pwc-archive/papers-with-abstracts huggingface dataset:
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
Authors: Fengan Li, Lingjiao Chen, Yijing Zeng, Arun Kumar, Jeffrey F. Naughton, Jignesh M. Patel, Xi Wu
Date: 2017-02-22 00:00:00
Tasks: Data Compression, Open-Ended Question Answering, Text Compression

Data compression is a popular technique for improving the efficiency of data
processing workloads such as SQL queries and more recently, machine learning
(ML) with classical batch gradient methods. But the efficacy of such ideas for
mini-batch stochastic gradient descent (MGD), arguably the workhorse algorithm
of modern ML, is an open question. MGD's unique data access pattern renders
prior art, including those designed for batch gradient methods, less effective.
We fill this crucial research gap by proposing a new lossless compression
scheme we call tuple-oriented compression (TOC) that is inspired by an unlikely
source, the string/text compression scheme Lempel-Ziv-Welch, but tailored to
MGD in a way that preserves tuple boundaries within mini-batches. We then
present a suite of novel compressed matrix operation execution techniques
tailored to the TOC compression scheme that operate directly over the
compressed data representation and avoid decompression overheads. An extensive
empirical evaluation with real-world datasets shows that TOC consistently
achieves substantial compression ratios by up to 51x and reduces runtimes for
MGD workloads by up to 10.2x in popular ML systems.
A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Authors: Enrico De Santis, Antonello Rizzi, Alireza Sadeghian
Date: 2016-04-16 00:00:00
Tasks: Decision Making, energy trading, Management

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems
(FIS) are nowadays widely adopted as hybrid techniques in commercial and
industrial environment. In this paper we present an interesting application of
the fuzzy-GA paradigm to Smart Grids. The main aim consists in performing
decision making for power flow management tasks in the proposed microgrid model
equipped by renewable sources and an energy storage system, taking into account
the economical profit in energy trading with the main-grid. In particular, this
study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for
tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover
a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform
decision making in the microgrid. The HGA rationale focuses on a particular
encoding scheme, based on control genes and parametric genes applied to the
optimization of the FIS parameters, allowing to perform a reduction in the
structural complexity of the RB. This approach will be referred in the
following as fuzzy-HGA. Results are compared with a simpler approach based on a
classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned,
while the number of fuzzy rules is fixed in advance. Experiments shows how the
fuzzy-HGA approach adopted for the synthesis of the proposed controller
outperforms the classic fuzzy-GA scheme, increasing the accounting profit by
67\% in the considered energy trading problem yielding at the same time a
simpler RB.
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Authors: Gong Cheng, Junwei Han, Xiaoqiang lu
Date: 2017-03-01 00:00:00
Tasks: Classification, Diversity, General Classification, Scene Classification

Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.
Learning convolutional neural network to maximize Pos@Top performance measure
Authors: Yanyan Geng, Ru-Ze Liang, Weizhi Li, Jingbin Wang, Gaoyuan Liang, Chenhao Xu, Jing-Yan Wang
Date: 2016-09-27 00:00:00
Tasks: BIG-bench Machine Learning, POS

In the machine learning problems, the performance measure is used to evaluate
the machine learning models. Recently, the number positive data points ranked
at the top positions (Pos@Top) has been a popular performance measure in the
machine learning community. In this paper, we propose to learn a convolutional
neural network (CNN) model to maximize the Pos@Top performance measure. The CNN
model is used to represent the multi-instance data point, and a classifier
function is used to predict the label from the its CNN representation. We
propose to minimize the loss function of Pos@Top over a training set to learn
the filters of CNN and the classifier parameter. The classifier parameter
vector is solved by the Lagrange multiplier method, and the filters are updated
by the gradient descent method alternately in an iterative algorithm.
Experiments over benchmark data sets show that the proposed method outperforms
the state-of-the-art Pos@Top maximization methods.
Learning Conversational Systems that Interleave Task and Non-Task Content
Authors: Zhou Yu, Alan W. black, Alexander I. Rudnicky
Date: 2017-03-01 00:00:00
Tasks: Reinforcement Learning

Task-oriented dialog systems have been applied in various tasks, such as
automated personal assistants, customer service providers and tutors. These
systems work well when users have clear and explicit intentions that are
well-aligned to the systems' capabilities. However, they fail if users
intentions are not explicit. To address this shortcoming, we propose a
framework to interleave non-task content (i.e. everyday social conversation)
into task conversations. When the task content fails, the system can still keep
the user engaged with the non-task content. We trained a policy using
reinforcement learning algorithms to promote long-turn conversation coherence
and consistency, so that the system can have smooth transitions between task
and non-task content. To test the effectiveness of the proposed framework, we
developed a movie promotion dialog system. Experiments with human users
indicate that a system that interleaves social and task content achieves a
better task success rate and is also rated as more engaging compared to a pure
task-oriented system.
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
Authors: William Lotter, Gabriel Kreiman, David Cox
Date: 2016-05-25 00:00:00
Tasks: Object Recognition, Video Prediction

While great strides have been made in using deep learning algorithms to solve
supervised learning tasks, the problem of unsupervised learning - leveraging
unlabeled examples to learn about the structure of a domain - remains a
difficult unsolved challenge. Here, we explore prediction of future frames in a
video sequence as an unsupervised learning rule for learning about the
structure of the visual world. We describe a predictive neural network
("PredNet") architecture that is inspired by the concept of "predictive coding"
from the neuroscience literature. These networks learn to predict future frames
in a video sequence, with each layer in the network making local predictions
and only forwarding deviations from those predictions to subsequent network
layers. We show that these networks are able to robustly learn to predict the
movement of synthetic (rendered) objects, and that in doing so, the networks
learn internal representations that are useful for decoding latent object
parameters (e.g. pose) that support object recognition with fewer training
views. We also show that these networks can scale to complex natural image
streams (car-mounted camera videos), capturing key aspects of both egocentric
movement and the movement of objects in the visual scene, and the
representation learned in this setting is useful for estimating the steering
angle. Altogether, these results suggest that prediction represents a powerful
framework for unsupervised learning, allowing for implicit learning of object
and scene structure.
A Joint Identification Approach for Argumentative Writing Revisions
Authors: Fan Zhang, Diane Litman
Date: 2017-02-28 00:00:00
Tasks: Classification, General Classification

Prior work on revision identification typically uses a pipeline method:
revision extraction is first conducted to identify the locations of revisions
and revision classification is then conducted on the identified revisions. Such
a setting propagates the errors of the revision extraction step to the revision
classification step. This paper proposes an approach that identifies the
revision location and the revision type jointly to solve the issue of error
propagation. It utilizes a sequence representation of revisions and conducts
sequence labeling for revision identification. A mutation-based approach is
utilized to update identification sequences. Results demonstrate that our
proposed approach yields better performance on both revision location
extraction and revision type classification compared to a pipeline baseline.
Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach
Authors: Kasthurirengan Suresh, Samuel Silva, Johnathan Votion, Yongcan Cao
Date: 2017-02-28 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Management

Data-target pairing is an important step towards multi-target localization
for the intelligent operation of unmanned systems. Target localization plays a
crucial role in numerous applications, such as search, and rescue missions,
traffic management and surveillance. The objective of this paper is to present
an innovative target location learning approach, where numerous machine
learning approaches, including K-means clustering and supported vector machines
(SVM), are used to learn the data pattern across a list of spatially
distributed sensors. To enable the accurate data association from different
sensors for accurate target localization, appropriate data pre-processing is
essential, which is then followed by the application of different machine
learning algorithms to appropriately group data from different sensors for the
accurate localization of multiple targets. Through simulation examples, the
performance of these machine learning algorithms is quantified and compared.
A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
Authors: Grigorios G. Chrysos, Epameinondas Antonakos, Patrick Snape, Akshay Asthana, Stefanos Zafeiriou
Date: 2016-03-18 00:00:00
Tasks: Face Alignment, Face Detection, Face Recognition

Recently, technologies such as face detection, facial landmark localisation
and face recognition and verification have matured enough to provide effective
and efficient solutions for imagery captured under arbitrary conditions
(referred to as "in-the-wild"). This is partially attributed to the fact that
comprehensive "in-the-wild" benchmarks have been developed for face detection,
landmark localisation and recognition/verification. A very important technology
that has not been thoroughly evaluated yet is deformable face tracking
"in-the-wild". Until now, the performance has mainly been assessed
qualitatively by visually assessing the result of a deformable face tracking
technology on short videos. In this paper, we perform the first, to the best of
our knowledge, thorough evaluation of state-of-the-art deformable face tracking
pipelines using the recently introduced 300VW benchmark. We evaluate many
different architectures focusing mainly on the task of on-line deformable face
tracking. In particular, we compare the following general strategies: (a)
generic face detection plus generic facial landmark localisation, (b) generic
model free tracking plus generic facial landmark localisation, as well as (c)
hybrid approaches using state-of-the-art face detection, model free tracking
and facial landmark localisation technologies. Our evaluation reveals future
avenues for further research on the topic.
Discrete Wavelet Transform Based Algorithm for Recognition of QRS Complexes
Authors: Rachid Haddadi, Elhassane Abdelmounim, Mustapha El Hanine, Abdelaziz Belaguid
Date: 2017-02-28 00:00:00

This paper proposes the application of Discrete Wavelet Transform (DWT) to
detect the QRS (ECG is characterized by a recurrent wave sequence of P, QRS and
T-wave) of an electrocardiogram (ECG) signal. Wavelet Transform provides
localization in both time and frequency. In preprocessing stage, DWT is used to
remove the baseline wander in the ECG signal. The performance of the algorithm
of QRS detection is evaluated against the standard MIT BIH (Massachusetts
Institute of Technology, Beth Israel Hospital) Arrhythmia database. The average
QRS complexes detection rate of 98.1 % is achieved.
Deep Image Harmonization
Authors: Yi-Hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, Ming-Hsuan Yang
Date: 2017-02-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Harmonization

Compositing is one of the most common operations in photo editing. To
generate realistic composites, the appearances of foreground and background
need to be adjusted to make them compatible. Previous approaches to harmonize
composites have focused on learning statistical relationships between
hand-crafted appearance features of the foreground and background, which is
unreliable especially when the contents in the two layers are vastly different.
In this work, we propose an end-to-end deep convolutional neural network for
image harmonization, which can capture both the context and semantic
information of the composite images during harmonization. We also introduce an
efficient way to collect large-scale and high-quality training data that can
facilitate the training process. Experiments on the synthesized dataset and
real composite images show that the proposed network outperforms previous
state-of-the-art methods.
On the Power of Learning from $k$-Wise Queries
Authors: Vitaly Feldman, Badih Ghazi
Date: 2017-02-28 00:00:00
Tasks: PAC learning

Several well-studied models of access to data samples, including statistical
queries, local differential privacy and low-communication algorithms rely on
queries that provide information about a function of a single sample. (For
example, a statistical query (SQ) gives an estimate of $Ex_{x \sim D}[q(x)]$
for any choice of the query function $q$ mapping $X$ to the reals, where $D$ is
an unknown data distribution over $X$.) Yet some data analysis algorithms rely
on properties of functions that depend on multiple samples. Such algorithms
would be naturally implemented using $k$-wise queries each of which is
specified by a function $q$ mapping $X^k$ to the reals. Hence it is natural to
ask whether algorithms using $k$-wise queries can solve learning problems more
efficiently and by how much.
  Blum, Kalai and Wasserman (2003) showed that for any weak PAC learning
problem over a fixed distribution, the complexity of learning with $k$-wise SQs
is smaller than the (unary) SQ complexity by a factor of at most $2^k$. We show
that for more general problems over distributions the picture is substantially
richer. For every $k$, the complexity of distribution-independent PAC learning
with $k$-wise queries can be exponentially larger than learning with
$(k+1)$-wise queries. We then give two approaches for simulating a $k$-wise
query using unary queries. The first approach exploits the structure of the
problem that needs to be solved. It generalizes and strengthens (exponentially)
the results of Blum et al.. It allows us to derive strong lower bounds for
learning DNF formulas and stochastic constraint satisfaction problems that hold
against algorithms using $k$-wise queries. The second approach exploits the
$k$-party communication complexity of the $k$-wise query function.
On the Troll-Trust Model for Edge Sign Prediction in Social Networks
Authors: Géraud Le Falher, Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale
Date: 2016-06-01 00:00:00

In the problem of edge sign prediction, we are given a directed graph
(representing a social network), and our task is to predict the binary labels
of the edges (i.e., the positive or negative nature of the social
relationships). Many successful heuristics for this problem are based on the
troll-trust features, estimating at each node the fraction of outgoing and
incoming positive/negative edges. We show that these heuristics can be
understood, and rigorously analyzed, as approximators to the Bayes optimal
classifier for a simple probabilistic model of the edge labels. We then show
that the maximum likelihood estimator for this model approximately corresponds
to the predictions of a Label Propagation algorithm run on a transformed
version of the original social graph. Extensive experiments on a number of
real-world datasets show that this algorithm is competitive against
state-of-the-art classifiers in terms of both accuracy and scalability.
Finally, we show that troll-trust features can also be used to derive online
learning algorithms which have theoretical guarantees even when edges are
adversarially labeled.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Authors: Alexandra Chouldechova
Date: 2017-02-28 00:00:00
Tasks: Fairness, Prediction

Recidivism prediction instruments (RPI's) provide decision makers with an
assessment of the likelihood that a criminal defendant will reoffend at a
future point in time. While such instruments are gaining increasing popularity
across the country, their use is attracting tremendous controversy. Much of the
controversy concerns potential discriminatory bias in the risk assessments that
are produced. This paper discusses several fairness criteria that have recently
been applied to assess the fairness of recidivism prediction instruments. We
demonstrate that the criteria cannot all be simultaneously satisfied when
recidivism prevalence differs across groups. We then show how disparate impact
can arise when a recidivism prediction instrument fails to satisfy the
criterion of error rate balance.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Authors: H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Agüera y Arcas
Date: 2016-02-17 00:00:00
Tasks: Federated Learning, Speech Recognition

Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.
Robust Sparse Estimation Tasks in High Dimensions
Authors: Jerry Li
Date: 2017-02-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper we initiate the study of whether or not sparse estimation tasks
can be performed efficiently in high dimensions, in the robust setting where an
$\eps$-fraction of samples are corrupted adversarially. We study the natural
robust version of two classical sparse estimation problems, namely, sparse mean
estimation and sparse PCA in the spiked covariance model. For both of these
problems, we provide the first efficient algorithms that provide non-trivial
error guarantees in the presence of noise, using only a number of samples which
is similar to the number required for these problems without noise. In
particular, our sample complexities are sublinear in the ambient dimension $d$.
Our work also suggests evidence for new computational-vs-statistical gaps for
these problems (similar to those for sparse PCA without noise) which only arise
in the presence of noise.
SceneSeer: 3D Scene Design with Natural Language
Authors: Angel X. Chang, Mihail Eric, Manolis Savva, Christopher D. Manning
Date: 2017-02-28 00:00:00
Tasks: Scene Generation, Text to 3D

Designing 3D scenes is currently a creative task that requires significant
expertise and effort in using complex 3D design interfaces. This effortful
design process starts in stark contrast to the easiness with which people can
use language to describe real and imaginary environments. We present SceneSeer:
an interactive text to 3D scene generation system that allows a user to design
3D scenes using natural language. A user provides input text from which we
extract explicit constraints on the objects that should appear in the scene.
Given these explicit constraints, the system then uses a spatial knowledge base
learned from an existing database of 3D scenes and 3D object models to infer an
arrangement of the objects forming a natural scene matching the input
description. Using textual commands the user can then iteratively refine the
created scene by adding, removing, replacing, and manipulating objects. We
evaluate the quality of 3D scenes generated by SceneSeer in a perceptual
evaluation experiment where we compare against manually designed scenes and
simpler baselines for 3D scene generation. We demonstrate how the generated
scenes can be iteratively refined through simple natural language commands.