pwc-archive/papers-with-abstracts huggingface dataset:
When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming
Authors: Indranil Pan, Saptarshi Das
Date: 2014-09-27 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper, we propose a novel methodology for automatically finding new
chaotic attractors through a computational intelligence technique known as
multi-gene genetic programming (MGGP). We apply this technique to the case of
the Lorenz attractor and evolve several new chaotic attractors based on the
basic Lorenz template. The MGGP algorithm automatically finds new nonlinear
expressions for the different state variables starting from the original Lorenz
system. The Lyapunov exponents of each of the attractors are calculated
numerically based on the time series of the state variables using time delay
embedding techniques. The MGGP algorithm tries to search the functional space
of the attractors by aiming to maximise the largest Lyapunov exponent (LLE) of
the evolved attractors. To demonstrate the potential of the proposed
methodology, we report over one hundred new chaotic attractor structures along
with their parameters, which are evolved from just the Lorenz system alone.
Some Theoretical Properties of a Network of Discretely Firing Neurons
Authors: Stephen Luttrell
Date: 2015-05-03 00:00:00

The problem of optimising a network of discretely firing neurons is
addressed. An objective function is introduced which measures the average
number of bits that are needed for the network to encode its state. When this
is minimised, it is shown that this leads to a number of results, such as
topographic mappings, piecewise linear dependence on the input of the
probability of a neuron firing, and factorial encoder networks.
Object Class Detection and Classification using Multi Scale Gradient and Corner Point based Shape Descriptors
Authors: Basura Fernando, Sezer Karaoglu, Sajib Kumar Saha
Date: 2015-05-03 00:00:00
Tasks: Classification, General Classification, Object, object-detection, Object Detection, Real-Time Object Detection, Translation

This paper presents a novel multi scale gradient and a corner point based
shape descriptors. The novel multi scale gradient based shape descriptor is
combined with generic Fourier descriptors to extract contour and region based
shape information. Shape information based object class detection and
classification technique with a random forest classifier has been optimized.
Proposed integrated descriptor in this paper is robust to rotation, scale,
translation, affine deformations, noisy contours and noisy shapes. The new
corner point based interpolated shape descriptor has been exploited for fast
object detection and classification with higher accuracy.
Electron Neutrino Classification in Liquid Argon Time Projection Chamber Detector
Authors: Piotr Płoński, Dorota Stefan, Robert Sulej, Krzysztof Zaremba
Date: 2015-05-03 00:00:00
Tasks: Classification, General Classification, Position

Neutrinos are one of the least known elementary particles. The detection of
neutrinos is an extremely difficult task since they are affected only by weak
sub-atomic force or gravity. Therefore large detectors are constructed to
reveal neutrino's properties. Among them the Liquid Argon Time Projection
Chamber (LAr-TPC) detectors provide excellent imaging and particle
identification ability for studying neutrinos. The computerized methods for
automatic reconstruction and identification of particles are needed to fully
exploit the potential of the LAr-TPC technique. Herein, the novel method for
electron neutrino classification is presented. The method constructs a feature
descriptor from images of observed event. It characterizes the signal
distribution propagated from vertex of interest, where the particle interacts
with the detector medium. The classifier is learned with a constructed feature
descriptor to decide whether the images represent the electron neutrino or
cascade produced by photons. The proposed approach assumes that the position of
primary interaction vertex is known. The method's performance in dependency to
the noise in a primary vertex position and deposited energy of particles is
studied.
Optimal Time-Series Motifs
Authors: Josif Grabocka, Nicolas Schilling, Lars Schmidt-Thieme
Date: 2015-05-03 00:00:00
Tasks: Time Series, Time Series Analysis

Motifs are the most repetitive/frequent patterns of a time-series. The
discovery of motifs is crucial for practitioners in order to understand and
interpret the phenomena occurring in sequential data. Currently, motifs are
searched among series sub-sequences, aiming at selecting the most frequently
occurring ones. Search-based methods, which try out series sub-sequence as
motif candidates, are currently believed to be the best methods in finding the
most frequent patterns.
  However, this paper proposes an entirely new perspective in finding motifs.
We demonstrate that searching is non-optimal since the domain of motifs is
restricted, and instead we propose a principled optimization approach able to
find optimal motifs. We treat the occurrence frequency as a function and
time-series motifs as its parameters, therefore we \textit{learn} the optimal
motifs that maximize the frequency function. In contrast to searching, our
method is able to discover the most repetitive patterns (hence optimal), even
in cases where they do not explicitly occur as sub-sequences. Experiments on
several real-life time-series datasets show that the motifs found by our method
are highly more frequent than the ones found through searching, for exactly the
same distance threshold.
Permutohedral Lattice CNNs
Authors: Martin Kiefel, Varun Jampani, Peter V. Gehler
Date: 2014-12-20 00:00:00
Tasks: Position

This paper presents a convolutional layer that is able to process sparse
input features. As an example, for image recognition problems this allows an
efficient filtering of signals that do not lie on a dense grid (like pixel
position), but of more general features (such as color values). The presented
algorithm makes use of the permutohedral lattice data structure. The
permutohedral lattice was introduced to efficiently implement a bilateral
filter, a commonly used image processing operation. Its use allows for a
generalization of the convolution type found in current (spatial) convolutional
network architectures.
On a fast bilateral filtering formulation using functional rearrangements
Authors: Gonzalo Galiano, Julián Velasco
Date: 2015-05-03 00:00:00

We introduce an exact reformulation of a broad class of neighborhood filters,
among which the bilateral filters, in terms of two functional rearrangements:
the decreasing and the relative rearrangements.
  Independently of the image spatial dimension (one-dimensional signal, image,
volume of images, etc.), we reformulate these filters as integral operators
defined in a one-dimensional space corresponding to the level sets measures.
  We prove the equivalence between the usual pixel-based version and the
rearranged version of the filter. When restricted to the discrete setting, our
reformulation of bilateral filters extends previous results for the so-called
fast bilateral filtering. We, in addition, prove that the solution of the
discrete setting, understood as constant-wise interpolators, converges to the
solution of the continuous setting.
  Finally, we numerically illustrate computational aspects concerning quality
approximation and execution time provided by the rearranged formulation.
Sample Size Planning for Classification Models
Authors: Claudia Beleites, Ute Neugebauer, Thomas Bocklitz, Christoph Krafft, Jürgen Popp
Date: 2012-11-06 00:00:00
Tasks: Classification, General Classification

In biospectroscopy, suitably annotated and statistically independent samples
(e. g. patients, batches, etc.) for classifier training and testing are scarce
and costly. Learning curves show the model performance as function of the
training sample size and can help to determine the sample size needed to train
good classifiers. However, building a good model is actually not enough: the
performance must also be proven. We discuss learning curves for typical small
sample size situations with 5 - 25 independent samples per class. Although the
classification models achieve acceptable performance, the learning curve can be
completely masked by the random testing uncertainty due to the equally limited
test sample size. In consequence, we determine test sample sizes necessary to
achieve reasonable precision in the validation and find that 75 - 100 samples
will usually be needed to test a good but not perfect classifier. Such a data
set will then allow refined sample size planning on the basis of the achieved
performance. We also demonstrate how to calculate necessary sample sizes in
order to show the superiority of one classifier over another: this often
requires hundreds of statistically independent test samples or is even
theoretically impossible. We demonstrate our findings with a data set of ca.
2550 Raman spectra of single cells (five classes: erythrocytes, leukocytes and
three tumour cell lines BT-20, MCF-7 and OCI-AML3) as well as by an extensive
simulation that allows precise determination of the actual performance of the
models in question.
LABR: A Large Scale Arabic Sentiment Analysis Benchmark
Authors: Mahmoud Nabil, Mohamed Aly, Amir Atiya
Date: 2014-11-25 00:00:00
Tasks: Arabic Sentiment Analysis, Classification, General Classification, Sentiment Analysis

We introduce LABR, the largest sentiment analysis dataset to-date for the
Arabic language. It consists of over 63,000 book reviews, each rated on a scale
of 1 to 5 stars. We investigate the properties of the dataset, and present its
statistics. We explore using the dataset for two tasks: (1) sentiment polarity
classification; and (2) ratings classification. Moreover, we provide standard
splits of the dataset into training, validation and testing, for both polarity
and ratings classification, in both balanced and unbalanced settings. We extend
our previous work by performing a comprehensive analysis on the dataset. In
particular, we perform an extended survey of the different classifiers
typically used for the sentiment polarity classification problem. We also
construct a sentiment lexicon from the dataset that contains both single and
compound sentiment words and we explore its effectiveness. We make the dataset
and experimental details publicly available.
Visualization of Tradeoff in Evaluation: from Precision-Recall & PN to LIFT, ROC & BIRD
Authors: David M. W. Powers
Date: 2015-05-03 00:00:00
Tasks: Sensitivity, Specificity

Evaluation often aims to reduce the correctness or error characteristics of a system down to a single number, but that always involves trade-offs. Another way of dealing with this is to quote two numbers, such as Recall and Precision, or Sensitivity and Specificity. But it can also be useful to see more than this, and a graphical approach can explore sensitivity to cost, prevalence, bias, noise, parameters and hyper-parameters. Moreover, most techniques are implicitly based on two balanced classes, and our ability to visualize graphically is intrinsically two dimensional, but we often want to visualize in a multiclass context. We review the dichotomous approaches relating to Precision, Recall, and ROC as well as the related LIFT chart, exploring how they handle unbalanced and multiclass data, and deriving new probabilistic and information theoretic variants of LIFT that help deal with the issues associated with the handling of multiple and unbalanced classes.
Metareasoning for Planning Under Uncertainty
Authors: Christopher H. Lin, Andrey Kolobov, Ece Kamar, Eric Horvitz
Date: 2015-05-03 00:00:00

The conventional model for online planning under uncertainty assumes that an
agent can stop and plan without incurring costs for the time spent planning.
However, planning time is not free in most real-world settings. For example, an
autonomous drone is subject to nature's forces, like gravity, even while it
thinks, and must either pay a price for counteracting these forces to stay in
place, or grapple with the state change caused by acquiescing to them. Policy
optimization in these settings requires metareasoning---a process that trades
off the cost of planning and the potential policy improvement that can be
achieved. We formalize and analyze the metareasoning problem for Markov
Decision Processes (MDPs). Our work subsumes previously studied special cases
of metareasoning and shows that in the general case, metareasoning is at most
polynomially harder than solving MDPs with any given algorithm that disregards
the cost of thinking. For reasons we discuss, optimal general metareasoning
turns out to be impractical, motivating approximations. We present approximate
metareasoning procedures which rely on special properties of the BRTDP planning
algorithm and explore the effectiveness of our methods on a variety of
problems.
Block Basis Factorization for Scalable Kernel Matrix Evaluation
Authors: Ruoxi Wang, Yingzhou Li, Michael W. Mahoney, Eric Darve
Date: 2015-05-03 00:00:00
Tasks: BIG-bench Machine Learning

Kernel methods are widespread in machine learning; however, they are limited by the quadratic complexity of the construction, application, and storage of kernel matrices. Low-rank matrix approximation algorithms are widely used to address this problem and reduce the arithmetic and storage cost. However, we observed that for some datasets with wide intra-class variability, the optimal kernel parameter for smaller classes yields a matrix that is less well approximated by low-rank methods. In this paper, we propose an efficient structured low-rank approximation method -- the Block Basis Factorization (BBF) -- and its fast construction algorithm to approximate radial basis function (RBF) kernel matrices. Our approach has linear memory cost and floating-point operations for many machine learning kernels. BBF works for a wide range of kernel bandwidth parameters and extends the domain of applicability of low-rank approximation methods significantly. Our empirical results demonstrate the stability and superiority over the state-of-art kernel approximation algorithms.
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Authors: Zhaoxin Li, Kuanquan Wang, WangMeng Zuo, Deyu Meng, Lei Zhang
Date: 2015-05-03 00:00:00
Tasks: Denoising, Image Registration

Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
$\ell_{p}$-minimization algorithm by adaptively estimating the $p$ value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.
Order-Revealing Encryption and the Hardness of Private Learning
Authors: Mark Bun, Mark Zhandry
Date: 2015-05-03 00:00:00
Tasks: PAC learning, valid

An order-revealing encryption scheme gives a public procedure by which two
ciphertexts can be compared to reveal the ordering of their underlying
plaintexts. We show how to use order-revealing encryption to separate
computationally efficient PAC learning from efficient $(\epsilon,
\delta)$-differentially private PAC learning. That is, we construct a concept
class that is efficiently PAC learnable, but for which every efficient learner
fails to be differentially private. This answers a question of Kasiviswanathan
et al. (FOCS '08, SIAM J. Comput. '11).
  To prove our result, we give a generic transformation from an order-revealing
encryption scheme into one with strongly correct comparison, which enables the
consistent comparison of ciphertexts that are not obtained as the valid
encryption of any message. We believe this construction may be of independent
interest.
Modeling Compositionality with Multiplicative Recurrent Neural Networks
Authors: Ozan İrsoy, Claire Cardie
Date: 2014-12-20 00:00:00
Tasks: Sentiment Analysis

We present the multiplicative recurrent neural network as a general model for
compositional meaning in language, and evaluate it on the task of fine-grained
sentiment analysis. We establish a connection to the previously investigated
matrix-space models for compositionality, and show they are special cases of
the multiplicative recurrent net. Our experiments show that these models
perform comparably or better than Elman-type additive recurrent neural networks
and outperform matrix-space models on a standard fine-grained sentiment
analysis corpus. Furthermore, they yield comparable results to structural deep
models on the recently published Stanford Sentiment Treebank without the need
for generating parse trees.
Learning Temporal Embeddings for Complex Video Analysis
Authors: Vignesh Ramanathan, Kevin Tang, Greg Mori, Li Fei-Fei
Date: 2015-05-02 00:00:00
Proceeding: ICCV 2015 12
Tasks: Data Augmentation, Retrieval

In this paper, we propose to learn temporal embeddings of video frames for
complex video analysis. Large quantities of unlabeled video data can be easily
obtained from the Internet. These videos possess the implicit weak label that
they are sequences of temporally and semantically coherent images. We leverage
this information to learn temporal embeddings for video frames by associating
frames with the temporal context that they appear in. To do this, we propose a
scheme for incorporating temporal context based on past and future frames in
videos, and compare this to other contextual representations. In addition, we
show how data augmentation using multi-resolution samples and hard negatives
helps to significantly improve the quality of the learned embeddings. We
evaluate various design decisions for learning temporal embeddings, and show
that our embeddings can improve performance for multiple video tasks such as
retrieval, classification, and temporal order recovery in unconstrained
Internet video.
Deconstructing Principal Component Analysis Using a Data Reconciliation Perspective
Authors: Shankar Narasimhan, Nirav Bhatt
Date: 2015-05-02 00:00:00
Tasks: Denoising

Data reconciliation (DR) and Principal Component Analysis (PCA) are two
popular data analysis techniques in process industries. Data reconciliation is
used to obtain accurate and consistent estimates of variables and parameters
from erroneous measurements. PCA is primarily used as a method for reducing the
dimensionality of high dimensional data and as a preprocessing technique for
denoising measurements. These techniques have been developed and deployed
independently of each other. The primary purpose of this article is to
elucidate the close relationship between these two seemingly disparate
techniques. This leads to a unified framework for applying PCA and DR. Further,
we show how the two techniques can be deployed together in a collaborative and
consistent manner to process data. The framework has been extended to deal with
partially measured systems and to incorporate partial knowledge available about
the process model.
Advanced Mean Field Theory of Restricted Boltzmann Machine
Authors: Haiping Huang, Taro Toyoizumi
Date: 2015-02-01 00:00:00

Learning in restricted Boltzmann machine is typically hard due to the
computation of gradients of log-likelihood function. To describe the network
state statistics of the restricted Boltzmann machine, we develop an advanced
mean field theory based on the Bethe approximation. Our theory provides an
efficient message passing based method that evaluates not only the partition
function (free energy) but also its gradients without requiring statistical
sampling. The results are compared with those obtained by the computationally
expensive sampling based method.
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Authors: Tejaswi Nimmagadda, Anima Anandkumar
Date: 2015-05-02 00:00:00
Tasks: Classification, Clustering, General Classification, Object, Scene Classification, Scene Understanding

Deep learning has shown state-of-art classification performance on datasets
such as ImageNet, which contain a single object in each image. However,
multi-object classification is far more challenging. We present a unified
framework which leverages the strengths of multiple machine learning methods,
viz deep learning, probabilistic models and kernel methods to obtain
state-of-art performance on Microsoft COCO, consisting of non-iconic images. We
incorporate contextual information in natural images through a conditional
latent tree probabilistic model (CLTM), where the object co-occurrences are
conditioned on the extracted fc7 features from pre-trained Imagenet CNN as
input. We learn the CLTM tree structure using conditional pairwise
probabilities for object co-occurrences, estimated through kernel methods, and
we learn its node and edge potentials by training a new 3-layer neural network,
which takes fc7 features as input. Object classification is carried out via
inference on the learnt conditional tree model, and we obtain significant gain
in precision-recall and F-measures on MS-COCO, especially for difficult object
categories. Moreover, the latent variables in the CLTM capture scene
information: the images with top activations for a latent node have common
themes such as being a grasslands or a food scene, and on on. In addition, we
show that a simple k-means clustering of the inferred latent nodes alone
significantly improves scene classification performance on the MIT-Indoor
dataset, without the need for any retraining, and without using scene labels
during training. Thus, we present a unified framework for multi-object
classification and unsupervised scene understanding.
Object-Scene Convolutional Neural Networks for Event Recognition in Images
Authors: Limin Wang, Zhe Wang, Wenbin Du, Yu Qiao
Date: 2015-05-02 00:00:00

Event recognition from still images is of great importance for image
understanding. However, compared with event recognition in videos, there are
much fewer research works on event recognition in images. This paper addresses
the issue of event recognition from images and proposes an effective method
with deep neural networks. Specifically, we design a new architecture, called
Object-Scene Convolutional Neural Network (OS-CNN). This architecture is
decomposed into object net and scene net, which extract useful information for
event understanding from the perspective of objects and scene context,
respectively. Meanwhile, we investigate different network architectures for
OS-CNN design, and adapt the deep (AlexNet) and very-deep (GoogLeNet) networks
to the task of event recognition. Furthermore, we find that the deep and
very-deep networks are complementary to each other. Finally, based on the
proposed OS-CNN and comparative study of different network architectures, we
come up with a solution of five-stream CNN for the track of cultural event
recognition at the ChaLearn Looking at People (LAP) challenge 2015. Our method
obtains the performance of 85.5% and ranks the $1^{st}$ place in this
challenge.