pwc-archive/papers-with-abstracts huggingface dataset:
On Study of the Reliable Fully Convolutional Networks with Tree Arranged Outputs (TAO-FCN) for Handwritten String Recognition
Authors: Song Wang, Jun Sun, Satoshi Naoi
Date: 2017-07-10 00:00:00

The handwritten string recognition is still a challengeable task, though the
powerful deep learning tools were introduced. In this paper, based on TAO-FCN,
we proposed an end-to-end system for handwritten string recognition. Compared
with the conventional methods, there is no preprocess nor manually designed
rules employed. With enough labelled data, it is easy to apply the proposed
method to different applications. Although the performance of the proposed
method may not be comparable with the state-of-the-art approaches, it's
usability and robustness are more meaningful for practical applications.
Submodular Variational Inference for Network Reconstruction
Authors: Lin Chen, Forrest W. Crawford, Amin Karbasi
Date: 2016-03-29 00:00:00
Tasks: Variational Inference

In real-world and online social networks, individuals receive and transmit
information in real time. Cascading information transmissions (e.g. phone
calls, text messages, social media posts) may be understood as a realization of
a diffusion process operating on the network, and its branching path can be
represented by a directed tree. The process only traverses and thus reveals a
limited portion of the edges. The network reconstruction/inference problem is
to infer the unrevealed connections. Most existing approaches derive a
likelihood and attempt to find the network topology maximizing the likelihood,
a problem that is highly intractable. In this paper, we focus on the network
reconstruction problem for a broad class of real-world diffusion processes,
exemplified by a network diffusion scheme called respondent-driven sampling
(RDS). We prove that under realistic and general models of network diffusion,
the posterior distribution of an observed RDS realization is a Bayesian
log-submodular model.We then propose VINE (Variational Inference for Network
rEconstruction), a novel, accurate, and computationally efficient variational
inference algorithm, for the network reconstruction problem under this model.
Crucially, we do not assume any particular probabilistic model for the
underlying network. VINE recovers any connected graph with high accuracy as
shown by our experimental results on real-life networks.
Learning Large-Scale Topological Maps Using Sum-Product Networks
Authors: Kaiyu Zheng
Date: 2017-06-11 00:00:00
Tasks: Attribute, Probabilistic Deep Learning

In order to perform complex actions in human environments, an autonomous
robot needs the ability to understand the environment, that is, to gather and
maintain spatial knowledge. Topological map is commonly used for representing
large scale, global maps such as floor plans. Although much work has been done
in topological map extraction, we have found little previous work on the
problem of learning the topological map using a probabilistic model. Learning a
topological map means learning the structure of the large-scale space and
dependency between places, for example, how the evidence of a group of places
influence the attributes of other places. This is an important step towards
planning complex actions in the environment. In this thesis, we consider the
problem of using probabilistic deep learning model to learn the topological
map, which is essentially a sparse undirected graph where nodes represent
places annotated with their semantic attributes (e.g. place category). We
propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
due to their unique properties. We present two methods for learning topological
maps using SPNs: the place grid method and the template-based method. We
contribute an algorithm that builds SPNs for graphs using template models. Our
experiments evaluate the ability of our models to enable robots to infer
semantic attributes and detect maps with novel semantic attribute arrangements.
Our results demonstrate their understanding of the topological map structure
and spatial relations between places.
Composition Properties of Inferential Privacy for Time-Series Data
Authors: Shuang Song, Kamalika Chaudhuri
Date: 2017-07-10 00:00:00
Tasks: Time Series, Time Series Analysis

With the proliferation of mobile devices and the internet of things,
developing principled solutions for privacy in time series applications has
become increasingly important. While differential privacy is the gold standard
for database privacy, many time series applications require a different kind of
guarantee, and a number of recent works have used some form of inferential
privacy to address these situations.
  However, a major barrier to using inferential privacy in practice is its lack
of graceful composition -- even if the same or related sensitive data is used
in multiple releases that are safe individually, the combined release may have
poor privacy properties. In this paper, we study composition properties of a
form of inferential privacy called Pufferfish when applied to time-series data.
We show that while general Pufferfish mechanisms may not compose gracefully, a
specific Pufferfish mechanism, called the Markov Quilt Mechanism, which was
recently introduced, has strong composition properties comparable to that of
pure differential privacy when applied to time series data.
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network
Authors: Jiaqi Li, Zhifeng Zhao, Rongpeng Li
Date: 2017-07-10 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection, Management

As an inevitable trend of future 5G networks, Software Defined architecture
has many advantages in providing central- ized control and flexible resource
management. But it is also confronted with various security challenges and
potential threats with emerging services and technologies. As the focus of
network security, Intrusion Detection Systems (IDS) are usually deployed
separately without collaboration. They are also unable to detect novel attacks
with limited intelligent abilities, which are hard to meet the needs of
software defined 5G. In this paper, we propose an intelligent intrusion system
taking the advances of software defined technology and artificial intelligence
based on Software Defined 5G architecture. It flexibly combines security
function mod- ules which are adaptively invoked under centralized management
and control with a globle view. It can also deal with unknown intrusions by
using machine learning algorithms. Evaluation results prove that the
intelligent intrusion detection system achieves a better performance.
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning
Authors: Jiyan Yang, Yin-Lam Chow, Christopher Ré, Michael W. Mahoney
Date: 2015-02-12 00:00:00
Tasks: regression

In recent years, stochastic gradient descent (SGD) methods and randomized
linear algebra (RLA) algorithms have been applied to many large-scale problems
in machine learning and data analysis. We aim to bridge the gap between these
two methods in solving constrained overdetermined linear regression
problems---e.g., $\ell_2$ and $\ell_1$ regression problems. We propose a hybrid
algorithm named pwSGD that uses RLA techniques for preconditioning and
constructing an importance sampling distribution, and then performs an SGD-like
iterative process with weighted sampling on the preconditioned system. We prove
that pwSGD inherits faster convergence rates that only depend on the lower
dimension of the linear system, while maintaining low computation complexity.
Particularly, when solving $\ell_1$ regression with size $n$ by $d$, pwSGD
returns an approximate solution with $\epsilon$ relative error in the objective
value in $\mathcal{O}(\log n \cdot \text{nnz}(A) + \text{poly}(d)/\epsilon^2)$
time. This complexity is uniformly better than that of RLA methods in terms of
both $\epsilon$ and $d$ when the problem is unconstrained. For $\ell_2$
regression, pwSGD returns an approximate solution with $\epsilon$ relative
error in the objective value and the solution vector measured in prediction
norm in $\mathcal{O}(\log n \cdot \text{nnz}(A) + \text{poly}(d)
\log(1/\epsilon) /\epsilon)$ time. We also provide lower bounds on the coreset
complexity for more general regression problems, indicating that still new
ideas will be needed to extend similar RLA preconditioning ideas to weighted
SGD algorithms for more general regression problems. Finally, the effectiveness
of such algorithms is illustrated numerically on both synthetic and real
datasets.
PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval
Authors: Weixun Zhou, Shawn Newsam, Congmin Li, Zhenfeng Shao
Date: 2017-06-11 00:00:00
Tasks: Image Retrieval, Land Cover Classification, Retrieval

Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data
of interest from large collections of remote sensing data, is a fundamental
task in remote sensing. Over the past several decades, there has been
significant effort to extract powerful feature representations for this task
since the retrieval performance depends on the representative strength of the
features. Benchmark datasets are also critical for developing, evaluating, and
comparing RSIR approaches. Current benchmark datasets are deficient in that 1)
they were originally collected for land use/land cover classification and not
image retrieval, 2) they are relatively small in terms of the number of classes
as well the number of sample images per class, and 3) the retrieval performance
has saturated. These limitations have severely restricted the development of
novel feature representations for RSIR, particularly the recent deep-learning
based features which require large amounts of training data. We therefore
present in this paper, a new large-scale remote sensing dataset termed
"PatternNet" that was collected specifically for RSIR. PatternNet was collected
from high-resolution imagery and contains 38 classes with 800 images per class.
We also provide a thorough review of RSIR approaches ranging from traditional
handcrafted feature based methods to recent deep learning based ones. We
evaluate over 35 methods to establish extensive baseline results for future
RSIR research using the PatternNet benchmark.
Anisotropic Diffusion-based Kernel Matrix Model for Face Liveness Detection
Authors: Changyong Yu, Yunde Jia
Date: 2017-07-10 00:00:00

Facial recognition and verification is a widely used biometric technology in
security system. Unfortunately, face biometrics is vulnerable to spoofing
attacks using photographs or videos. In this paper, we present an anisotropic
diffusion-based kernel matrix model (ADKMM) for face liveness detection to
prevent face spoofing attacks. We use the anisotropic diffusion to enhance the
edges and boundary locations of a face image, and the kernel matrix model to
extract face image features which we call the diffusion-kernel (D-K) features.
The D-K features reflect the inner correlation of the face image sequence. We
introduce convolution neural networks to extract the deep features, and then,
employ a generalized multiple kernel learning method to fuse the D-K features
and the deep features to achieve better performance. Our experimental
evaluation on the two publicly available datasets shows that the proposed
method outperforms the state-of-art face liveness detection methods.
Learning in High-Dimensional Multimedia Data: The State of the Art
Authors: Lianli Gao, Jingkuan Song, Xingyi Liu, Junming Shao, Jiajun Liu, Jie Shao
Date: 2017-07-10 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Management, Retrieval, Vocal Bursts Intensity Prediction

During the last decade, the deluge of multimedia data has impacted a wide
range of research areas, including multimedia retrieval, 3D tracking, database
management, data mining, machine learning, social media analysis, medical
imaging, and so on. Machine learning is largely involved in multimedia
applications of building models for classification and regression tasks etc.,
and the learning principle consists in designing the models based on the
information contained in the multimedia dataset. While many paradigms exist and
are widely used in the context of machine learning, most of them suffer from
the `curse of dimensionality', which means that some strange phenomena appears
when data are represented in a high-dimensional space. Given the high
dimensionality and the high complexity of multimedia data, it is important to
investigate new machine learning algorithms to facilitate multimedia data
analysis. To deal with the impact of high dimensionality, an intuitive way is
to reduce the dimensionality. On the other hand, some researchers devoted
themselves to designing some effective learning schemes for high-dimensional
data. In this survey, we cover feature transformation, feature selection and
feature encoding, three approaches fighting the consequences of the curse of
dimensionality. Next, we briefly introduce some recent progress of effective
learning algorithms. Finally, promising future trends on multimedia learning
are envisaged.
A Nested Attention Neural Hybrid Model for Grammatical Error Correction
Authors: Jianshu Ji, Qinlong Wang, Kristina Toutanova, Yongen Gong, Steven Truong, Jianfeng Gao
Date: 2017-07-07 00:00:00
Proceeding: ACL 2017 7
Tasks: Grammatical Error Correction, Machine Translation, Translation

Grammatical error correction (GEC) systems strive to correct both global
errors in word order and usage, and local errors in spelling and inflection.
Further developing upon recent work on neural machine translation, we propose a
new hybrid neural model with nested attention layers for GEC. Experiments show
that the new model can effectively correct errors of both types by
incorporating word and character-level information,and that the model
significantly outperforms previous neural models for GEC as measured on the
standard CoNLL-14 benchmark dataset. Further analysis also shows that the
superiority of the proposed model can be largely attributed to the use of the
nested attention mechanism, which has proven particularly effective in
correcting local errors that involve small edits in orthography.
Generation of discrete random variables in scalable frameworks
Authors: Giacomo Aletti
Date: 2016-11-21 00:00:00
Tasks: Discrete Choice Models

In this paper, we face the problem of simulating discrete random variables
with general and varying distributions in a scalable framework, where fully
parallelizable operations should be preferred. The new paradigm is inspired by
the context of discrete choice models. Compared to classical algorithms, we add
parallelized randomness, and we leave the final simulation of the random
variable to a single associative operation. We characterize the set of
algorithms that work in this way, and those algorithms that may have an
additive or multiplicative local noise. As a consequence, we could define a
natural way to solve some popular simulation problems.
Understanding Black-box Predictions via Influence Functions
Authors: Pang Wei Koh, Percy Liang
Date: 2017-03-14 00:00:00
Proceeding: ICML 2017 8

How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Authors: Stefan Wager, Susan Athey
Date: 2015-10-14 00:00:00
Tasks: Marketing, valid

Many scientific and engineering challenges -- ranging from personalized
medicine to customized marketing recommendations -- require an understanding of
treatment effect heterogeneity. In this paper, we develop a non-parametric
causal forest for estimating heterogeneous treatment effects that extends
Breiman's widely used random forest algorithm. In the potential outcomes
framework with unconfoundedness, we show that causal forests are pointwise
consistent for the true treatment effect, and have an asymptotically Gaussian
and centered sampling distribution. We also discuss a practical method for
constructing asymptotic confidence intervals for the true treatment effect that
are centered at the causal forest estimates. Our theoretical results rely on a
generic Gaussian theory for a large family of random forest algorithms. To our
knowledge, this is the first set of results that allows any type of random
forest, including classification and regression forests, to be used for
provably valid statistical inference. In experiments, we find causal forests to
be substantially more powerful than classical methods based on nearest-neighbor
matching, especially in the presence of irrelevant covariates.
Accelerated Stochastic Power Iteration
Authors: Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, Peng Xu
Date: 2017-07-10 00:00:00
Tasks: Dimensionality Reduction

Principal component analysis (PCA) is one of the most powerful tools in
machine learning. The simplest method for PCA, the power iteration, requires
$\mathcal O(1/\Delta)$ full-data passes to recover the principal component of a
matrix with eigen-gap $\Delta$. Lanczos, a significantly more complex method,
achieves an accelerated rate of $\mathcal O(1/\sqrt{\Delta})$ passes. Modern
applications, however, motivate methods that only ingest a subset of available
data, known as the stochastic setting. In the online stochastic setting, simple
algorithms like Oja's iteration achieve the optimal sample complexity $\mathcal
O(\sigma^2/\Delta^2)$. Unfortunately, they are fully sequential, and also
require $\mathcal O(\sigma^2/\Delta^2)$ iterations, far from the $\mathcal
O(1/\sqrt{\Delta})$ rate of Lanczos. We propose a simple variant of the power
iteration with an added momentum term, that achieves both the optimal sample
and iteration complexity. In the full-pass setting, standard analysis shows
that momentum achieves the accelerated rate, $\mathcal O(1/\sqrt{\Delta})$. We
demonstrate empirically that naively applying momentum to a stochastic method,
does not result in acceleration. We perform a novel, tight variance analysis
that reveals the "breaking-point variance" beyond which this acceleration does
not occur. By combining this insight with modern variance reduction techniques,
we construct stochastic PCA algorithms, for the online and offline setting,
that achieve an accelerated iteration complexity $\mathcal O(1/\sqrt{\Delta})$.
Due to the embarassingly parallel nature of our methods, this acceleration
translates directly to wall-clock time if deployed in a parallel environment.
Our approach is very general, and applies to many non-convex optimization
problems that can now be accelerated using the same technique.
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Authors: Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar
Date: 2017-06-17 00:00:00
Tasks: Classification, General Classification, Video Classification

The YouTube-8M video classification challenge requires teams to classify 0.7
million videos into one or more of 4,716 classes. In this Kaggle competition,
we placed in the top 3% out of 650 participants using released video and audio
features. Beyond that, we extend the original competition by including text
information in the classification, making this a truly multi-modal approach
with vision, audio and text. The newly introduced text data is termed as
YouTube-8M-Text. We present a classification framework for the joint use of
text, visual and audio features, and conduct an extensive set of experiments to
quantify the benefit that this additional mode brings. The inclusion of text
yields state-of-the-art results, e.g. 86.7% GAP on the YouTube-8M-Text
validation dataset.
PELESent: Cross-domain polarity classification using distant supervision
Authors: Edilson A. Corrêa Jr, Vanessa Q. Marinho, Leandro B. dos Santos, Thales F. C. Bertaglia, Marcos V. Treviso, Henrico B. Brum
Date: 2017-07-09 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis

The enormous amount of texts published daily by Internet users has fostered
the development of methods to analyze this content in several natural language
processing areas, such as sentiment analysis. The main goal of this task is to
classify the polarity of a message. Even though many approaches have been
proposed for sentiment analysis, some of the most successful ones rely on the
availability of large annotated corpus, which is an expensive and
time-consuming process. In recent years, distant supervision has been used to
obtain larger datasets. So, inspired by these techniques, in this paper we
extend such approaches to incorporate popular graphic symbols used in
electronic messages, the emojis, in order to create a large sentiment corpus
for Portuguese. Trained on almost one million tweets, several models were
tested in both same domain and cross-domain corpora. Our methods obtained very
competitive results in five annotated corpora from mixed domains (Twitter and
product reviews), which proves the domain-independent property of such
approach. In addition, our results suggest that the combination of emoticons
and emojis is able to properly capture the sentiment of a message.
Nonlinear Sequential Accepts and Rejects for Identification of Top Arms in Stochastic Bandits
Authors: Shahin Shahrampour, Vahid Tarokh
Date: 2017-07-09 00:00:00
Tasks: Multi-Armed Bandits

We address the M-best-arm identification problem in multi-armed bandits. A
player has a limited budget to explore K arms (M<K), and once pulled, each arm
yields a reward drawn (independently) from a fixed, unknown distribution. The
goal is to find the top M arms in the sense of expected reward. We develop an
algorithm which proceeds in rounds to deactivate arms iteratively. At each
round, the budget is divided by a nonlinear function of remaining arms, and the
arms are pulled correspondingly. Based on a decision rule, the deactivated arm
at each round may be accepted or rejected. The algorithm outputs the accepted
arms that should ideally be the top M arms. We characterize the decay rate of
the misidentification probability and establish that the nonlinear budget
allocation proves to be useful for different problem environments (described by
the number of competitive arms). We provide comprehensive numerical experiments
showing that our algorithm outperforms the state-of-the-art using suitable
nonlinearity.