pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Authors: Christopher Amato, Frans A. Oliehoek
Date: 2014-04-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems.
KF metamodel formalization
Authors: Pablo R. Fillottrani, C. Maria Keet
Date: 2014-12-19 00:00:00

The KF metamodel is a comprehensive unifying metamodel covering the static
structural entities and constraints of UML Class Diagrams (v2.4.1), ER, EER,
ORM, and ORM2, and intended to boost interoperability of common conceptual data
modelling languages. It was originally designed in UML with textual
constraints, and in this report we present its formalisations in FOL and OWL,
which accompanies the paper that describes, discusses, and analyses the KF
metamodel in detail. These new formalizations contribute to give a precise
meaning to the metamodel, to understand its complexity properties and to
provide a basis for future implementations.
Training for Fast Sequential Prediction Using Dynamic Feature Selection
Authors: Emma Strubell, Luke Vilnis, Andrew McCallum
Date: 2014-10-30 00:00:00
Tasks: feature selection, parameter estimation, Part-Of-Speech Tagging

We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. We present experiments in left-to-right
part-of-speech tagging on WSJ, demonstrating that we can preserve accuracy
above 97% with over a five-fold reduction in run-time.
Deep Speech: Scaling up end-to-end speech recognition
Authors: Awni Hannun, Carl Case, Jared Casper, Bryan Catanzaro, Greg Diamos, Erich Elsen, Ryan Prenger, Sanjeev Satheesh, Shubho Sengupta, Adam Coates, Andrew Y. Ng
Date: 2014-12-17 00:00:00
Tasks: Accented Speech Recognition, Speech Recognition

We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. In contrast, our system does not need hand-designed
components to model background noise, reverberation, or speaker variation, but
instead directly learns a function that is robust to such effects. We do not
need a phoneme dictionary, nor even the concept of a "phoneme." Key to our
approach is a well-optimized RNN training system that uses multiple GPUs, as
well as a set of novel data synthesis techniques that allow us to efficiently
obtain a large amount of varied data for training. Our system, called Deep
Speech, outperforms previously published results on the widely studied
Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech
also handles challenging noisy environments better than widely used,
state-of-the-art commercial speech systems.
A Simple and Efficient Method To Generate Word Sense Representations
Authors: Luis Nieto Piña, Richard Johansson
Date: 2014-12-18 00:00:00
Proceeding: RANLP 2015 9
Tasks: Language Modeling, Language Modelling

Distributed representations of words have boosted the performance of many
Natural Language Processing tasks. However, usually only one representation per
word is obtained, not acknowledging the fact that some words have multiple
meanings. This has a negative effect on the individual word representations and
the language model as a whole. In this paper we present a simple model that
enables recent techniques for building word vectors to represent distinct
senses of polysemic words. In our assessment of this model we show that it is
able to effectively discriminate between words' senses and to do so in a
computationally efficient manner.
Fast Algorithm for Low-rank matrix recovery in Poisson noise
Authors: Yang Cao, Yao Xie
Date: 2014-07-02 00:00:00

This paper describes a fast algorithm for recovering low-rank matrices from
their linear measurements contaminated with Poisson noise: the Poisson noise
Maximum Likelihood Singular Value thresholding (PMLSV) algorithm. We propose a
convex optimization formulation with a cost function consisting of the sum of a
likelihood function and a regularization function which the nuclear norm of the
matrix. Instead of solving the optimization problem directly by semi-definite
program (SDP), we derive an iterative singular value thresholding algorithm by
expanding the likelihood function. We demonstrate the good performance of the
proposed algorithm on recovery of solar flare images with Poisson noise: the
algorithm is more efficient than solving SDP using the interior-point algorithm
and it generates a good approximate solution compared to that solved from SDP.
Cauchy Principal Component Analysis
Authors: Pengtao Xie, Eric Xing
Date: 2014-12-19 00:00:00

Principal Component Analysis (PCA) has wide applications in machine learning,
text mining and computer vision. Classical PCA based on a Gaussian noise model
is fragile to noise of large magnitude. Laplace noise assumption based PCA
methods cannot deal with dense noise effectively. In this paper, we propose
Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective
PCA method which is robust to various types of noise. We utilize Cauchy
distribution to model noise and derive Cauchy PCA under the maximum likelihood
estimation (MLE) framework with low rank constraint. Our method can robustly
estimate the low rank matrix regardless of whether noise is large or small,
dense or sparse. We analyze the robustness of Cauchy PCA from a robust
statistics view and present an efficient singular value projection optimization
method. Experimental results on both simulated data and real applications
demonstrate the robustness of Cauchy PCA to various noise patterns.
A la Carte - Learning Fast Kernels
Authors: Zichao Yang, Alexander J. Smola, Le Song, Andrew Gordon Wilson
Date: 2014-12-19 00:00:00

Kernel methods have great promise for learning rich statistical
representations of large modern datasets. However, compared to neural networks,
kernel methods have been perceived as lacking in scalability and flexibility.
We introduce a family of fast, flexible, lightly parametrized and general
purpose kernel learning methods, derived from Fastfood basis function
expansions. We provide mechanisms to learn the properties of groups of spectral
frequencies in these expansions, which require only O(mlogd) time and O(m)
memory, for m basis functions and d input dimensions. We show that the proposed
methods can learn a wide class of kernels, outperforming the alternatives in
accuracy, speed, and memory consumption.
Simplified firefly algorithm for 2D image key-points search
Authors: Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana, Zbigniew Marszałek, Dawid Połap, Marcin Woźniak
Date: 2014-12-19 00:00:00
Tasks: Object

In order to identify an object, human eyes firstly search the field of view
for points or areas which have particular properties. These properties are used
to recognise an image or an object. Then this process could be taken as a model
to develop computer algorithms for images identification. This paper proposes
the idea of applying the simplified firefly algorithm to search for key-areas
in 2D images. For a set of input test images the proposed version of firefly
algorithm has been examined. Research results are presented and discussed to
show the efficiency of this evolutionary computation method.
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer
Authors: Mark Wernsdorfer, Ute Schmid
Date: 2014-12-19 00:00:00
Tasks: Deep Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Methods of deep machine learning enable to to reuse low-level representations
efficiently for generating more abstract high-level representations.
Originally, deep learning has been applied passively (e.g., for classification
purposes). Recently, it has been extended to estimate the value of actions for
autonomous agents within the framework of reinforcement learning (RL). Explicit
models of the environment can be learned to augment such a value function.
Although "flat" connectionist methods have already been used for model-based
RL, up to now, only model-free variants of RL have been equipped with methods
from deep learning. We propose a variant of deep model-based RL that enables an
agent to learn arbitrarily abstract hierarchical representations of its
environment. In this paper, we present research on how such hierarchical
representations can be grounded in sensorimotor interaction between an agent
and its environment.
Py3DFreeHandUS: a library for voxel-array reconstruction using Ultrasonography and attitude sensors
Authors: Davide Monari, Francesco Cenni, Erwin Aertbeliën, Kaat Desloovere
Date: 2014-12-19 00:00:00

In medical imaging, there is a growing interest to provide real-time images
with good quality for large anatomical structures. To cope with this issue, we
developed a library that allows to replace, for some specific clinical
applications, more robust systems such as Computer Tomography (CT) and Magnetic
Resonance Imaging (MRI). Our python library Py3DFreeHandUS is a package for
processing data acquired simultaneously by ultra-sonographic systems (US) and
marker-based optoelectronic systems. In particular, US data enables to
visualize subcutaneous body structures, whereas the optoelectronic system is
able to collect the 3D position in space for reflective objects, that are
called markers. By combining these two measurement devices, it is possible to
reconstruct the real 3D morphology of body structures such as muscles, for
relevant clinical implications. In the present research work, the different
steps which allow to obtain a relevant 3D data set as well as the procedures
for calibrating the systems and for determining the quality of the
reconstruction.
Distributed Decision Trees
Authors: Ozan İrsoy, Ethem Alpaydin
Date: 2014-12-19 00:00:00
Tasks: Representation Learning

Recently proposed budding tree is a decision tree algorithm in which every
node is part internal node and part leaf. This allows representing every
decision tree in a continuous parameter space, and therefore a budding tree can
be jointly trained with backpropagation, like a neural network. Even though
this continuity allows it to be used in hierarchical representation learning,
the learned representations are local: Activation makes a soft selection among
all root-to-leaf paths in a tree. In this work we extend the budding tree and
propose the distributed tree where the children use different and independent
splits and hence multiple paths in a tree can be traversed at the same time.
This ability to combine multiple paths gives the power of a distributed
representation, as in a traditional perceptron layer. We show that distributed
trees perform comparably or better than budding and traditional hard trees on
classification and regression tasks.
Reverse Engineering Chemical Reaction Networks from Time Series Data
Authors: Dominic P. Searson, Mark J. Willis, Allen Wright
Date: 2014-12-19 00:00:00
Tasks: Time Series, Time Series Analysis

The automated inference of physically interpretable (bio)chemical reaction
network models from measured experimental data is a challenging problem whose
solution has significant commercial and academic ramifications. It is
demonstrated, using simulations, how sets of elementary reactions comprising
chemical reaction networks, as well as their rate coefficients, may be
accurately recovered from non-equilibrium time series concentration data, such
as that obtained from laboratory scale reactors. A variant of an evolutionary
algorithm called differential evolution in conjunction with least squares
techniques is used to search the space of reaction networks in order to infer
both the reaction network topology and its rate parameters. Properties of the
stoichiometric matrices of trial networks are used to bias the search towards
physically realisable solutions. No other information, such as chemical
characterisation of the reactive species is required, although where available
it may be used to improve the search process.
From dependency to causality: a machine learning approach
Authors: Gianluca Bontempi, Maxime Flauder
Date: 2014-12-19 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

The relationship between statistical dependency and causality lies at the
heart of all statistical approaches to causal inference. Recent results in the
ChaLearn cause-effect pair challenge have shown that causal directionality can
be inferred with good accuracy also in Markov indistinguishable configurations
thanks to data driven approaches. This paper proposes a supervised machine
learning approach to infer the existence of a directed causal link between two
variables in multivariate settings with $n>2$ variables. The approach relies on
the asymmetry of some conditional (in)dependence relations between the members
of the Markov blankets of two variables causally connected. Our results show
that supervised learning methods may be successfully used to extract causal
information on the basis of asymmetric statistical descriptors also for $n>2$
variate distributions.
Supertagging: Introduction, learning, and application
Authors: Taraka Rama K
Date: 2014-12-19 00:00:00
Tasks: Semantic Parsing, TAG

Supertagging is an approach originally developed by Bangalore and Joshi
(1999) to improve the parsing efficiency. In the beginning, the scholars used
small training datasets and somewhat na\"ive smoothing techniques to learn the
probability distributions of supertags. Since its inception, the applicability
of Supertags has been explored for TAG (tree-adjoining grammar) formalism as
well as other related yet, different formalisms such as CCG. This article will
try to summarize the various chapters, relevant to statistical parsing, from
the most recent edited book volume (Bangalore and Joshi, 2010). The chapters
were selected so as to blend the learning of supertags, its integration into
full-scale parsing, and in semantic parsing.
Gradual training of deep denoising auto encoders
Authors: Alexander Kalmanovich, Gal Chechik
Date: 2014-12-19 00:00:00
Tasks: Denoising, General Classification

Stacked denoising auto encoders (DAEs) are well known to learn useful deep
representations, which can be used to improve supervised training by
initializing a deep network. We investigate a training scheme of a deep DAE,
where DAE layers are gradually added and keep adapting as additional layers are
added. We show that in the regime of mid-sized datasets, this gradual training
provides a small but consistent improvement over stacked training in both
reconstruction quality and classification error over stacked training on MNIST
and CIFAR datasets.
Fisher Kernel for Deep Neural Activations
Authors: Donggeun Yoo, Sunggyun Park, Joon-Young Lee, In So Kweon
Date: 2014-12-04 00:00:00

Compared to image representation based on low-level local descriptors, deep
neural activations of Convolutional Neural Networks (CNNs) are richer in
mid-level representation, but poorer in geometric invariance properties. In
this paper, we present a straightforward framework for better image
representation by combining the two approaches. To take advantages of both
representations, we propose an efficient method to extract a fair amount of
multi-scale dense local activations from a pre-trained CNN. We then aggregate
the activations by Fisher kernel framework, which has been modified with a
simple scale-wise normalization essential to make it suitable for CNN
activations. Replacing the direct use of a single activation vector with our
representation demonstrates significant performance improvements: +17.76 (Acc.)
on MIT Indoor 67 and +7.18 (mAP) on PASCAL VOC 2007. The results suggest that
our proposal can be used as a primary image representation for better
performances in visual recognition tasks.
Information-Theoretic Methods for Identifying Relationships among Climate Variables
Authors: Kevin H. Knuth, Deniz Gençağa, William B. Rossow
Date: 2014-12-19 00:00:00

Information-theoretic quantities, such as entropy, are used to quantify the
amount of information a given variable provides. Entropies can be used together
to compute the mutual information, which quantifies the amount of information
two variables share. However, accurately estimating these quantities from data
is extremely challenging. We have developed a set of computational techniques
that allow one to accurately compute marginal and joint entropies. These
algorithms are probabilistic in nature and thus provide information on the
uncertainty in our estimates, which enable us to establish statistical
significance of our findings. We demonstrate these methods by identifying
relations between cloud data from the International Satellite Cloud Climatology
Project (ISCCP) and data from other sources, such as equatorial pacific sea
surface temperatures (SST).
Multiple Authors Detection: A Quantitative Analysis of Dream of the Red Chamber
Authors: Xianfeng Hu, Yang Wang, Qiang Wu
Date: 2014-12-19 00:00:00
Tasks: Authorship Attribution

Inspired by the authorship controversy of Dream of the Red Chamber and the
application of machine learning in the study of literary stylometry, we develop
a rigorous new method for the mathematical analysis of authorship by testing
for a so-called chrono-divide in writing styles. Our method incorporates some
of the latest advances in the study of authorship attribution, particularly
techniques from support vector machines. By introducing the notion of relative
frequency as a feature ranking metric our method proves to be highly effective
and robust.
  Applying our method to the Cheng-Gao version of Dream of the Red Chamber has
led to convincing if not irrefutable evidence that the first $80$ chapters and
the last $40$ chapters of the book were written by two different authors.
Furthermore, our analysis has unexpectedly provided strong support to the
hypothesis that Chapter 67 was not the work of Cao Xueqin either.
  We have also tested our method to the other three Great Classical Novels in
Chinese. As expected no chrono-divides have been found. This provides further
evidence of the robustness of our method.
Automated Objective Surgical Skill Assessment in the Operating Room Using Unstructured Tool Motion
Authors: Piyush Poddar, Narges Ahmidi, S. Swaroop Vedula, Lisa Ishii, Gregory D. Hager, Masaru Ishii
Date: 2014-12-18 00:00:00
Tasks: Descriptive

Previous work on surgical skill assessment using intraoperative tool motion
in the operating room (OR) has focused on highly-structured surgical tasks such
as cholecystectomy. Further, these methods only considered generic motion
metrics such as time and number of movements, which are of limited instructive
value. In this paper, we developed and evaluated an automated approach to the
surgical skill assessment of nasal septoplasty in the OR. The obstructed field
of view and highly unstructured nature of septoplasty precludes trainees from
efficiently learning the procedure. We propose a descriptive structure of
septoplasty consisting of two types of activity: (1) brushing activity directed
away from the septum plane characterizing the consistency of the surgeon's
wrist motion and (2) activity along the septal plane characterizing the
surgeon's coverage pattern. We derived features related to these two activity
types that classify a surgeon's level of training with an average accuracy of
about 72%. The features we developed provide surgeons with personalized,
actionable feedback regarding their tool motion.
Semantic Part Segmentation using Compositional Model combining Shape and Appearance
Authors: Jianyu Wang, Alan Yuille
Date: 2014-12-18 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection, Pose Estimation, Segmentation, Semantic Segmentation

In this paper, we study the problem of semantic part segmentation for
animals. This is more challenging than standard object detection, object
segmentation and pose estimation tasks because semantic parts of animals often
have similar appearance and highly varying shapes. To tackle these challenges,
we build a mixture of compositional models to represent the object boundary and
the boundaries of semantic parts. And we incorporate edge, appearance, and
semantic part cues into the compositional model. Given part-level segmentation
annotation, we develop a novel algorithm to learn a mixture of compositional
models under various poses and viewpoints for certain animal classes.
Furthermore, a linear complexity algorithm is offered for efficient inference
of the compositional model using dynamic programming. We evaluate our method
for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has
pixelwise part labels. Experimental results demonstrate the effectiveness of
our method.
Compressing Deep Convolutional Networks using Vector Quantization
Authors: Yunchao Gong, Liu Liu, Ming Yang, Lubomir Bourdev
Date: 2014-12-18 00:00:00
Tasks: Classification, Clustering, General Classification, image-classification, Image Classification, object-detection, Object Detection, Object Recognition, Quantization

Deep convolutional neural networks (CNN) has become the most promising method for object recognition, repeatedly demonstrating record breaking results for image classification and object detection in recent years. However, a very deep CNN generally involves many layers with millions of parameters, making the storage of the network model to be extremely large. This prohibits the usage of deep CNNs on resource limited hardware, especially cell phones or other embedded devices. In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs. In particular, we have found in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods. Simply applying k-means clustering to the weights or conducting product quantization can lead to a very good balance between model size and recognition accuracy. For the 1000-category classification task in the ImageNet challenge, we are able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN.
Stochastic Local Search for Pattern Set Mining
Authors: Muktadir Hossain, Tajkia Tasnim, Swakkhar Shatabda, Dewan M. Farid
Date: 2014-12-18 00:00:00

Local search methods can quickly find good quality solutions in cases where
systematic search methods might take a large amount of time. Moreover, in the
context of pattern set mining, exhaustive search methods are not applicable due
to the large search space they have to explore. In this paper, we propose the
application of stochastic local search to solve the pattern set mining.
Specifically, to the task of concept learning. We applied a number of local
search algorithms on a standard benchmark instances for pattern set mining and
the results show the potentials for further exploration.