pwc-archive/papers-with-abstracts huggingface dataset:
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
Authors: Shaowei Wang, Liusheng Huang, Pengzhan Wang, Hongli Xu, Wei Yang
Date: 2017-01-25 00:00:00
Tasks: Ensemble Learning, Ensemble Pruning

Ensemble learning has been widely employed by mobile applications, ranging
from environmental sensing to activity recognitions. One of the fundamental
issue in ensemble learning is the trade-off between classification accuracy and
computational costs, which is the goal of ensemble pruning. During
crowdsourcing, the centralized aggregator releases ensemble learning models to
a large number of mobile participants for task evaluation or as the
crowdsourcing learning results, while different participants may seek for
different levels of the accuracy-cost trade-off. However, most of existing
ensemble pruning approaches consider only one identical level of such
trade-off. In this study, we present an efficient ensemble pruning framework
for personalized accuracy-cost trade-offs via multi-objective optimization.
Specifically, for the commonly used linear-combination style of the trade-off,
we provide an objective-mixture optimization to further reduce the number of
ensemble candidates. Experimental results show that our framework is highly
efficient for personalized ensemble pruning, and achieves much better pruning
performance with objective-mixture optimization when compared to state-of-art
approaches.
Historic Emergence of Diversity in Painting: Heterogeneity in Chromatic Distance in Images and Characterization of Massive Painting Data Set
Authors: Byunghwee Lee, Daniel Kim, Seunghye Sun, Hawoong Jeong, Juyong Park
Date: 2017-01-25 00:00:00
Tasks: Diversity

Painting is an art form that has long functioned as a major channel for the
creative expression and communication of humans, its evolution taking place
under an interplay with the science, technology, and social environments of the
times. Therefore, understanding the process based on comprehensive data could
shed light on how humans acted and manifested creatively under changing
conditions. Yet, there exist few systematic frameworks that characterize the
process for painting, which would require robust statistical methods for
defining painting characteristics and identifying human's creative
developments, and data of high quality and sufficient quantity. Here we propose
that the color contrast of a painting image signifying the heterogeneity in
inter-pixel chromatic distance can be a useful representation of its style,
integrating both the color and geometry. From the color contrasts of paintings
from a large-scale, comprehensive archive of 179,853 high-quality images
spanning several centuries we characterize the temporal evolutionary patterns
of paintings, and present a deep study of an extraordinary expansion in
creative diversity and individuality that came to define the modern era.
An Edge Driven Wavelet Frame Model for Image Restoration
Authors: Jae Kyu Choi, Bin Dong, Xiaoqun Zhang
Date: 2017-01-25 00:00:00
Tasks: Deblurring, Image Inpainting, Image Restoration

Wavelet frame systems are known to be effective in capturing singularities
from noisy and degraded images. In this paper, we introduce a new edge driven
wavelet frame model for image restoration by approximating images as piecewise
smooth functions. With an implicit representation of image singularities sets,
the proposed model inflicts different strength of regularization on smooth and
singular image regions and edges. The proposed edge driven model is robust to
both image approximation and singularity estimation. The implicit formulation
also enables an asymptotic analysis of the proposed models and a rigorous
connection between the discrete model and a general continuous variational
model. Finally, numerical results on image inpainting and deblurring show that
the proposed model is compared favorably against several popular image
restoration models.
Mahalanobis Distance for Class Averaging of Cryo-EM Images
Authors: Tejal Bhamre, Zhizhen Zhao, Amit Singer
Date: 2016-11-10 00:00:00
Tasks: General Classification

Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a
technique in which the 3D structure of a molecule needs to be determined from
its contrast transfer function (CTF) affected, noisy 2D projection images taken
at unknown viewing directions. One of the main challenges in cryo-EM is the
typically low signal to noise ratio (SNR) of the acquired images. 2D
classification of images, followed by class averaging, improves the SNR of the
resulting averages, and is used for selecting particles from micrographs and
for inspecting the particle images. We introduce a new affinity measure, akin
to the Mahalanobis distance, to compare cryo-EM images belonging to different
defocus groups. The new similarity measure is employed to detect similar
images, thereby leading to an improved algorithm for class averaging. We
evaluate the performance of the proposed class averaging procedure on synthetic
datasets, obtaining state of the art classification.
Hierarchical Recurrent Attention Network for Response Generation
Authors: Chen Xing, Wei Wu, Yu Wu, Ming Zhou, YaLou Huang, Wei-Ying Ma
Date: 2017-01-25 00:00:00
Tasks: Response Generation

We study multi-turn response generation in chatbots where a response is
generated according to a conversation context. Existing work has modeled the
hierarchy of the context, but does not pay enough attention to the fact that
words and utterances in the context are differentially important. As a result,
they may lose important information in context and generate irrelevant
responses. We propose a hierarchical recurrent attention network (HRAN) to
model both aspects in a unified framework. In HRAN, a hierarchical attention
mechanism attends to important parts within and among utterances with word
level attention and utterance level attention respectively. With the word level
attention, hidden vectors of a word level encoder are synthesized as utterance
vectors and fed to an utterance level encoder to construct hidden
representations of the context. The hidden vectors of the context are then
processed by the utterance level attention and formed as context vectors for
decoding the response. Empirical studies on both automatic evaluation and human
judgment show that HRAN can significantly outperform state-of-the-art models
for multi-turn response generation.
CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression
Authors: Marcella Astrid, Seung-Ik Lee
Date: 2017-01-25 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional Neural Networks (CNNs) has shown a great success in many areas
including complex image classification tasks. However, they need a lot of
memory and computational cost, which hinders them from running in relatively
low-end smart devices such as smart phones. We propose a CNN compression method
based on CP-decomposition and Tensor Power Method. We also propose an iterative
fine tuning, with which we fine-tune the whole network after decomposing each
layer, but before decomposing the next layer. Significant reduction in memory
and computation cost is achieved compared to state-of-the-art previous work
with no more accuracy loss.
jsCoq: Towards Hybrid Theorem Proving Interfaces
Authors: Emilio Jesús Gallego Arias, Benoît Pin, Pierre Jouvelot
Date: 2017-01-25 00:00:00
Tasks: Automated Theorem Proving

We describe jsCcoq, a new platform and user environment for the Coq
interactive proof assistant. The jsCoq system targets the HTML5-ECMAScript 2015
specification, and it is typically run inside a standards-compliant browser,
without the need of external servers or services. Targeting educational use,
jsCoq allows the user to start interaction with proof scripts right away,
thanks to its self-contained nature. Indeed, a full Coq environment is packed
along the proof scripts, easing distribution and installation. Starting to use
jsCoq is as easy as clicking on a link. The current release ships more than 10
popular Coq libraries, and supports popular books such as Software Foundations
or Certified Programming with Dependent Types. The new target platform has
opened up new interaction and display possibilities. It has also fostered the
development of some new Coq-related technology. In particular, we have
implemented a new serialization-based protocol for interaction with the proof
assistant, as well as a new package format for library distribution.
Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
Authors: Guillermo Vigueras, Manuel Carro, Salvador Tamarit, Julio Mariño
Date: 2017-01-25 00:00:00
Tasks: Reinforcement Learning

The current trends in next-generation exascale systems go towards integrating
a wide range of specialized (co-)processors into traditional supercomputers.
Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per
surface unit, opening the access of heterogeneous platforms to a wider range of
users is an important problem to be tackled. However, heterogeneous platforms
limit the portability of the applications and increase development complexity
due to the programming skills required. Program transformation can help make
programming heterogeneous systems easier by defining a step-wise transformation
process that translates a given initial code into a semantically equivalent
final code, but adapted to a specific platform. Program transformation systems
require the definition of efficient transformation strategies to tackle the
combinatorial problem that emerges due to the large set of transformations
applicable at each step of the process. In this paper we propose a machine
learning-based approach to learn heuristics to define program transformation
strategies. Our approach proposes a novel combination of reinforcement learning
and classification methods to efficiently tackle the problems inherent to this
type of systems. Preliminary results demonstrate the suitability of this
approach.
Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation
Authors: Tong Shen, Guosheng Lin, Chunhua Shen, Ian Reid
Date: 2017-01-25 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Semantic image segmentation is a fundamental task in image understanding.
Per-pixel semantic labelling of an image benefits greatly from the ability to
consider region consistency both locally and globally. However, many Fully
Convolutional Network based methods do not impose such consistency, which may
give rise to noisy and implausible predictions. We address this issue by
proposing a dense multi-label network module that is able to encourage the
region consistency at different levels. This simple but effective module can be
easily integrated into any semantic segmentation systems. With comprehensive
experiments, we show that the dense multi-label can successfully remove the
implausible labels and clear the confusion so as to boost the performance of
semantic segmentation systems.
On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers
Authors: Nayyar A. Zaidi, Yang Du, Geoffrey I. Webb
Date: 2017-01-24 00:00:00
Tasks: Attribute

Learning algorithms that learn linear models often have high representation
bias on real-world problems. In this paper, we show that this representation
bias can be greatly reduced by discretization. Discretization is a common
procedure in machine learning that is used to convert a quantitative attribute
into a qualitative one. It is often motivated by the limitation of some
learners to qualitative data. Discretization loses information, as fewer
distinctions between instances are possible using discretized data relative to
undiscretized data. In consequence, where discretization is not essential, it
might appear desirable to avoid it. However, it has been shown that
discretization often substantially reduces the error of the linear generative
Bayesian classifier naive Bayes. This motivates a systematic study of the
effectiveness of discretizing quantitative attributes for other linear
classifiers. In this work, we study the effect of discretization on the
performance of linear classifiers optimizing three distinct discriminative
objective functions --- logistic regression (optimizing negative
log-likelihood), support vector classifiers (optimizing hinge loss) and a
zero-hidden layer artificial neural network (optimizing mean-square-error). We
show that discretization can greatly increase the accuracy of these linear
discriminative learners by reducing their representation bias, especially on
big datasets. We substantiate our claims with an empirical study on $42$
benchmark datasets.
Artificial Intelligence Approaches To UCAV Autonomy
Authors: Amir Husain, Bruce Porter
Date: 2017-01-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper covers a number of approaches that leverage Artificial
Intelligence algorithms and techniques to aid Unmanned Combat Aerial Vehicle
(UCAV) autonomy. An analysis of current approaches to autonomous control is
provided followed by an exploration of how these techniques can be extended and
enriched with AI techniques including Artificial Neural Networks (ANN),
Ensembling and Reinforcement Learning (RL) to evolve control strategies for
UCAVs.
On Robustness in Multilayer Interdependent Network
Authors: Joydeep Banerjee, Chenyang Zhou, Arunabha Sen
Date: 2017-01-24 00:00:00

Critical Infrastructures like power and communication networks are highly
interdependent on each other for their full functionality. Many significant
research have been pursued to model the interdependency and failure analysis of
these interdependent networks. However, most of these models fail to capture
the complex interdependencies that might actually exist between the
infrastructures. The \emph{Implicative Interdependency Model} that utilizes
Boolean Logic to capture complex interdependencies was recently proposed which
overcome the limitations of the existing models. A number of problems were
studies based on this model. In this paper we study the \textit{Robustness}
problem in Interdependent Power and Communication Network. The robustness is
defined with respect to two parameters $K \in I^{+} \cup \{0\}$ and $\rho \in
(0,1]$. We utilized the \emph{Implicative Interdependency Model} model to
capture the complex interdependency between the two networks. The model
classifies the interdependency relations into four cases. Computational
complexity of the problem is analyzed for each of these cases. A polynomial
time algorithm is designed for the first case that outputs the optimal
solution. All the other cases are proved to be NP-complete. An
in-approximability bound is provided for the third case. For the general case
we formulate an Integer Linear Program to get the optimal solution and a
polynomial time heuristic. The applicability of the heuristic is evaluated
using power and communication network data of Maricopa County, Arizona. The
experimental results showed that the heuristic almost always produced near
optimal value of parameter $K$ for $\rho < 0.42$.
Expectation Consistent Approximate Inference: Generalizations and Convergence
Authors: Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter
Date: 2016-02-25 00:00:00

Approximations of loopy belief propagation, including expectation propagation
and approximate message passing, have attracted considerable attention for
probabilistic inference problems. This paper proposes and analyzes a
generalization of Opper and Winther's expectation consistent (EC) approximate
inference method. The proposed method, called Generalized Expectation
Consistency (GEC), can be applied to both maximum a posteriori (MAP) and
minimum mean squared error (MMSE) estimation. Here we characterize its fixed
points, convergence, and performance relative to the replica prediction of
optimality.
Deep Stereo Matching with Dense CRF Priors
Authors: Ron Slossberg, Aaron Wetzler, Ron Kimmel
Date: 2016-12-06 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Stereo reconstruction from rectified images has recently been revisited
within the context of deep learning. Using a deep Convolutional Neural Network
to obtain patch-wise matching cost volumes has resulted in state of the art
stereo reconstruction on classic datasets like Middlebury and Kitti. By
introducing this cost into a classical stereo pipeline, the final results are
improved dramatically over non-learning based cost models. However these
pipelines typically include hand engineered post processing steps to
effectively regularize and clean the result. Here, we show that it is possible
to take a more holistic approach by training a fully end-to-end network which
directly includes regularization in the form of a densely connected Conditional
Random Field (CRF) that acts as a prior on inter-pixel interactions. We
demonstrate that our approach on both synthetic and real world datasets
outperforms an alternative end-to-end network and compares favorably to more
hand engineered approaches.
RNN Approaches to Text Normalization: A Challenge
Authors: Richard Sproat, Navdeep Jaitly
Date: 2016-10-31 00:00:00
Tasks: Text Normalization, text-to-speech, Text to Speech

This paper presents a challenge to the community: given a large corpus of
written text aligned to its normalized spoken form, train an RNN to learn the
correct normalization function. We present a data set of general text where the
normalizations were generated using an existing text normalization component of
a text-to-speech system. This data set will be released open-source in the near
future.
  We also present our own experiments with this data set with a variety of
different RNN architectures. While some of the architectures do in fact produce
very good results when measured in terms of overall accuracy, the errors that
are produced are problematic, since they would convey completely the wrong
message if such a system were deployed in a speech application. On the other
hand, we show that a simple FST-based filter can mitigate those errors, and
achieve a level of accuracy not achievable by the RNN alone.
  Though our conclusions are largely negative on this point, we are actually
not arguing that the text normalization problem is intractable using an pure
RNN approach, merely that it is not going to be something that can be solved
merely by having huge amounts of annotated text data and feeding that to a
general RNN model. And when we open-source our data, we will be providing a
novel data set for sequence-to-sequence modeling in the hopes that the the
community can find better solutions.
  The data used in this work have been released and are available at:
https://github.com/rwsproat/text-normalization-data
Statistical limits of spiked tensor models
Authors: Amelia Perry, Alexander S. Wein, Afonso S. Bandeira
Date: 2016-12-22 00:00:00

We study the statistical limits of both detecting and estimating a rank-one
deformation of a symmetric random Gaussian tensor. We establish upper and lower
bounds on the critical signal-to-noise ratio, under a variety of priors for the
planted vector: (i) a uniformly sampled unit vector, (ii) i.i.d. $\pm 1$
entries, and (iii) a sparse vector where a constant fraction $\rho$ of entries
are i.i.d. $\pm 1$ and the rest are zero. For each of these cases, our upper
and lower bounds match up to a $1+o(1)$ factor as the order $d$ of the tensor
becomes large. For sparse signals (iii), our bounds are also asymptotically
tight in the sparse limit $\rho \to 0$ for any fixed $d$ (including the $d=2$
case of sparse PCA). Our upper bounds for (i) demonstrate a phenomenon
reminiscent of the work of Baik, Ben Arous and P\'ech\'e: an `eigenvalue' of a
perturbed tensor emerges from the bulk at a strictly lower signal-to-noise
ratio than when the perturbation itself exceeds the bulk; we quantify the size
of this effect. We also provide some general results for larger classes of
priors. In particular, the large $d$ asymptotics of the threshold location
differs between problems with discrete priors versus continuous priors.
Finally, for priors (i) and (ii) we carry out the replica prediction from
statistical physics, which is conjectured to give the exact
information-theoretic threshold for any fixed $d$.
  Of independent interest, we introduce a new improvement to the second moment
method for contiguity, on which our lower bounds are based. Our technique
conditions away from rare `bad' events that depend on interactions between the
signal and noise. This enables us to close $\sqrt{2}$-factor gaps present in
several previous works.
Multi-Layer Generalized Linear Estimation
Authors: Andre Manoel, Florent Krzakala, Marc Mézard, Lenka Zdeborová
Date: 2017-01-24 00:00:00
Tasks: compressed sensing

We consider the problem of reconstructing a signal from multi-layered
(possibly) non-linear measurements. Using non-rigorous but standard methods
from statistical physics we present the Multi-Layer Approximate Message Passing
(ML-AMP) algorithm for computing marginal probabilities of the corresponding
estimation problem and derive the associated state evolution equations to
analyze its performance. We also give the expression of the asymptotic free
energy and the minimal information-theoretically achievable reconstruction
error. Finally, we present some applications of this measurement model for
compressed sensing and perceptron learning with structured matrices/patterns,
and for a simple model of estimation of latent variables in an auto-encoder.
Deep Network Guided Proof Search
Authors: Sarah Loos, Geoffrey Irving, Christian Szegedy, Cezary Kaliszyk
Date: 2017-01-24 00:00:00
Tasks: Deep Learning, Game of Go, Image Captioning, Machine Translation, Object Recognition, speech-recognition, Speech Recognition, Translation

Deep learning techniques lie at the heart of several significant AI advances
in recent years including object recognition and detection, image captioning,
machine translation, speech recognition and synthesis, and playing the game of
Go. Automated first-order theorem provers can aid in the formalization and
verification of mathematical theorems and play a crucial role in program
analysis, theory reasoning, security, interpolation, and system verification.
Here we suggest deep learning based guidance in the proof search of the theorem
prover E. We train and compare several deep neural network models on the traces
of existing ATP proofs of Mizar statements and use them to select processed
clauses during proof search. We give experimental evidence that with a hybrid,
two-phase approach, deep learning based guidance can significantly reduce the
average number of proof search steps while increasing the number of theorems
proved. Using a few proof guidance strategies that leverage deep neural
networks, we have found first-order proofs of 7.36% of the first-order logic
translations of the Mizar Mathematical Library theorems that did not previously
have ATP generated proofs. This increases the ratio of statements in the corpus
with ATP generated proofs from 56% to 59%.