pwc-archive/papers-with-abstracts huggingface dataset:
Ground Truth Bias in External Cluster Validity Indices
Authors: Yang Lei, James C. Bezdek, Simone Romano, Nguyen Xuan Vinh, Jeffrey Chan, James Bailey
Date: 2016-06-17 00:00:00
Tasks: Vocal Bursts Type Prediction

It has been noticed that some external CVIs exhibit a preferential bias
towards a larger or smaller number of clusters which is monotonic (directly or
inversely) in the number of clusters in candidate partitions. This type of bias
is caused by the functional form of the CVI model. For example, the popular
Rand index (RI) exhibits a monotone increasing (NCinc) bias, while the Jaccard
Index (JI) index suffers from a monotone decreasing (NCdec) bias. This type of
bias has been previously recognized in the literature. In this work, we
identify a new type of bias arising from the distribution of the ground truth
(reference) partition against which candidate partitions are compared. We call
this new type of bias ground truth (GT) bias. This type of bias occurs if a
change in the reference partition causes a change in the bias status (e.g.,
NCinc, NCdec) of a CVI. For example, NCinc bias in the RI can be changed to
NCdec bias by skewing the distribution of clusters in the ground truth
partition. It is important for users to be aware of this new type of biased
behaviour, since it may affect the interpretations of CVI results. The
objective of this article is to study the empirical and theoretical
implications of GT bias. To the best of our knowledge, this is the first
extensive study of such a property for external cluster validity indices.
Introspective Agents: Confidence Measures for General Value Functions
Authors: Craig Sherstan, Adam White, Marlos C. Machado, Patrick M. Pilarski
Date: 2016-06-17 00:00:00
Tasks: Position

Agents of general intelligence deployed in real-world scenarios must adapt to
ever-changing environmental conditions. While such adaptive agents may leverage
engineered knowledge, they will require the capacity to construct and evaluate
knowledge themselves from their own experience in a bottom-up, constructivist
fashion. This position paper builds on the idea of encoding knowledge as
temporally extended predictions through the use of general value functions.
Prior work has focused on learning predictions about externally derived signals
about a task or environment (e.g. battery level, joint position). Here we
advocate that the agent should also predict internally generated signals
regarding its own learning process - for example, an agent's confidence in its
learned predictions. Finally, we suggest how such information would be
beneficial in creating an introspective agent that is able to learn to make
good decisions in a complex, changing world.
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Authors: Abhishek Das, Harsh Agrawal, C. Lawrence Zitnick, Devi Parikh, Dhruv Batra
Date: 2016-06-17 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We conduct large-scale studies on `human attention' in Visual Question
Answering (VQA) to understand where humans choose to look to answer questions
about images. We design and test multiple game-inspired novel
attention-annotation interfaces that require the subject to sharpen regions of
a blurred image to answer a question. Thus, we introduce the VQA-HAT (Human
ATtention) dataset. We evaluate attention maps generated by state-of-the-art
VQA models against human attention both qualitatively (via visualizations) and
quantitatively (via rank-order correlation). Overall, our experiments show that
current attention models in VQA do not seem to be looking at the same regions
as humans.
A Proposal for Linguistic Similarity Datasets Based on Commonality Lists
Authors: Dmitrijs Milajevs, Sascha Griffiths
Date: 2016-05-15 00:00:00
Proceeding: WS 2016 8
Tasks: Sentence, Sentence Similarity

Similarity is a core notion that is used in psychology and two branches of
linguistics: theoretical and computational. The similarity datasets that come
from the two fields differ in design: psychological datasets are focused around
a certain topic such as fruit names, while linguistic datasets contain words
from various categories. The later makes humans assign low similarity scores to
the words that have nothing in common and to the words that have contrast in
meaning, making similarity scores ambiguous. In this work we discuss the
similarity collection procedure for a multi-category dataset that avoids score
ambiguity and suggest changes to the evaluation procedure to reflect the
insights of psychological literature for word, phrase and sentence similarity.
We suggest to ask humans to provide a list of commonalities and differences
instead of numerical similarity scores and employ the structure of human
judgements beyond pairwise similarity for model evaluation. We believe that the
proposed approach will give rise to datasets that test meaning representation
models more thoroughly with respect to the human treatment of similarity.
Learning Interpretable Musical Compositional Rules and Traces
Authors: Haizi Yu, Lav R. Varshney, Guy E. Garnett, Ranjitha Kumar
Date: 2016-06-17 00:00:00
Tasks: Self-Learning

Throughout music history, theorists have identified and documented
interpretable rules that capture the decisions of composers. This paper asks,
"Can a machine behave like a music theorist?" It presents MUS-ROVER, a
self-learning system for automatically discovering rules from symbolic music.
MUS-ROVER performs feature learning via $n$-gram models to extract
compositional rules --- statistical patterns over the resulting features. We
evaluate MUS-ROVER on Bach's (SATB) chorales, demonstrating that it can recover
known rules, as well as identify new, characteristic patterns for further
study. We discuss how the extracted rules can be used in both machine and human
composition.
Image-Based Correction of Continuous and Discontinuous Non-Planar Axial Distortion in Serial Section Microscopy
Authors: Philipp Hanslovsky, John A. Bogovic, Stephan Saalfeld
Date: 2015-11-03 00:00:00
Tasks: Anatomy

Motivation: Serial section microscopy is an established method for detailed
anatomy reconstruction of biological specimen. During the last decade, high
resolution electron microscopy (EM) of serial sections has become the de-facto
standard for reconstruction of neural connectivity at ever increasing scales
(EM connectomics). In serial section microscopy, the axial dimension of the
volume is sampled by physically removing thin sections from the embedded
specimen and subsequently imaging either the block-face or the section series.
This process has limited precision leading to inhomogeneous non-planar sampling
of the axial dimension of the volume which, in turn, results in distorted image
volumes. This includes that section series may be collected and imaged in
unknown order.
  Results: We developed methods to identify and correct these distortions
through image-based signal analysis without any additional physical apparatus
or measurements. We demonstrate the efficacy of our methods in proof of
principle experiments and application to real world problems.
  Availability and Implementation: We made our work available as libraries for
the ImageJ distribution Fiji and for deployment in a high performance parallel
computing environment. Our sources are open and available at
http://github.com/saalfeldla/section-sort,
http://github.com/saalfeldlab/em-thickness-estimation, and
http://github.com/saalfeldlab/z-spacing-spark.
  Contact: saalfelds@janelia.hhmi.org
SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder
Authors: Noura Al Moubayed, Toby Breckon, Peter Matthews, A. Stephen McGough
Date: 2016-06-17 00:00:00
Tasks: Denoising, Spam detection

In This paper we present a novel approach to spam filtering and demonstrate
its applicability with respect to SMS messages. Our approach requires minimum
features engineering and a small set of la- belled data samples. Features are
extracted using topic modelling based on latent Dirichlet allocation, and then
a comprehensive data model is created using a Stacked Denoising Autoencoder
(SDA). Topic modelling summarises the data providing ease of use and high
interpretability by visualising the topics using word clouds. Given that the
SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA
is able to model the messages and accurately discriminate between the two
classes without the need for a pre-labelled training set. The results are
compared against the state-of-the-art spam detection algorithms with our
proposed approach achieving over 97% accuracy which compares favourably to the
best reported algorithms presented in the literature.
Self-adaptation of Mutation Rates in Non-elitist Populations
Authors: Duc-Cuong Dang, Per Kristian Lehre
Date: 2016-06-17 00:00:00
Tasks: Evolutionary Algorithms

The runtime of evolutionary algorithms (EAs) depends critically on their
parameter settings, which are often problem-specific. Automated schemes for
parameter tuning have been developed to alleviate the high costs of manual
parameter tuning. Experimental results indicate that self-adaptation, where
parameter settings are encoded in the genomes of individuals, can be effective
in continuous optimisation. However, results in discrete optimisation have been
less conclusive. Furthermore, a rigorous runtime analysis that explains how
self-adaptation can lead to asymptotic speedups has been missing. This paper
provides the first such analysis for discrete, population-based EAs. We apply
level-based analysis to show how a self-adaptive EA is capable of fine-tuning
its mutation rate, leading to exponential speedups over EAs using fixed
mutation rates.
Tensor Ring Decomposition
Authors: Qibin Zhao, Guoxu Zhou, Shengli Xie, Liqing Zhang, Andrzej Cichocki
Date: 2016-06-17 00:00:00
Tasks: Tensor Decomposition, Tensor Networks

Tensor networks have in recent years emerged as the powerful tools for
solving the large-scale optimization problems. One of the most popular tensor
network is tensor train (TT) decomposition that acts as the building blocks for
the complicated tensor networks. However, the TT decomposition highly depends
on permutations of tensor dimensions, due to its strictly sequential
multilinear products over latent cores, which leads to difficulties in finding
the optimal TT representation. In this paper, we introduce a fundamental tensor
decomposition model to represent a large dimensional tensor by a circular
multilinear products over a sequence of low dimensional cores, which can be
graphically interpreted as a cyclic interconnection of 3rd-order tensors, and
thus termed as tensor ring (TR) decomposition. The key advantage of TR model is
the circular dimensional permutation invariance which is gained by employing
the trace operation and treating the latent cores equivalently. TR model can be
viewed as a linear combination of TT decompositions, thus obtaining the
powerful and generalized representation abilities. For optimization of latent
cores, we present four different algorithms based on the sequential SVDs, ALS
scheme, and block-wise ALS techniques. Furthermore, the mathematical properties
of TR model are investigated, which shows that the basic multilinear algebra
can be performed efficiently by using TR representaions and the classical
tensor decompositions can be conveniently transformed into the TR
representation. Finally, the experiments on both synthetic signals and
real-world datasets were conducted to evaluate the performance of different
algorithms.
Learning Abstract Classes using Deep Learning
Authors: Sebastian Stabinger, Antonio Rodriguez-Sanchez, Justus Piater
Date: 2016-06-17 00:00:00
Tasks: Deep Learning

Humans are generally good at learning abstract concepts about objects and
scenes (e.g.\ spatial orientation, relative sizes, etc.). Over the last years
convolutional neural networks have achieved almost human performance in
recognizing concrete classes (i.e.\ specific object categories). This paper
tests the performance of a current CNN (GoogLeNet) on the task of
differentiating between abstract classes which are trivially differentiable for
humans. We trained and tested the CNN on the two abstract classes of horizontal
and vertical orientation and determined how well the network is able to
transfer the learned classes to other, previously unseen objects.
Car Segmentation and Pose Estimation using 3D Object Models
Authors: Siddharth Mahendran, René Vidal
Date: 2015-12-21 00:00:00
Tasks: 3D Pose Estimation, Image Segmentation, Object, Pose Estimation, Scene Understanding, Segmentation, Semantic Segmentation

Image segmentation and 3D pose estimation are two key cogs in any algorithm
for scene understanding. However, state-of-the-art CRF-based models for image
segmentation rely mostly on 2D object models to construct top-down high-order
potentials. In this paper, we propose new top-down potentials for image
segmentation and pose estimation based on the shape and volume of a 3D object
model. We show that these complex top-down potentials can be easily decomposed
into standard forms for efficient inference in both the segmentation and pose
estimation tasks. Experiments on a car dataset show that knowledge of
segmentation helps perform pose estimation better and vice versa.
Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings
Authors: Ondřej Dušek, Filip Jurčíček
Date: 2016-06-17 00:00:00
Tasks: Sentence

We present a natural language generator based on the sequence-to-sequence
approach that can be trained to produce natural language strings as well as
deep syntax dependency trees from input dialogue acts, and we use it to
directly compare two-step generation with separate sentence planning and
surface realization stages to a joint, one-step approach. We were able to train
both setups successfully using very little training data. The joint setup
offers better performance, surpassing state-of-the-art with regards to
n-gram-based scores while providing more relevant outputs.
Explore First, Exploit Next: The True Shape of Regret in Bandit Problems
Authors: Aurélien Garivier, Pierre Ménard, Gilles Stoltz
Date: 2016-02-23 00:00:00

We revisit lower bounds on the regret in the case of multi-armed bandit
problems. We obtain non-asymptotic, distribution-dependent bounds and provide
straightforward proofs based only on well-known properties of Kullback-Leibler
divergences. These bounds show in particular that in an initial phase the
regret grows almost linearly, and that the well-known logarithmic growth of the
regret only holds in a final phase. The proof techniques come to the essence of
the information-theoretic arguments used and they are deprived of all
unnecessary complications.
Most central or least central? How much modeling decisions influence a node's centrality ranking in multiplex networks
Authors: Sude Tavassoli, Katharina Anna Zweig
Date: 2016-06-17 00:00:00
Tasks: Sensitivity

To understand a node's centrality in a multiplex network, its centrality
values in all the layers of the network can be aggregated. This requires a
normalization of the values, to allow their meaningful comparison and
aggregation over networks with different sizes and orders. The concrete choices
of such preprocessing steps like normalization and aggregation are almost never
discussed in network analytic papers. In this paper, we show that even sticking
to the most simple centrality index (the degree) but using different, classic
choices of normalization and aggregation strategies, can turn a node from being
among the most central to being among the least central. We present our results
by using an aggregation operator which scales between different, classic
aggregation strategies based on three multiplex networks. We also introduce a
new visualization and characterization of a node's sensitivity to the choice of
a normalization and aggregation strategy in multiplex networks. The observed
high sensitivity of single nodes to the specific choice of aggregation and
normalization strategies is of strong importance, especially for all kinds of
intelligence-analytic software as it questions the interpretations of the
findings.
Abducing Compliance of Incomplete Event Logs
Authors: Federico Chesani, Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Paola Mello, Marco Montali, Sergio Tessaris
Date: 2016-06-17 00:00:00

The capability to store data about business processes execution in so-called
Event Logs has brought to the diffusion of tools for the analysis of process
executions and for the assessment of the goodness of a process model.
Nonetheless, these tools are often very rigid in dealing with with Event Logs
that include incomplete information about the process execution. Thus, while
the ability of handling incomplete event data is one of the challenges
mentioned in the process mining manifesto, the evaluation of compliance of an
execution trace still requires an end-to-end complete trace to be performed.
  This paper exploits the power of abduction to provide a flexible, yet
computationally effective, framework to deal with different forms of
incompleteness in an Event Log. Moreover it proposes a refinement of the
classical notion of compliance into strong and conditional compliance to take
into account incomplete logs. Finally, performances evaluation in an
experimental setting shows the feasibility of the presented approach.
Approachability in unknown games: Online learning meets multi-objective optimization
Authors: Shie Mannor, Vianney Perchet, Gilles Stoltz
Date: 2014-02-10 00:00:00

In the standard setting of approachability there are two players and a target
set. The players play repeatedly a known vector-valued game where the first
player wants to have the average vector-valued payoff converge to the target
set which the other player tries to exclude it from this set. We revisit this
setting in the spirit of online learning and do not assume that the first
player knows the game structure: she receives an arbitrary vector-valued reward
vector at every round. She wishes to approach the smallest ("best") possible
set given the observed average payoffs in hindsight. This extension of the
standard setting has implications even when the original target set is not
approachable and when it is not obvious which expansion of it should be
approached instead. We show that it is impossible, in general, to approach the
best target set in hindsight and propose achievable though ambitious
alternative goals. We further propose a concrete strategy to approach these
goals. Our method does not require projection onto a target set and amounts to
switching between scalar regret minimization algorithms that are performed in
episodes. Applications to global cost minimization and to approachability under
sample path constraints are considered.
Proceedings First International Workshop on Hammers for Type Theories
Authors: Jasmin Christian Blanchette, Cezary Kaliszyk
Date: 2016-06-17 00:00:00
Tasks: Vocal Bursts Type Prediction

This volume of EPTCS contains the proceedings of the First Workshop on
Hammers for Type Theories (HaTT 2016), held on 1 July 2016 as part of the
International Joint Conference on Automated Reasoning (IJCAR 2016) in Coimbra,
Portugal. The proceedings contain four regular papers, as well as abstracts of
the two invited talks by Pierre Corbineau (Verimag, France) and Aleksy Schubert
(University of Warsaw, Poland).
DecomposeMe: Simplifying ConvNets for End-to-End Learning
Authors: Jose Alvarez, Lars Petersson
Date: 2016-06-17 00:00:00

Deep learning and convolutional neural networks (ConvNets) have been
successfully applied to most relevant tasks in the computer vision community.
However, these networks are computationally demanding and not suitable for
embedded devices where memory and time consumption are relevant.
  In this paper, we propose DecomposeMe, a simple but effective technique to
learn features using 1D convolutions. The proposed architecture enables both
simplicity and filter sharing leading to increased learning capacity. A
comprehensive set of large-scale experiments on ImageNet and Places2
demonstrates the ability of our method to improve performance while
significantly reducing the number of parameters required. Notably, on Places2,
we obtain an improvement in relative top-1 classification accuracy of 7.7\%
with an architecture that requires 92% fewer parameters compared to VGG-B. The
proposed network is also demonstrated to generalize to other tasks by
converting existing networks.
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Authors: Abhishek Das, Harsh Agrawal, C. Lawrence Zitnick, Devi Parikh, Dhruv Batra
Date: 2016-06-11 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We conduct large-scale studies on `human attention' in Visual Question
Answering (VQA) to understand where humans choose to look to answer questions
about images. We design and test multiple game-inspired novel
attention-annotation interfaces that require the subject to sharpen regions of
a blurred image to answer a question. Thus, we introduce the VQA-HAT (Human
ATtention) dataset. We evaluate attention maps generated by state-of-the-art
VQA models against human attention both qualitatively (via visualizations) and
quantitatively (via rank-order correlation). Overall, our experiments show that
current attention models in VQA do not seem to be looking at the same regions
as humans.
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Authors: Chenchen Zhu, Yutong Zheng, Khoa Luu, Marios Savvides
Date: 2016-06-17 00:00:00
Tasks: Face Detection, Face Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Pose Estimation, Region Proposal, Robust Face Recognition, Video Compression

Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection problem has been intensely studied for decades with various
commercial applications, it still meets problems in some real-world scenarios
due to numerous challenges, e.g. heavy facial occlusions, extremely low
resolutions, strong illumination, exceptionally pose variations, image or video
compression artifacts, etc. In this paper, we present a face detection approach
named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN)
to robustly solve the problems mentioned above. Similar to the region-based
CNNs, our proposed network consists of the region proposal component and the
region-of-interest (RoI) detection component. However, far apart of that
network, there are two main contributions in our proposed network that play a
significant role to achieve the state-of-the-art performance in face detection.
Firstly, the multi-scale information is grouped both in region proposal and RoI
detection to deal with tiny face regions. Secondly, our proposed network allows
explicit body contextual reasoning in the network inspired from the intuition
of human vision system. The proposed approach is benchmarked on two recent
challenging face detection databases, i.e. the WIDER FACE Dataset which
contains high degree of variability, as well as the Face Detection Dataset and
Benchmark (FDDB). The experimental results show that our proposed approach
trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE
Dataset by a large margin, and consistently achieves competitive results on
FDDB against the recent state-of-the-art face detection methods.