pwc-archive/papers-with-abstracts huggingface dataset:
State Space representation of non-stationary Gaussian Processes
Authors: Alessio Benavoli, Marco Zaffalon
Date: 2016-01-07 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes

The state space (SS) representation of Gaussian processes (GP) has recently
gained a lot of interest. The main reason is that it allows to compute GPs
based inferences in O(n), where $n$ is the number of observations. This
implementation makes GPs suitable for Big Data. For this reason, it is
important to provide a SS representation of the most important kernels used in
machine learning. The aim of this paper is to show how to exploit the transient
behaviour of SS models to map non-stationary kernels to SS models.
Resiliency of Deep Neural Networks under Quantization
Authors: Wonyong Sung, Sungho Shin, Kyuyeon Hwang
Date: 2015-11-20 00:00:00
Tasks: Quantization

The complexity of deep neural network algorithms for hardware implementation
can be much lowered by optimizing the word-length of weights and signals.
Direct quantization of floating-point weights, however, does not show good
performance when the number of bits assigned is small. Retraining of quantized
networks has been developed to relieve this problem. In this work, the effects
of retraining are analyzed for a feedforward deep neural network (FFDNN) and a
convolutional neural network (CNN). The network complexity is controlled to
know their effects on the resiliency of quantized networks by retraining. The
complexity of the FFDNN is controlled by varying the unit size in each hidden
layer and the number of layers, while that of the CNN is done by modifying the
feature map configuration. We find that the performance gap between the
floating-point and the retrain-based ternary (+1, 0, -1) weight neural networks
exists with a fair amount in 'complexity limited' networks, but the discrepancy
almost vanishes in fully complex networks whose capability is limited by the
training data, rather than by the number of connections. This research shows
that highly complex DNNs have the capability of absorbing the effects of severe
weight quantization through retraining, but connection limited networks are
less resilient. This paper also presents the effective compression ratio to
guide the trade-off between the network size and the precision when the
hardware resource is limited.
Ensemble Methods of Classification for Power Systems Security Assessment
Authors: Alexei Zhukov, Victor Kurbatsky, Nikita Tomin, Denis Sidorov, Daniil Panasetsky, Aoife Foley
Date: 2016-01-07 00:00:00
Tasks: Classification, Decision Making, General Classification

One of the most promising approaches for complex technical systems analysis
employs ensemble methods of classification. Ensemble methods enable to build a
reliable decision rules for feature space classification in the presence of
many possible states of the system. In this paper, novel techniques based on
decision trees are used for evaluation of the reliability of the regime of
electric power systems. We proposed hybrid approach based on random forests
models and boosting models. Such techniques can be applied to predict the
interaction of increasing renewable power, storage devices and swiching of
smart loads from intelligent domestic appliances, heaters and air-conditioning
units and electric vehicles with grid for enhanced decision making. The
ensemble classification methods were tested on the modified 118-bus IEEE power
system showing that proposed technique can be employed to examine whether the
power system is secured under steady-state operating conditions.
Complexity of Shift Bribery in Committee Elections
Authors: Robert Bredereck, Piotr Faliszewski, Rolf Niedermeier, Nimrod Talmon
Date: 2016-01-07 00:00:00

Given an election, a preferred candidate p, and a budget, the SHIFT BRIBERY
problem asks whether p can win the election after shifting p higher in some
voters' preference orders. Of course, shifting comes at a price (depending on
the voter and on the extent of the shift) and one must not exceed the given
budget. We study the (parameterized) computational complexity of S HIFT BRIBERY
for multiwinner voting rules where winning the election means to be part of
some winning committee. We focus on the well-established SNTV, Bloc, k-Borda,
and Chamberlin-Courant rules, as well as on approximate variants of the
Chamberlin-Courant rule, since the original rule is NP-hard to compute. We show
that SHIFT BRIBERY tends to be harder in the multiwinner setting than in the
single-winner one by showing settings where SHIFT BRIBERY is easy in the
single-winner cases, but is hard (and hard to approximate) in the multiwinner
ones. Moreover, we show that the non-monotonicity of those rules which are
based on approximation algorithms for the Chamberlin-Courant rule sometimes
affects the complexity of SHIFT BRIBERY.
Measuring and Discovering Correlations in Large Data Sets
Authors: Lijue Liu, Ming Li, Sha Wen
Date: 2016-01-07 00:00:00

In this paper, a class of statistics named ART (the alternant recursive
topology statistics) is proposed to measure the properties of correlation
between two variables. A wide range of bi-variable correlations both linear and
nonlinear can be evaluated by ART efficiently and equitably even if nothing is
known about the specific types of those relationships. ART compensates the
disadvantages of Reshef's model in which no polynomial time precise algorithm
exists and the "local random" phenomenon can not be identified. As a class of
nonparametric exploration statistics, ART is applied for analyzing a dataset of
10 American classical indexes, as a result, lots of bi-variable correlations
are discovered.
Basic Level Categorization Facilitates Visual Object Recognition
Authors: Panqu Wang, Garrison W. Cottrell
Date: 2015-11-12 00:00:00
Tasks: Object, Object Categorization, Object Recognition, Transfer Learning

Recent advances in deep learning have led to significant progress in the
computer vision field, especially for visual object recognition tasks. The
features useful for object classification are learned by feed-forward deep
convolutional neural networks (CNNs) automatically, and they are shown to be
able to predict and decode neural representations in the ventral visual pathway
of humans and monkeys. However, despite the huge amount of work on optimizing
CNNs, there has not been much research focused on linking CNNs with guiding
principles from the human visual cortex. In this work, we propose a network
optimization strategy inspired by both of the developmental trajectory of
children's visual object recognition capabilities, and Bar (2003), who
hypothesized that basic level information is carried in the fast magnocellular
pathway through the prefrontal cortex (PFC) and then projected back to inferior
temporal cortex (IT), where subordinate level categorization is achieved. We
instantiate this idea by training a deep CNN to perform basic level object
categorization first, and then train it on subordinate level categorization. We
apply this idea to training AlexNet (Krizhevsky et al., 2012) on the ILSVRC
2012 dataset and show that the top-5 accuracy increases from 80.13% to 82.14%,
demonstrating the effectiveness of the method. We also show that subsequent
transfer learning on smaller datasets gives superior results.
A Deep Memory-based Architecture for Sequence-to-Sequence Learning
Authors: Fandong Meng, Zhengdong Lu, Zhaopeng Tu, Hang Li, Qun Liu
Date: 2015-06-22 00:00:00
Tasks: Machine Translation, Sentence, Translation

We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence
learning, which performs the task through a series of nonlinear transformations
from the representation of the input sequence (e.g., a Chinese sentence) to the
final output sequence (e.g., translation to English). Inspired by the recently
proposed Neural Turing Machine (Graves et al., 2014), we store the intermediate
representations in stacked layers of memories, and use read-write operations on
the memories to realize the nonlinear transformations between the
representations. The types of transformations are designed in advance but the
parameters are learned from data. Through layer-by-layer transformations,
DEEPMEMORY can model complicated relations between sequences necessary for
applications such as machine translation between distant languages. The
architecture can be trained with normal back-propagation on sequenceto-sequence
data, and the learning can be easily scaled up to a large corpus. DEEPMEMORY is
broad enough to subsume the state-of-the-art neural translation model in
(Bahdanau et al., 2015) as its special case, while significantly improving upon
the model with its deeper architecture. Remarkably, DEEPMEMORY, being purely
neural network-based, can achieve performance comparable to the traditional
phrase-based machine translation system Moses with a small vocabulary and a
modest parameter size.
Mixture of Bilateral-Projection Two-dimensional Probabilistic Principal Component Analysis
Authors: Fujiao Ju, Yanfeng Sun, Junbin Gao, Simeng Liu, Yongli Hu
Date: 2016-01-07 00:00:00
Proceeding: CVPR 2016 6
Tasks: Bayesian Inference, Vocal Bursts Valence Prediction

The probabilistic principal component analysis (PPCA) is built upon a global
linear mapping, with which it is insufficient to model complex data variation.
This paper proposes a mixture of bilateral-projection probabilistic principal
component analysis model (mixB2DPPCA) on 2D data. With multi-components in the
mixture, this model can be seen as a soft cluster algorithm and has capability
of modeling data with complex structures. A Bayesian inference scheme has been
proposed based on the variational EM (Expectation-Maximization) approach for
learning model parameters. Experiments on some publicly available databases
show that the performance of mixB2DPPCA has been largely improved, resulting in
more accurate reconstruction errors and recognition rates than the existing
PCA-based algorithms.
Stochastic Dykstra Algorithms for Metric Learning on Positive Semi-Definite Cone
Authors: Tomoki Matsuzawa, Raissa Relator, Jun Sese, Tsuyoshi Kato
Date: 2016-01-07 00:00:00
Tasks: Metric Learning

Recently, covariance descriptors have received much attention as powerful
representations of set of points. In this research, we present a new metric
learning algorithm for covariance descriptors based on the Dykstra algorithm,
in which the current solution is projected onto a half-space at each iteration,
and runs at O(n^3) time. We empirically demonstrate that randomizing the order
of half-spaces in our Dykstra-based algorithm significantly accelerates the
convergence to the optimal solution. Furthermore, we show that our approach
yields promising experimental results on pattern recognition tasks.
Learning Kernels for Structured Prediction using Polynomial Kernel Transformations
Authors: Chetan Tonde, Ahmed Elgammal
Date: 2016-01-07 00:00:00
Tasks: Prediction, Structured Prediction

Learning the kernel functions used in kernel methods has been a vastly
explored area in machine learning. It is now widely accepted that to obtain
'good' performance, learning a kernel function is the key challenge. In this
work we focus on learning kernel representations for structured regression. We
propose use of polynomials expansion of kernels, referred to as Schoenberg
transforms and Gegenbaur transforms, which arise from the seminal result of
Schoenberg (1938). These kernels can be thought of as polynomial combination of
input features in a high dimensional reproducing kernel Hilbert space (RKHS).
We learn kernels over input and output for structured data, such that,
dependency between kernel features is maximized. We use Hilbert-Schmidt
Independence Criterion (HSIC) to measure this. We also give an efficient,
matrix decomposition-based algorithm to learn these kernel transformations, and
demonstrate state-of-the-art results on several real-world datasets.
Angrier Birds: Bayesian reinforcement learning
Authors: Imanol Arrieta Ibarra, Bernardo Ramos, Lars Roemheld
Date: 2016-01-06 00:00:00
Tasks: Efficient Exploration, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We train a reinforcement learner to play a simplified version of the game
Angry Birds. The learner is provided with a game state in a manner similar to
the output that could be produced by computer vision algorithms. We improve on
the efficiency of regular {\epsilon}-greedy Q-Learning with linear function
approximation through more systematic exploration in Randomized Least Squares
Value Iteration (RLSVI), an algorithm that samples its policy from a posterior
distribution on optimal policies. With larger state-action spaces, efficient
exploration becomes increasingly important, as evidenced by the faster learning
in RLSVI.
Quality Adaptive Low-Rank Based JPEG Decoding with Applications
Authors: Xiao Shu, Xiaolin Wu
Date: 2016-01-06 00:00:00
Tasks: Deblurring, Image Restoration, Quantization

Small compression noises, despite being transparent to human eyes, can
adversely affect the results of many image restoration processes, if left
unaccounted for. Especially, compression noises are highly detrimental to
inverse operators of high-boosting (sharpening) nature, such as deblurring and
superresolution against a convolution kernel. By incorporating the non-linear
DCT quantization mechanism into the formulation for image restoration, we
propose a new sparsity-based convex programming approach for joint compression
noise removal and image restoration. Experimental results demonstrate
significant performance gains of the new approach over existing image
restoration methods.
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity
Authors: Jason D. Lee, Qihang Lin, Tengyu Ma, Tianbao Yang
Date: 2015-07-27 00:00:00
Tasks: Distributed Optimization

We study distributed optimization algorithms for minimizing the average of
convex functions. The applications include empirical risk minimization problems
in statistical machine learning where the datasets are large and have to be
stored on different machines. We design a distributed stochastic variance
reduced gradient algorithm that, under certain conditions on the condition
number, simultaneously achieves the optimal parallel runtime, amount of
communication and rounds of communication among all distributed first-order
methods up to constant factors. Our method and its accelerated extension also
outperform existing distributed algorithms in terms of the rounds of
communication as long as the condition number is not too large compared to the
size of data in each machine. We also prove a lower bound for the number of
rounds of communication for a broad class of distributed first-order methods
including the proposed algorithms in this paper. We show that our accelerated
distributed stochastic variance reduced gradient algorithm achieves this lower
bound so that it uses the fewest rounds of communication among all distributed
first-order algorithms.
The new hybrid COAW method for solving multi-objective problems
Authors: Zeinab Borhanifar, Elham Shadkam
Date: 2016-01-06 00:00:00

In this article using Cuckoo Optimization Algorithm and simple additive
weighting method the hybrid COAW algorithm is presented to solve
multi-objective problems. Cuckoo algorithm is an efficient and structured
method for solving nonlinear continuous problems. The created Pareto frontiers
of the COAW proposed algorithm are exact and have good dispersion. This method
has a high speed in finding the Pareto frontiers and identifies the beginning
and end points of Pareto frontiers properly. In order to validation the
proposed algorithm, several experimental problems were analyzed. The results of
which indicate the proper effectiveness of COAW algorithm for solving
multi-objective problems.
Shape Animation with Combined Captured and Simulated Dynamics
Authors: Benjamin Allain, Li Wang, Jean-Sebastien Franco, Franck Hetroy, Edmond Boyer
Date: 2016-01-06 00:00:00

We present a novel volumetric animation generation framework to create new
types of animations from raw 3D surface or point cloud sequence of captured
real performances. The framework considers as input time incoherent 3D
observations of a moving shape, and is thus particularly suitable for the
output of performance capture platforms. In our system, a suitable virtual
representation of the actor is built from real captures that allows seamless
combination and simulation with virtual external forces and objects, in which
the original captured actor can be reshaped, disassembled or reassembled from
user-specified virtual physics. Instead of using the dominant surface-based
geometric representation of the capture, which is less suitable for volumetric
effects, our pipeline exploits Centroidal Voronoi tessellation decompositions
as unified volumetric representation of the real captured actor, which we show
can be used seamlessly as a building block for all processing stages, from
capture and tracking to virtual physic simulation. The representation makes no
human specific assumption and can be used to capture and re-simulate the actor
with props or other moving scenery elements. We demonstrate the potential of
this pipeline for virtual reanimation of a real captured event with various
unprecedented volumetric visual effects, such as volumetric distortion,
erosion, morphing, gravity pull, or collisions.
Some Experimental Issues in Financial Fraud Detection: An Investigation
Authors: J. West, Maumita Bhattacharya
Date: 2016-01-06 00:00:00
Tasks: Binary Classification, Fraud Detection

Financial fraud detection is an important problem with a number of design
aspects to consider. Issues such as algorithm selection and performance
analysis will affect the perceived ability of proposed solutions, so for
auditors and re-searchers to be able to sufficiently detect financial fraud it
is necessary that these issues be thoroughly explored. In this paper we will
revisit the key performance metrics used for financial fraud detection with a
focus on credit card fraud, critiquing the prevailing ideas and offering our
own understandings. There are many different performance metrics that have been
employed in prior financial fraud detection research. We will analyse several
of the popular metrics and compare their effectiveness at measuring the ability
of detection mechanisms. We further investigated the performance of a range of
computational intelligence techniques when applied to this problem domain, and
explored the efficacy of several binary classification methods.
Adaptive and Efficient Nonlinear Channel Equalization for Underwater Acoustic Communication
Authors: Dariush Kari, Nuri Denizcan Vanli, Suleyman Serdar Kozat
Date: 2016-01-06 00:00:00

We investigate underwater acoustic (UWA) channel equalization and introduce
hierarchical and adaptive nonlinear channel equalization algorithms that are
highly efficient and provide significantly improved bit error rate (BER)
performance. Due to the high complexity of nonlinear equalizers and poor
performance of linear ones, to equalize highly difficult underwater acoustic
channels, we employ piecewise linear equalizers. However, in order to achieve
the performance of the best piecewise linear model, we use a tree structure to
hierarchically partition the space of the received signal. Furthermore, the
equalization algorithm should be completely adaptive, since due to the highly
non-stationary nature of the underwater medium, the optimal MSE equalizer as
well as the best piecewise linear equalizer changes in time. To this end, we
introduce an adaptive piecewise linear equalization algorithm that not only
adapts the linear equalizer at each region but also learns the complete
hierarchical structure with a computational complexity only polynomial in the
number of nodes of the tree. Furthermore, our algorithm is constructed to
directly minimize the final squared error without introducing any ad-hoc
parameters. We demonstrate the performance of our algorithms through highly
realistic experiments performed on accurately simulated underwater acoustic
channels.
Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis
Authors: Ramin Norousi, Volker J. Schmid
Date: 2016-01-06 00:00:00
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Semantic Segmentation

Since manual object detection is very inaccurate and time consuming, some
automatic object detection tools have been developed in recent years. At the
moment, there is no image analysis software available which provides an
automatic, objective assessment of 3D foci which is generally applicable.
Complications arise from discrete foci which are very close or even come in
contact to other foci, moreover they are of variable sizes and show variable
signal-to-noise, and must be analyzed fully in 3D. Therefore we introduce the
3D-OSCOS (3D-Object Segmentation and Colocalization Analysis based on Spatial
statistics) algorithm which is implemented as a user-friendly toolbox for
interactive detection of 3D objects and visualization of labeled images.
A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints
Authors: Nicolas Boumal
Date: 2015-06-01 00:00:00

We propose a new algorithm to solve optimization problems of the form $\min
f(X)$ for a smooth function $f$ under the constraints that $X$ is positive
semidefinite and the diagonal blocks of $X$ are small identity matrices. Such
problems often arise as the result of relaxing a rank constraint (lifting). In
particular, many estimation tasks involving phases, rotations, orthonormal
bases or permutations fit in this framework, and so do certain relaxations of
combinatorial problems such as Max-Cut. The proposed algorithm exploits the
facts that (1) such formulations admit low-rank solutions, and (2) their
rank-restricted versions are smooth optimization problems on a Riemannian
manifold. Combining insights from both the Riemannian and the convex geometries
of the problem, we characterize when second-order critical points of the smooth
problem reveal KKT points of the semidefinite problem. We compare against state
of the art, mature software and find that, on certain interesting problem
instances, what we call the staircase method is orders of magnitude faster, is
more accurate and scales better. Code is available.
Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015
Authors: Kamal Sarkar
Date: 2016-01-06 00:00:00
Tasks: Part-Of-Speech Tagging, POS, POS Tagging

This paper discusses the experiments carried out by us at Jadavpur University
as part of the participation in ICON 2015 task: POS Tagging for Code-mixed
Indian Social Media Text. The tool that we have developed for the task is based
on Trigram Hidden Markov Model that utilizes information from dictionary as
well as some other word level features to enhance the observation probabilities
of the known tokens as well as unknown tokens. We submitted runs for
Bengali-English, Hindi-English and Tamil-English Language pairs. Our system has
been trained and tested on the datasets released for ICON 2015 shared task: POS
Tagging For Code-mixed Indian Social Media Text. In constrained mode, our
system obtains average overall accuracy (averaged over all three language
pairs) of 75.60% which is very close to other participating two systems (76.79%
for IIITH and 75.79% for AMRITA_CEN) ranked higher than our system. In
unconstrained mode, our system obtains average overall accuracy of 70.65% which
is also close to the system (72.85% for AMRITA_CEN) which obtains the highest
average overall accuracy.