pwc-archive/papers-with-abstracts huggingface dataset:
Enabling Bio-Plausible Multi-level STDP using CMOS Neurons with Dendrites and Bistable RRAMs
Authors: Xinyu Wu, Vishal Saxena
Date: 2016-12-05 00:00:00

Large-scale integration of emerging nanoscale non-volatile memory devices,
e.g. resistive random-access memory (RRAM), can enable a new generation of
neuromorphic computers that can solve a wide range of machine learning
problems. Such hybrid CMOS-RRAM neuromorphic architectures will result in
several orders of magnitude reduction in energy consumption at a very small
form factor, and herald autonomous learning machines capable of self-adapting
to their environment. However, the progress in this area has been impeded from
the realization that the actual memory devices fall well short of their
expected behavior. In this work, we discuss the challenges associated with
these memory devices and their use in neuromorphic computing circuits, and
propose pathways to overcome these limitations by introducing 'dendritic
learning'.
Parsing Images of Overlapping Organisms with Deep Singling-Out Networks
Authors: Victor Yurchenko, Victor Lempitsky
Date: 2016-12-19 00:00:00
Proceeding: CVPR 2017 7

This work is motivated by the mostly unsolved task of parsing biological
images with multiple overlapping articulated model organisms (such as worms or
larvae). We present a general approach that separates the two main challenges
associated with such data, individual object shape estimation and object groups
disentangling. At the core of the approach is a deep feed-forward singling-out
network (SON) that is trained to map each local patch to a vectorial descriptor
that is sensitive to the characteristics (e.g. shape) of a central object,
while being invariant to the variability of all other surrounding elements.
Given a SON, a local image patch can be matched to a gallery of isolated
elements using their SON-descriptors, thus producing a hypothesis about the
shape of the central element in that patch. The image-level optimization based
on integer programming can then pick a subset of the hypotheses to explain
(parse) the whole image and disentangle groups of organisms.
  While sharing many similarities with existing "analysis-by-synthesis"
approaches, our method avoids the need for stochastic search in the
high-dimensional configuration space and numerous rendering operations at
test-time. We show that our approach can parse microscopy images of three
popular model organisms (the C.Elegans roundworms, the Drosophila larvae, and
the E.Coli bacteria) even under significant crowding and overlaps between
organisms. We speculate that the overall approach is applicable to a wider
class of image parsing problems concerned with crowded articulated objects, for
which rendering training images is possible.
Separating Sets of Strings by Finding Matching Patterns is Almost Always Hard
Authors: Giuseppe Lancia, Luke Mathieson, Pablo Moscato
Date: 2016-04-12 00:00:00

We study the complexity of the problem of searching for a set of patterns
that separate two given sets of strings. This problem has applications in a
wide variety of areas, most notably in data mining, computational biology, and
in understanding the complexity of genetic algorithms. We show that the basic
problem of finding a small set of patterns that match one set of strings but do
not match any string in a second set is difficult (NP-complete, W[2]-hard when
parameterized by the size of the pattern set, and APX-hard). We then perform a
detailed parameterized analysis of the problem, separating tractable and
intractable variants. In particular we show that parameterizing by the size of
pattern set and the number of strings, and the size of the alphabet and the
number of strings give FPT results, amongst others.
Inexact Proximal Gradient Methods for Non-convex and Non-smooth Optimization
Authors: Bin Gu, De Wang, Zhouyuan Huo, Heng Huang
Date: 2016-12-18 00:00:00
Tasks: BIG-bench Machine Learning

In machine learning research, the proximal gradient methods are popular for
solving various optimization problems with non-smooth regularization. Inexact
proximal gradient methods are extremely important when exactly solving the
proximal operator is time-consuming, or the proximal operator does not have an
analytic solution. However, existing inexact proximal gradient methods only
consider convex problems. The knowledge of inexact proximal gradient methods in
the non-convex setting is very limited. % Moreover, for some machine learning
models, there is still no proposed solver for exactly solving the proximal
operator. To address this challenge, in this paper, we first propose three
inexact proximal gradient algorithms, including the basic version and
Nesterov's accelerated version. After that, we provide the theoretical analysis
to the basic and Nesterov's accelerated versions. The theoretical results show
that our inexact proximal gradient algorithms can have the same convergence
rates as the ones of exact proximal gradient algorithms in the non-convex
setting.
  Finally, we show the applications of our inexact proximal gradient algorithms
on three representative non-convex learning problems. All experimental results
confirm the superiority of our new inexact proximal gradient algorithms.
Sample-efficient Deep Reinforcement Learning for Dialog Control
Authors: Kavosh Asadi, Jason D. Williams
Date: 2016-12-18 00:00:00
Tasks: Deep Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Representing a dialog policy as a recurrent neural network (RNN) is
attractive because it handles partial observability, infers a latent
representation of state, and can be optimized with supervised learning (SL) or
reinforcement learning (RL). For RL, a policy gradient approach is natural, but
is sample inefficient. In this paper, we present 3 methods for reducing the
number of dialogs required to optimize an RNN-based dialog policy with RL. The
key idea is to maintain a second RNN which predicts the value of the current
policy, and to apply experience replay to both networks. On two tasks, these
methods reduce the number of dialogs/episodes required by about a third, vs.
standard policy gradient methods.
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Authors: Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
Date: 2016-12-18 00:00:00
Tasks: Classification, General Classification, Whole Mammogram Classification

Mammogram classification is directly related to computer-aided diagnosis of
breast cancer. Traditional methods requires great effort to annotate the
training data by costly manual labeling and specialized computational models to
detect these annotations during test. Inspired by the success of using deep
convolutional features for natural image analysis and multi-instance learning
for labeling a set of instances/patches, we propose end-to-end trained deep
multi-instance networks for mass classification based on whole mammogram
without the aforementioned costly need to annotate the training data. We
explore three different schemes to construct deep multi-instance networks for
whole mammogram classification. Experimental results on the INbreast dataset
demonstrate the robustness of proposed deep networks compared to previous work
using segmentation and detection annotations in the training.
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity
Authors: James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett
Date: 2016-05-13 00:00:00

The challenging task of learning structures of probabilistic graphical models
is an important problem within modern AI research. Recent years have witnessed
several major algorithmic advances in structure learning for Bayesian
networks---arguably the most central class of graphical models---especially in
what is known as the score-based setting. A successful generic approach to
optimal Bayesian network structure learning (BNSL), based on integer
programming (IP), is implemented in the GOBNILP system. Despite the recent
algorithmic advances, current understanding of foundational aspects underlying
the IP based approach to BNSL is still somewhat lacking. Understanding
fundamental aspects of cutting planes and the related separation problem( is
important not only from a purely theoretical perspective, but also since it
holds out the promise of further improving the efficiency of state-of-the-art
approaches to solving BNSL exactly. In this paper, we make several theoretical
contributions towards these goals: (i) we study the computational complexity of
the separation problem, proving that the problem is NP-hard; (ii) we formalise
and analyse the relationship between three key polytopes underlying the
IP-based approach to BNSL; (iii) we study the facets of the three polytopes
both from the theoretical and practical perspective, providing, via exhaustive
computation, a complete enumeration of facets for low-dimensional
family-variable polytopes; and, furthermore, (iv) we establish a tight
connection of the BNSL problem to the acyclic subgraph problem.
Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and its Asymptotic Optimality
Authors: Ganggang Xu, Zuofeng Shang, Guang Cheng
Date: 2016-12-18 00:00:00
Proceeding: ICML 2018 7
Tasks: regression

Tuning parameter selection is of critical importance for kernel ridge
regression. To this date, data driven tuning method for divide-and-conquer
kernel ridge regression (d-KRR) has been lacking in the literature, which
limits the applicability of d-KRR for large data sets. In this paper, by
modifying the Generalized Cross-validation (GCV, Wahba, 1990) score, we propose
a distributed Generalized Cross-Validation (dGCV) as a data-driven tool for
selecting the tuning parameters in d-KRR. Not only the proposed dGCV is
computationally scalable for massive data sets, it is also shown, under mild
conditions, to be asymptotically optimal in the sense that minimizing the dGCV
score is equivalent to minimizing the true global conditional empirical loss of
the averaged function estimator, extending the existing optimality results of
GCV to the divide-and-conquer framework.
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
Authors: Chao Ma, Chih-Yuan Yang, Xiaokang Yang, Ming-Hsuan Yang
Date: 2016-12-18 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Video Quality Assessment

Numerous single-image super-resolution algorithms have been proposed in the
literature, but few studies address the problem of performance evaluation based
on visual perception. While most super-resolution images are evaluated by
fullreference metrics, the effectiveness is not clear and the required
ground-truth images are not always available in practice. To address these
problems, we conduct human subject studies using a large set of
super-resolution images and propose a no-reference metric learned from visual
perceptual scores. Specifically, we design three types of low-level statistical
features in both spatial and frequency domains to quantify super-resolved
artifacts, and learn a two-stage regression model to predict the quality scores
of super-resolution images without referring to ground-truth images. Extensive
experimental results show that the proposed metric is effective and efficient
to assess the quality of super-resolution images based on human perception.
3D Shape Induction from 2D Views of Multiple Objects
Authors: Matheus Gadelha, Subhransu Maji, Rui Wang
Date: 2016-12-18 00:00:00

In this paper we investigate the problem of inducing a distribution over
three-dimensional structures given two-dimensional views of multiple objects
taken from unknown viewpoints. Our approach called "projective generative
adversarial networks" (PrGANs) trains a deep generative model of 3D shapes
whose projections match the distributions of the input 2D views. The addition
of a projection module allows us to infer the underlying 3D shape distribution
without using any 3D, viewpoint information, or annotation during the learning
phase. We show that our approach produces 3D shapes of comparable quality to
GANs trained on 3D data for a number of shape categories including chairs,
airplanes, and cars. Experiments also show that the disentangled representation
of 2D shapes into geometry and viewpoint leads to a good generative model of 2D
shapes. The key advantage is that our model allows us to predict 3D, viewpoint,
and generate novel views from an input image in a completely unsupervised
manner.
Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems
Authors: Alyssa M Adams, Hector Zenil, Paul CW Davies, Sara I. Walker
Date: 2016-07-06 00:00:00

Open-ended evolution (OEE) is relevant to a variety of biological, artificial
and technological systems, but has been challenging to reproduce in silico.
Most theoretical efforts focus on key aspects of open-ended evolution as it
appears in biology. We recast the problem as a more general one in dynamical
systems theory, providing simple criteria for open-ended evolution based on two
hallmark features: unbounded evolution and innovation. We define unbounded
evolution as patterns that are non-repeating within the expected Poincare
recurrence time of an equivalent isolated system, and innovation as
trajectories not observed in isolated systems. As a case study, we implement
novel variants of cellular automata (CA) in which the update rules are allowed
to vary with time in three alternative ways. Each is capable of generating
conditions for open-ended evolution, but vary in their ability to do so. We
find that state-dependent dynamics, widely regarded as a hallmark of life,
statistically out-performs other candidate mechanisms, and is the only
mechanism to produce open-ended evolution in a scalable manner, essential to
the notion of ongoing evolution. This analysis suggests a new framework for
unifying mechanisms for generating OEE with features distinctive to life and
its artifacts, with broad applicability to biological and artificial systems.
Optimal whitening and decorrelation
Authors: Agnan Kessy, Alex Lewin, Korbinian Strimmer
Date: 2015-12-02 00:00:00

Whitening, or sphering, is a common preprocessing step in statistical
analysis to transform random variables to orthogonality. However, due to
rotational freedom there are infinitely many possible whitening procedures.
Consequently, there is a diverse range of sphering methods in use, for example
based on principal component analysis (PCA), Cholesky matrix decomposition and
zero-phase component analysis (ZCA), among others.
  Here we provide an overview of the underlying theory and discuss five natural
whitening procedures. Subsequently, we demonstrate that investigating the
cross-covariance and the cross-correlation matrix between sphered and original
variables allows to break the rotational invariance and to identify optimal
whitening transformations. As a result we recommend two particular approaches:
ZCA-cor whitening to produce sphered variables that are maximally similar to
the original variables, and PCA-cor whitening to obtain sphered variables that
maximally compress the original variables.
EgoTransfer: Transferring Motion Across Egocentric and Exocentric Domains using Deep Neural Networks
Authors: Shervin Ardeshir, Krishna Regmi, Ali Borji
Date: 2016-12-17 00:00:00

Mirror neurons have been observed in the primary motor cortex of primate
species, in particular in humans and monkeys. A mirror neuron fires when a
person performs a certain action, and also when he observes the same action
being performed by another person. A crucial step towards building fully
autonomous intelligent systems with human-like learning abilities is the
capability in modeling the mirror neuron. On one hand, the abundance of
egocentric cameras in the past few years has offered the opportunity to study a
lot of vision problems from the first-person perspective. A great deal of
interesting research has been done during the past few years, trying to explore
various computer vision tasks from the perspective of the self. On the other
hand, videos recorded by traditional static cameras, capture humans performing
different actions from an exocentric third-person perspective. In this work, we
take the first step towards relating motion information across these two
perspectives. We train models that predict motion in an egocentric view, by
observing it from an exocentric view, and vice versa. This allows models to
predict how an egocentric motion would look like from outside. To do so, we
train linear and nonlinear models and evaluate their performance in terms of
retrieving the egocentric (exocentric) motion features, while having access to
an exocentric (egocentric) motion feature. Our experimental results demonstrate
that motion information can be successfully transferred across the two views.
Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture
Authors: Sushrut Thorat, Varad Choudhari
Date: 2016-05-31 00:00:00
Proceeding: COLING 2016 12
Tasks: Reverse Dictionary

In this paper, we outline an approach to build graph-based reverse
dictionaries using word definitions. A reverse dictionary takes a phrase as an
input and outputs a list of words semantically similar to that phrase. It is a
solution to the Tip-of-the-Tongue problem. We use a distance-based similarity
measure, computed on a graph, to assess the similarity between a word and the
input phrase. We compare the performance of our approach with the Onelook
Reverse Dictionary and a distributional semantics method based on word2vec, and
show that our approach is much better than the distributional semantics method,
and as good as Onelook, on a 3k lexicon. This simple approach sets a new
performance baseline for reverse dictionaries.
The Dem@Care Experiments and Datasets: a Technical Report
Authors: Anastasios Karakostas, Alexia Briassouli, Konstantinos Avgerinakis, Ioannis Kompatsiaris, Magda Tsolaki
Date: 2016-12-17 00:00:00
Tasks: Decision Making

The objective of Dem@Care is the development of a complete system providing
personal health services to people with dementia, as well as medical
professionals and caregivers, by using a multitude of sensors, for
context-aware, multi-parametric monitoring of lifestyle, ambient environment,
and health parameters. Multi-sensor data analysis, combined with intelligent
decision making mechanisms, will allow an accurate representation of the
person's current status and will provide the appropriate feedback, both to the
person and the associated caregivers, enhancing the standard clinical workflow.
Within the project framework, several data collection activities have taken
place to assist technical development and evaluation tasks. In all these
activities, particular attention has been paid to adhere to ethical guidelines
and preserve the participants' privacy. This technical report describes shorty
the (a) the main objectives of the project, (b) the main ethical principles and
(c) the datasets that have been already created.
A new recurrent neural network based predictive model for Faecal Calprotectin analysis: A retrospective study
Authors: Zeeshan Khawar Malik, Zain U. Hussain, Ziad Kobti, Charlie W. Lees, Newton Howard, Amir Hussain
Date: 2016-12-17 00:00:00
Tasks: BIG-bench Machine Learning, regression

Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation,
termed Inflammatory Bowel Disease (IBD), but not for cancer. In this
retrospective study of 804 patients, an enhanced benchmark predictive model for
analyzing FC is developed, based on a novel state-of-the-art Echo State Network
(ESN), an advanced dynamic recurrent neural network which implements a
biologically plausible architecture, and a supervised learning mechanism. The
proposed machine learning driven predictive model is benchmarked against a
conventional logistic regression model, demonstrating statistically significant
performance improvements.
Variations on Memetic Algorithms for Graph Coloring Problems
Authors: Laurent Moalic, Alexandre Gondran
Date: 2014-01-08 00:00:00
Tasks: Diversity

Graph vertex coloring with a given number of colors is a well-known and
much-studied NP-complete problem.The most effective methods to solve this
problem are proved to be hybrid algorithms such as memetic algorithms or
quantum annealing. Those hybrid algorithms use a powerful local search inside a
population-based algorithm.This paper presents a new memetic algorithm based on
one of the most effective algorithms: the Hybrid Evolutionary Algorithm HEA
from Galinier and Hao (1999).The proposed algorithm, denoted HEAD - for HEA in
Duet - works with a population of only two individuals.Moreover, a new way of
managing diversity is brought by HEAD.These two main differences greatly
improve the results, both in terms of solution quality and computational
time.HEAD has produced several good results for the popular DIMACS benchmark
graphs, such as 222-colorings for \textless{}dsjc1000.9\textgreater{},
81-colorings for \textless{}flat1000\_76\_0\textgreater{} and even 47-colorings
for \textless{}dsjc500.5\textgreater{} and 82-colorings for
\textless{}dsjc1000.5\textgreater{}.
A Comment on Argumentation
Authors: Karl Schlechta
Date: 2016-12-17 00:00:00

We use the theory of defaults and their meaning of [GS16] to develop (the
outline of a) new theory of argumentation.
Machine Learning, Linear and Bayesian Models for Logistic Regression in Failure Detection Problems
Authors: B. Pavlyshenko
Date: 2016-12-17 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

In this work, we study the use of logistic regression in manufacturing
failures detection. As a data set for the analysis, we used the data from
Kaggle competition Bosch Production Line Performance. We considered the use of
machine learning, linear and Bayesian models. For machine learning approach, we
analyzed XGBoost tree based classifier to obtain high scored classification.
Using the generalized linear model for logistic regression makes it possible to
analyze the influence of the factors under study. The Bayesian approach for
logistic regression gives the statistical distribution for the parameters of
the model. It can be useful in the probabilistic analysis, e.g. risk
assessment.
Web-based Semantic Similarity for Emotion Recognition in Web Objects
Authors: Valentina Franzoni, Giulio Biondi, Alfredo Milani, Yuanxi Li
Date: 2016-12-17 00:00:00
Tasks: Emotion Recognition, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentiment Analysis

In this project we propose a new approach for emotion recognition using
web-based similarity (e.g. confidence, PMI and PMING). We aim to extract basic
emotions from short sentences with emotional content (e.g. news titles, tweets,
captions), performing a web-based quantitative evaluation of semantic proximity
between each word of the analyzed sentence and each emotion of a psychological
model (e.g. Plutchik, Ekman, Lovheim). The phases of the extraction include:
text preprocessing (tokenization, stop words, filtering), search engine
automated query, HTML parsing of results (i.e. scraping), estimation of
semantic proximity, ranking of emotions according to proximity measures. The
main idea is that, since it is possible to generalize semantic similarity under
the assumption that similar concepts co-occur in documents indexed in search
engines, therefore also emotions can be generalized in the same way, through
tags or terms that express them in a particular language, ranking emotions.
Training results are compared to human evaluation, then additional comparative
tests on results are performed, both for the global ranking correlation (e.g.
Kendall, Spearman, Pearson) both for the evaluation of the emotion linked to
each single word. Different from sentiment analysis, our approach works at a
deeper level of abstraction, aiming at recognizing specific emotions and not
only the positive/negative sentiment, in order to predict emotions as semantic
data.