pwc-archive/papers-with-abstracts huggingface dataset:
Scalability in Neural Control of Musculoskeletal Robots
Authors: Christoph Richter, Sören Jentzsch, Rafael Hostettler, Jesús A. Garrido, Eduardo Ros, Alois C. Knoll, Florian Röhrbein, Patrick van der Smagt, Jörg Conradt
Date: 2016-01-19 00:00:00

Anthropomimetic robots are robots that sense, behave, interact and feel like
humans. By this definition, anthropomimetic robots require human-like physical
hardware and actuation, but also brain-like control and sensing. The most
self-evident realization to meet those requirements would be a human-like
musculoskeletal robot with a brain-like neural controller. While both
musculoskeletal robotic hardware and neural control software have existed for
decades, a scalable approach that could be used to build and control an
anthropomimetic human-scale robot has not been demonstrated yet. Combining
Myorobotics, a framework for musculoskeletal robot development, with SpiNNaker,
a neuromorphic computing platform, we present the proof-of-principle of a
system that can scale to dozens of neurally-controlled, physically compliant
joints. At its core, it implements a closed-loop cerebellar model which
provides real-time low-level neural control at minimal power consumption and
maximal extensibility: higher-order (e.g., cortical) neural networks and
neuromorphic sensors like silicon-retinae or -cochleae can naturally be
incorporated.
Top-N Recommender System via Matrix Completion
Authors: Zhao Kang, Chong Peng, Qiang Cheng
Date: 2016-01-19 00:00:00
Tasks: Matrix Completion, Recommendation Systems

Top-N recommender systems have been investigated widely both in industry and
academia. However, the recommendation quality is far from satisfactory. In this
paper, we propose a simple yet promising algorithm. We fill the user-item
matrix based on a low-rank assumption and simultaneously keep the original
information. To do that, a nonconvex rank relaxation rather than the nuclear
norm is adopted to provide a better rank approximation and an efficient
optimization strategy is designed. A comprehensive set of experiments on real
datasets demonstrates that our method pushes the accuracy of Top-N
recommendation to a new level.
One-bit compressive sensing with norm estimation
Authors: Karin Knudson, Rayan Saab, Rachel Ward
Date: 2014-04-28 00:00:00
Tasks: Compressive Sensing

Consider the recovery of an unknown signal ${x}$ from quantized linear
measurements. In the one-bit compressive sensing setting, one typically assumes
that ${x}$ is sparse, and that the measurements are of the form
$\operatorname{sign}(\langle {a}_i, {x} \rangle) \in \{\pm1\}$. Since such
measurements give no information on the norm of ${x}$, recovery methods from
such measurements typically assume that $\| {x} \|_2=1$. We show that if one
allows more generally for quantized affine measurements of the form
$\operatorname{sign}(\langle {a}_i, {x} \rangle + b_i)$, and if the vectors
${a}_i$ are random, an appropriate choice of the affine shifts $b_i$ allows
norm recovery to be easily incorporated into existing methods for one-bit
compressive sensing. Additionally, we show that for arbitrary fixed ${x}$ in
the annulus $r \leq \| {x} \|_2 \leq R$, one may estimate the norm $\| {x}
\|_2$ up to additive error $\delta$ from $m \gtrsim R^4 r^{-2} \delta^{-2}$
such binary measurements through a single evaluation of the inverse Gaussian
error function. Finally, all of our recovery guarantees can be made universal
over sparse vectors, in the sense that with high probability, one set of
measurements and thresholds can successfully estimate all sparse vectors ${x}$
within a Euclidean ball of known radius.
Feature and Region Selection for Visual Learning
Authors: Ji Zhao, Lian-Tao Wang, Ricardo Cabral, Fernando de la Torre
Date: 2014-07-20 00:00:00
Tasks: Action Recognition, feature selection, Multiple Instance Learning, Temporal Action Localization

Visual learning problems such as object classification and action recognition
are typically approached using extensions of the popular bag-of-words (BoW)
model. Despite its great success, it is unclear what visual features the BoW
model is learning: Which regions in the image or video are used to discriminate
among classes? Which are the most discriminative visual words? Answering these
questions is fundamental for understanding existing BoW models and inspiring
better models for visual recognition.
  To answer these questions, this paper presents a method for feature selection
and region selection in the visual BoW model. This allows for an intermediate
visualization of the features and regions that are important for visual
learning. The main idea is to assign latent weights to the features or regions,
and jointly optimize these latent variables with the parameters of a classifier
(e.g., support vector machine). There are four main benefits of our approach:
(1) Our approach accommodates non-linear additive kernels such as the popular
$\chi^2$ and intersection kernel; (2) our approach is able to handle both
regions in images and spatio-temporal regions in videos in a unified way; (3)
the feature selection problem is convex, and both problems can be solved using
a scalable reduced gradient method; (4) we point out strong connections with
multiple kernel learning and multiple instance learning approaches.
Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube
illustrate the benefits of our approach.
Semi-supervised Learning for Convolutional Neural Networks via Online Graph Construction
Authors: Sheng-Yi Bai, Sebastian Agethen, Ting-Hsuan Chao, Winston Hsu
Date: 2015-11-19 00:00:00
Tasks: graph construction

The recent promising achievements of deep learning rely on the large amount
of labeled data. Considering the abundance of data on the web, most of them do
not have labels at all. Therefore, it is important to improve generalization
performance using unlabeled data on supervised tasks with few labeled
instances. In this work, we revisit graph-based semi-supervised learning
algorithms and propose an online graph construction technique which suits deep
convolutional neural network better. We consider an EM-like algorithm for
semi-supervised learning on deep neural networks: In forward pass, the graph is
constructed based on the network output, and the graph is then used for loss
calculation to help update the network by back propagation in the backward
pass. We demonstrate the strength of our online approach compared to the
conventional ones whose graph is constructed on static but not robust enough
feature representations beforehand.
Improved Sampling Techniques for Learning an Imbalanced Data Set
Authors: Maureen Lyndel C. Lauron, Jaderick P. Pabico
Date: 2016-01-18 00:00:00

This paper presents the performance of a classifier built using the stackingC
algorithm in nine different data sets. Each data set is generated using a
sampling technique applied on the original imbalanced data set. Five new
sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random
Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling
Undersampling, and Combined-Lax Random Undersampling Oversampling) that were
based on the three sampling techniques (i.e., Random Undersampling, Random
Oversampling, and Synthetic Minority Oversampling Technique) usually used as
solutions in imbalance learning. The metrics used to evaluate the classifier's
performance were F-measure and G-mean. F-measure determines the performance of
the classifier for every class, while G-mean measures the overall performance
of the classifier. The results using F-measure showed that for the data without
a sampling technique, the classifier's performance is good only for the
majority class. It also showed that among the eight sampling techniques, RU and
LRU have the worst performance while other techniques (i.e., RO, C-LRUO and
C-LROU) performed well only on some classes. The best performing techniques in
all data sets were SMOTE, SMOTERandRep, and LRO having the lowest F-measure
values between 0.5 and 0.65. The results using G-mean showed that the
oversampling technique that attained the highest G-mean value is LRO (0.86),
next is C-LROU (0.85), then SMOTE (0.84) and finally is SMOTERandRep (0.83).
Combining the result of the two metrics (F-measure and G-mean), only the three
sampling techniques are considered as good performing (i.e., LRO, SMOTE, and
SMOTERandRep).
Spectral Theory of Unsigned and Signed Graphs. Applications to Graph Clustering: a Survey
Authors: Jean Gallier
Date: 2016-01-18 00:00:00
Tasks: Clustering, Graph Clustering

This is a survey of the method of graph cuts and its applications to graph
clustering of weighted unsigned and signed graphs. I provide a fairly thorough
treatment of the method of normalized graph cuts, a deeply original method due
to Shi and Malik, including complete proofs. The main thrust of this paper is
the method of normalized cuts. I give a detailed account for K = 2 clusters,
and also for K > 2 clusters, based on the work of Yu and Shi. I also show how
both graph drawing and normalized cut K-clustering can be easily generalized to
handle signed graphs, which are weighted graphs in which the weight matrix W
may have negative coefficients. Intuitively, negative coefficients indicate
distance or dissimilarity. The solution is to replace the degree matrix by the
matrix in which absolute values of the weights are used, and to replace the
Laplacian by the Laplacian with the new degree matrix of absolute values. As
far as I know, the generalization of K-way normalized clustering to signed
graphs is new. Finally, I show how the method of ratio cuts, in which a cut is
normalized by the size of the cluster rather than its volume, is just a special
case of normalized cuts.
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Authors: Yarin Gal, Zoubin Ghahramani
Date: 2015-06-06 00:00:00
Tasks: Small Data Image Classification, Variational Inference

Convolutional neural networks (CNNs) work well on large datasets. But
labelled data is hard to collect, and in some applications larger amounts of
data are not available. The problem then is how to use CNNs with small data --
as CNNs overfit quickly. We present an efficient Bayesian CNN, offering better
robustness to over-fitting on small data than traditional approaches. This is
by placing a probability distribution over the CNN's kernels. We approximate
our model's intractable posterior with Bernoulli variational distributions,
requiring no additional model parameters.
  On the theoretical side, we cast dropout network training as approximate
inference in Bayesian neural networks. This allows us to implement our model
using existing tools in deep learning with no increase in time complexity,
while highlighting a negative result in the field. We show a considerable
improvement in classification accuracy compared to standard techniques and
improve on published state-of-the-art results for CIFAR-10.
The Image Torque Operator for Contour Processing
Authors: Morimichi Nishigaki, Cornelia Fermüller
Date: 2016-01-18 00:00:00
Tasks: Edge Detection, Image Description, Object Recognition

Contours are salient features for image description, but the detection and
localization of boundary contours is still considered a challenging problem.
This paper introduces a new tool for edge processing implementing the
Gestaltism idea of edge grouping. This tool is a mid-level image operator,
called the Torque operator, that is designed to help detect closed contours in
images. The torque operator takes as input the raw image and creates an image
map by computing from the image gradients within regions of multiple sizes a
measure of how well the edges are aligned to form closed convex contours.
Fundamental properties of the torque are explored and illustrated through
examples. Then it is applied in pure bottom-up processing in a variety of
applications, including edge detection, visual attention and segmentation and
experimentally demonstrated a useful tool that can improve existing techniques.
Finally, its extension as a more general grouping mechanism and application in
object recognition is discussed.
Proactive Message Passing on Memory Factor Networks
Authors: Patrick Eschenfeldt, Dan Schmidt, Stark Draper, Jonathan Yedidia
Date: 2016-01-18 00:00:00
Tasks: Two-sample testing

We introduce a new type of graphical model that we call a "memory factor
network" (MFN). We show how to use MFNs to model the structure inherent in many
types of data sets. We also introduce an associated message-passing style
algorithm called "proactive message passing"' (PMP) that performs inference on
MFNs. PMP comes with convergence guarantees and is efficient in comparison to
competing algorithms such as variants of belief propagation. We specialize MFNs
and PMP to a number of distinct types of data (discrete, continuous, labelled)
and inference problems (interpolation, hypothesis testing), provide examples,
and discuss approaches for efficient implementation.
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders
Authors: Alexander G. Ororbia II, C. Lee Giles, David Reitter
Date: 2015-11-22 00:00:00
Tasks: Denoising

Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and
the Deep Hybrid Denoising Auto-encoder, are proposed for handling
semi-supervised learning problems. The models combine experts that model
relevant distributions at different levels of abstraction to improve overall
predictive performance on discriminative tasks. Theoretical motivations and
algorithms for joint learning for each are presented. We apply the new models
to the domain of data-streams in work towards life-long learning. The proposed
architectures show improved performance compared to a pseudo-labeled, drop-out
rectifier network.
Comparison-based Image Quality Assessment for Parameter Selection
Authors: Haoyi Liang, Daniel S. Weller
Date: 2016-01-18 00:00:00
Tasks: Image Quality Assessment, Image Reconstruction, NR-IQA

Image quality assessment (IQA) is traditionally classified into
full-reference (FR) IQA and no-reference (NR) IQA according to whether the
original image is required. Although NR-IQA is widely used in practical
applications, room for improvement still remains because of the lack of the
reference image. Inspired by the fact that in many applications, such as
parameter selection, a series of distorted images are available, the authors
propose a novel comparison-based image quality assessment (C-IQA) method. The
new comparison-based framework parallels FR-IQA by requiring two input images,
and resembles NR-IQA by not using the original image. As a result, the new
comparison-based approach has more application scenarios than FR-IQA does, and
takes greater advantage of the accessible information than the traditional
single-input NR-IQA does. Further, C-IQA is compared with other
state-of-the-art NR-IQA methods on two widely used IQA databases. Experimental
results show that C-IQA outperforms the other NR-IQA methods for parameter
selection, and the parameter trimming framework combined with C-IQA saves the
computation of iterative image reconstruction up to 80%.
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
Authors: Chuan Li, Michael Wand
Date: 2016-01-18 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Generation, Texture Synthesis

This paper studies a combination of generative Markov random field (MRF)
models and discriminatively trained deep convolutional neural networks (dCNNs)
for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN
feature pyramid, controling the image layout at an abstract level. We apply the
method to both photographic and non-photo-realistic (artwork) synthesis tasks.
The MRF regularizer prevents over-excitation artifacts and reduces implausible
feature mixtures common to previous dCNN inversion approaches, permitting
synthezing photographic content with increased visual plausibility. Unlike
standard MRF-based texture synthesis, the combined system can both match and
adapt local features with considerable variability, yielding results far out of
reach of classic generative MRF methods.
SimpleDS: A Simple Deep Reinforcement Learning Dialogue System
Authors: Heriberto Cuayáhuitl
Date: 2016-01-18 00:00:00
Tasks: Deep Reinforcement Learning, Feature Engineering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents 'SimpleDS', a simple and publicly available dialogue
system trained with deep reinforcement learning. In contrast to previous
reinforcement learning dialogue systems, this system avoids manual feature
engineering by performing action selection directly from raw text of the last
system and (noisy) user responses. Our initial results, in the restaurant
domain, show that it is indeed possible to induce reasonable dialogue behaviour
with an approach that aims for high levels of automation in dialogue control
for intelligent interactive agents.
Content Aware Neural Style Transfer
Authors: Rujie Yin
Date: 2016-01-18 00:00:00
Tasks: Style Transfer

This paper presents a content-aware style transfer algorithm for paintings
and photos of similar content using pre-trained neural network, obtaining
better results than the previous work. In addition, the numerical experiments
show that the style pattern and the content information is not completely
separated by neural network.
Incremental Semiparametric Inverse Dynamics Learning
Authors: Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori
Date: 2016-01-18 00:00:00

This paper presents a novel approach for incremental semiparametric inverse
dynamics learning. In particular, we consider the mixture of two approaches:
Parametric modeling based on rigid body dynamics equations and nonparametric
modeling based on incremental kernel methods, with no prior information on the
mechanical properties of the system. This yields to an incremental
semiparametric approach, leveraging the advantages of both the parametric and
nonparametric models. We validate the proposed technique learning the dynamics
of one arm of the iCub humanoid robot.
Domain based classification
Authors: Robert P. W. Duin, Elzbieta Pekalska
Date: 2016-01-18 00:00:00
Tasks: Classification, General Classification

The majority of traditional classification ru les minimizing the expected
probability of error (0-1 loss) are inappropriate if the class probability
distributions are ill-defined or impossible to estimate. We argue that in such
cases class domains should be used instead of class distributions or densities
to construct a reliable decision function. Proposals are presented for some
evaluation criteria and classifier learning schemes, illustrated by an example.
Probabilistic Line Searches for Stochastic Optimization
Authors: Maren Mahsereci, Philipp Hennig
Date: 2015-02-10 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Bayesian Optimization, Stochastic Optimization

In deterministic optimization, line searches are a standard tool ensuring
stability and efficiency. Where only stochastic gradients are available, no
direct equivalent has so far been formulated, because uncertain gradients do
not allow for a strict sequence of decisions collapsing the search space. We
construct a probabilistic line search by combining the structure of existing
deterministic methods with notions from Bayesian optimization. Our method
retains a Gaussian process surrogate of the univariate optimization objective,
and uses a probabilistic belief over the Wolfe conditions to monitor the
descent. The algorithm has very low computational cost, and no user-controlled
parameters. Experiments show that it effectively removes the need to define a
learning rate for stochastic gradient descent.
Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation
Authors: Artem Sokolov, Stefan Riezler, Tanguy Urvoy
Date: 2016-01-18 00:00:00
Tasks: Machine Translation, Reranking, Structured Prediction, Translation

We present an approach to structured prediction from bandit feedback, called
Bandit Structured Prediction, where only the value of a task loss function at a
single predicted point, instead of a correct structure, is observed in
learning. We present an application to discriminative reranking in Statistical
Machine Translation (SMT) where the learning algorithm only has access to a
1-BLEU loss evaluation of a predicted translation instead of obtaining a gold
standard reference translation. In our experiment bandit feedback is obtained
by evaluating BLEU on reference translations without revealing them to the
algorithm. This can be thought of as a simulation of interactive machine
translation where an SMT system is personalized by a user who provides single
point feedback to predicted translations. Our experiments show that our
approach improves translation quality and is comparable to approaches that
employ more informative feedback in learning.
Zero-error dissimilarity based classifiers
Authors: Robert P. W. Duin, Elzbieta Pekalska
Date: 2016-01-18 00:00:00

We consider general non-Euclidean distance measures between real world
objects that need to be classified. It is assumed that objects are represented
by distances to other objects only. Conditions for zero-error dissimilarity
based classifiers are derived. Additional conditions are given under which the
zero-error decision boundary is a continues function of the distances to a
finite set of training samples. These conditions affect the objects as well as
the distance measure used. It is argued that they can be met in practice.
Kauffman's adjacent possible in word order evolution
Authors: Ramon Ferrer-i-Cancho
Date: 2015-12-17 00:00:00
Tasks: Model Selection

Word order evolution has been hypothesized to be constrained by a word order
permutation ring: transitions involving orders that are closer in the
permutation ring are more likely. The hypothesis can be seen as a particular
case of Kauffman's adjacent possible in word order evolution. Here we consider
the problem of the association of the six possible orders of S, V and O to
yield a couple of primary alternating orders as a window to word order
evolution. We evaluate the suitability of various competing hypotheses to
predict one member of the couple from the other with the help of information
theoretic model selection. Our ensemble of models includes a six-way model that
is based on the word order permutation ring (Kauffman's adjacent possible) and
another model based on the dual two-way of standard typology, that reduces word
order to basic orders preferences (e.g., a preference for SV over VS and
another for SO over OS). Our analysis indicates that the permutation ring
yields the best model when favoring parsimony strongly, providing support for
Kauffman's general view and a six-way typology.
Discovering Picturesque Highlights from Egocentric Vacation Videos
Authors: Vinay Bettadapura, Daniel Castro, Irfan Essa
Date: 2016-01-18 00:00:00
Tasks: Highlight Detection

We present an approach for identifying picturesque highlights from large
amounts of egocentric video data. Given a set of egocentric videos captured
over the course of a vacation, our method analyzes the videos and looks for
images that have good picturesque and artistic properties. We introduce novel
techniques to automatically determine aesthetic features such as composition,
symmetry and color vibrancy in egocentric videos and rank the video frames
based on their photographic qualities to generate highlights. Our approach also
uses contextual information such as GPS, when available, to assess the relative
importance of each geographic location where the vacation videos were shot.
Furthermore, we specifically leverage the properties of egocentric videos to
improve our highlight detection. We demonstrate results on a new egocentric
vacation dataset which includes 26.5 hours of videos taken over a 14 day
vacation that spans many famous tourist destinations and also provide results
from a user-study to access our results.