagation is functorial
by construction, as shown earlier in recent other work. We illustrate this
perspective by training a simple instance of a neural network.