pwc-archive/papers-with-abstracts huggingface dataset:
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Authors: Hugo Jair Escalante, Heysem Kaya, Albert Ali Salah, Sergio Escalera, Yagmur Gucluturk, Umut Guclu, Xavier Baro, Isabelle Guyon, Julio Jacques Junior, Meysam Madadi, Stephane Ayache, Evelyne Viegas, Furkan Gurpinar, Achmadnoer Sukma Wicaksana, Cynthia C. S. Liem, Marcel A. J. van Gerven, Rob van Lier
Date: 2018-02-02 00:00:00

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.
Finding Better Active Learners for Faster Literature Reviews
Authors: Zhe Yu, Nicholas A. Kraft, Tim Menzies
Date: 2016-12-10 00:00:00
Tasks: Active Learning, Systematic Literature Review

Literature reviews can be time-consuming and tedious to complete. By
cataloging and refactoring three state-of-the-art active learning techniques
from evidence-based medicine and legal electronic discovery, this paper finds
and implements FASTREAD, a faster technique for studying a large corpus of
documents. This paper assesses FASTREAD using datasets generated from existing
SE literature reviews (Hall, Wahono, Radjenovi\'c, Kitchenham et al.). Compared
to manual methods, FASTREAD lets researchers find 95% relevant studies after
reviewing an order of magnitude fewer papers. Compared to other
state-of-the-art automatic methods, FASTREAD reviews 20-50% fewer studies while
finding same number of relevant primary studies in a systematic literature
review.
Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration
Authors: Per Kristian Lehre, Phan Trung Hai Nguyen
Date: 2018-02-02 00:00:00
Tasks: Evolutionary Algorithms

Unlike traditional evolutionary algorithms which produce offspring via
genetic operators, Estimation of Distribution Algorithms (EDAs) sample
solutions from probabilistic models which are learned from selected
individuals. It is hoped that EDAs may improve optimisation performance on
epistatic fitness landscapes by learning variable interactions. However, hardly
any rigorous results are available to support claims about the performance of
EDAs, even for fitness functions without epistasis. The expected runtime of the
Univariate Marginal Distribution Algorithm (UMDA) on OneMax was recently shown
to be in $\mathcal{O}\left(n\lambda\log \lambda\right)$ by Dang and Lehre
(GECCO 2015). Later, Krejca and Witt (FOGA 2017) proved the lower bound
$\Omega\left(\lambda\sqrt{n}+n\log n\right)$ via an involved drift analysis.
  We prove a $\mathcal{O}\left(n\lambda\right)$ bound, given some restrictions
on the population size. This implies the tight bound $\Theta\left(n\log
n\right)$ when $\lambda=\mathcal{O}\left(\log n\right)$, matching the runtime
of classical EAs. Our analysis uses the level-based theorem and
anti-concentration properties of the Poisson-Binomial distribution. We expect
that these generic methods will facilitate further analysis of EDAs.
The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval
Authors: Spencer Cappallo, Stacey Svetlichnaya, Pierre Garrigues, Thomas Mensink, Cees G. M. Snoek
Date: 2018-01-30 00:00:00
Tasks: Retrieval

Over the past decade, emoji have emerged as a new and widespread form of
digital communication, spanning diverse social networks and spoken languages.
We propose to treat these ideograms as a new modality in their own right,
distinct in their semantic structure from both the text in which they are often
embedded as well as the images which they resemble. As a new modality, emoji
present rich novel possibilities for representation and interaction. In this
paper, we explore the challenges that arise naturally from considering the
emoji modality through the lens of multimedia research. Specifically, the ways
in which emoji can be related to other common modalities such as text and
images. To do so, we first present a large scale dataset of real-world emoji
usage collected from Twitter. This dataset contains examples of both text-emoji
and image-emoji relationships. We present baseline results on the challenge of
predicting emoji from both text and images, using state-of-the-art neural
networks. Further, we offer a first consideration into the problem of how to
account for new, unseen emoji - a relevant issue as the emoji vocabulary
continues to expand on a yearly basis. Finally, we present results for
multimedia retrieval using emoji as queries.
Selecting the best system and multi-armed bandits
Authors: Peter Glynn, Sandeep Juneja
Date: 2015-07-16 00:00:00
Tasks: Multi-Armed Bandits

Consider the problem of finding a population or a probability distribution
amongst many with the largest mean when these means are unknown but population
samples can be simulated or otherwise generated. Typically, by selecting
largest sample mean population, it can be shown that false selection
probability decays at an exponential rate. Lately, researchers have sought
algorithms that guarantee that this probability is restricted to a small
$\delta$ in order $\log(1/\delta)$ computational time by estimating the
associated large deviations rate function via simulation. We show that such
guarantees are misleading when populations have unbounded support even when
these may be light-tailed. Specifically, we show that any policy that
identifies the correct population with probability at least $1-\delta$ for each
problem instance requires infinite number of samples in expectation in making
such a determination in any problem instance. This suggests that some
restrictions are essential on populations to devise $O(\log(1/\delta))$
algorithms with $1 - \delta$ correctness guarantees. We note that under
restriction on population moments, such methods are easily designed, and that
sequential methods from stochastic multi-armed bandit literature can be adapted
to devise such algorithms.
Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
Authors: Filip Klubička, Antonio Toral, Víctor M. Sánchez-Cartagena
Date: 2018-02-02 00:00:00
Tasks: Machine Translation, Sentence, Translation

This paper presents a quantitative fine-grained manual evaluation approach to
comparing the performance of different machine translation (MT) systems. We
build upon the well-established Multidimensional Quality Metrics (MQM) error
taxonomy and implement a novel method that assesses whether the differences in
performance for MQM error types between different MT systems are statistically
significant. We conduct a case study for English-to-Croatian, a language
direction that involves translating into a morphologically rich language, for
which we compare three MT systems belonging to different paradigms: pure
phrase-based, factored phrase-based and neural. First, we design an
MQM-compliant error taxonomy tailored to the relevant linguistic phenomena of
Slavic languages, which made the annotation process feasible and accurate.
Errors in MT outputs were then annotated by two annotators following this
taxonomy. Subsequently, we carried out a statistical analysis which showed that
the best-performing system (neural) reduces the errors produced by the worst
system (pure phrase-based) by more than half (54\%). Moreover, we conducted an
additional analysis of agreement errors in which we distinguished between short
(phrase-level) and long distance (sentence-level) errors. We discovered that
phrase-based MT approaches are of limited use for long distance agreement
phenomena, for which neural MT was found to be especially effective.
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
Authors: Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, Finale Doshi-Velez
Date: 2018-02-02 00:00:00
Tasks: BIG-bench Machine Learning

Recent years have seen a boom in interest in machine learning systems that
can provide a human-understandable rationale for their predictions or
decisions. However, exactly what kinds of explanation are truly
human-interpretable remains poorly understood. This work advances our
understanding of what makes explanations interpretable in the specific context
of verification. Suppose we have a machine learning system that predicts X, and
we provide rationale for this prediction X. Given an input, an explanation, and
an output, is the output consistent with the input and the supposed rationale?
Via a series of user-studies, we identify what kinds of increases in complexity
have the greatest effect on the time it takes for humans to verify the
rationale, and which seem relatively insensitive.
A Generative Model for Natural Sounds Based on Latent Force Modelling
Authors: William J. Wilkinson, Joshua D. Reiss, Dan Stowell
Date: 2018-02-02 00:00:00

Recent advances in analysis of subband amplitude envelopes of natural sounds
have resulted in convincing synthesis, showing subband amplitudes to be a
crucial component of perception. Probabilistic latent variable analysis is
particularly revealing, but existing approaches don't incorporate prior
knowledge about the physical behaviour of amplitude envelopes, such as
exponential decay and feedback. We use latent force modelling, a probabilistic
learning paradigm that incorporates physical knowledge into Gaussian process
regression, to model correlation across spectral subband envelopes. We augment
the standard latent force model approach by explicitly modelling correlations
over multiple time steps. Incorporating this prior knowledge strengthens the
interpretation of the latent functions as the source that generated the signal.
We examine this interpretation via an experiment which shows that sounds
generated by sampling from our probabilistic model are perceived to be more
realistic than those generated by similar models based on nonnegative matrix
factorisation, even in cases where our model is outperformed from a
reconstruction error perspective.
Representation Learning for Resource Usage Prediction
Authors: Florian Schmidt, Mathias Niepert, Felipe Huici
Date: 2018-02-02 00:00:00
Tasks: Anomaly Detection, Prediction, Representation Learning

Creating a model of a computer system that can be used for tasks such as predicting future resource usage and detecting anomalies is a challenging problem. Most current systems rely on heuristics and overly simplistic assumptions about the workloads and system statistics. These heuristics are typically a one-size-fits-all solution so as to be applicable in a wide range of applications and systems environments. With this paper, we present our ongoing work of integrating systems telemetry ranging from standard resource usage statistics to kernel and library calls of applications into a machine learning model. Intuitively, such a ML model approximates, at any point in time, the state of a system and allows us to solve tasks such as resource usage prediction and anomaly detection. To achieve this goal, we leverage readily-available information that does not require any changes to the applications run on the system. We train recurrent neural networks to learn a model of the system under consideration. As a proof of concept, we train models specifically to predict future resource usage of running applications.
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
Authors: Saikat Roy, Nibaran Das, Mahantapas Kundu, Mita Nasipuri
Date: 2018-02-02 00:00:00

In this work, a novel deep learning technique for the recognition of
handwritten Bangla isolated compound character is presented and a new benchmark
of recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy
layer wise training of Deep Neural Network has helped to make significant
strides in various pattern recognition problems. We employ layerwise training
to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and
augment the training process with the RMSProp algorithm to achieve faster
convergence. We compare results with those obtained from standard shallow
learning methods with predefined features, as well as standard DCNNs.
Supervised layerwise trained DCNNs are found to outperform standard shallow
learning models such as Support Vector Machines as well as regular DCNNs of
similar architecture by achieving error rate of 9.67% thereby setting a new
benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%,
representing an improvement of nearly 10%.
Convolutional neural network-based regression for depth prediction in digital holography
Authors: Tomoyoshi Shimobaba, Takashi Kakue, Tomoyoshi Ito
Date: 2018-02-02 00:00:00
Tasks: Depth Estimation, Depth Prediction, Object, Position, regression

Digital holography enables us to reconstruct objects in three-dimensional
space from holograms captured by an imaging device. For the reconstruction, we
need to know the depth position of the recoded object in advance. In this
study, we propose depth prediction using convolutional neural network
(CNN)-based regression. In the previous researches, the depth of an object was
estimated through reconstructed images at different depth positions from a
hologram using a certain metric that indicates the most focused depth position;
however, such a depth search is time-consuming. The CNN of the proposed method
can directly predict the depth position with millimeter precision from
holograms.
Learning Discrete Weights Using the Local Reparameterization Trick
Authors: Oran Shayer, Dan Levi, Ethan Fetaya
Date: 2017-10-21 00:00:00
Proceeding: ICLR 2018 1

Recent breakthroughs in computer vision make use of large deep neural
networks, utilizing the substantial speedup offered by GPUs. For applications
running on limited hardware, however, high precision real-time processing can
still be a challenge. One approach to solving this problem is training networks
with binary or ternary weights, thus removing the need to calculate
multiplications and significantly reducing memory size. In this work, we
introduce LR-nets (Local reparameterization networks), a new method for
training neural networks with discrete weights using stochastic parameters. We
show how a simple modification to the local reparameterization trick,
previously used to train Gaussian distributed weights, enables the training of
discrete weights. Using the proposed training we test both binary and ternary
models on MNIST, CIFAR-10 and ImageNet benchmarks and reach state-of-the-art
results on most experiments.
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
Authors: Moritz Einfalt, Dan Zecha, Rainer Lienhart
Date: 2018-02-02 00:00:00
Tasks: Pose Estimation

In this paper we consider the problem of human pose estimation in real-world
videos of swimmers. Swimming channels allow filming swimmers simultaneously
above and below the water surface with a single stationary camera. These
recordings can be used to quantitatively assess the athletes' performance. The
quantitative evaluation, so far, requires manual annotations of body parts in
each video frame. We therefore apply the concept of CNNs in order to
automatically infer the required pose information. Starting with an
off-the-shelf architecture, we develop extensions to leverage activity
information - in our case the swimming style of an athlete - and the continuous
nature of the video recordings. Our main contributions are threefold: (a) We
apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a
baseline in our very challenging aquatic environment and discuss its error
modes, (b) we propose an extension to input swimming style information into the
fully convolutional architecture and (c) modify the architecture for continuous
pose estimation in videos. With these additions we achieve reliable pose
estimates with up to +16% more correct body joint detections compared to the
baseline architecture.
When can $l_p$-norm objective functions be minimized via graph cuts?
Authors: Filip Malmberg, Robin Strand
Date: 2018-02-02 00:00:00
Tasks: Combinatorial Optimization

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular. This can be interpreted as minimizing the $l_1$-norm of the vector containing all pairwise and unary terms. By raising each term to a power $p$, the same technique can also be used to minimize the $l_p$-norm of the vector. Unfortunately, the submodularity of an $l_1$-norm objective function does not guarantee the submodularity of the corresponding $l_p$-norm objective function. The contribution of this paper is to provide useful conditions under which an $l_p$-norm objective function is submodular for all $p\geq 1$, thereby identifying a large class of $l_p$-norm objective functions that can be minimized via minimal graph cuts.
Semantic Interpolation in Implicit Models
Authors: Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1

In implicit models, one often interpolates between sampled points in latent
space. As we show in this paper, care needs to be taken to match-up the
distributional assumptions on code vectors with the geometry of the
interpolating paths. Otherwise, typical assumptions about the quality and
semantics of in-between points may not be justified. Based on our analysis we
propose to modify the prior code distribution to put significantly more
probability mass closer to the origin. As a result, linear interpolation paths
are not only shortest paths, but they are also guaranteed to pass through
high-density regions, irrespective of the dimensionality of the latent space.
Experiments on standard benchmark image datasets demonstrate clear visual
improvements in the quality of the generated samples and exhibit more
meaningful interpolation paths.
A Quantum Implementation Model for Artificial Neural Networks
Authors: Ammar Daskin
Date: 2016-09-19 00:00:00
Tasks: model

The learning process for multi layered neural networks with many nodes makes
heavy demands on computational resources. In some neural network models, the
learning formulas, such as the Widrow-Hoff formula, do not change the
eigenvectors of the weight matrix while flatting the eigenvalues. In infinity,
this iterative formulas result in terms formed by the principal components of
the weight matrix: i.e., the eigenvectors corresponding to the non-zero
eigenvalues. In quantum computing, the phase estimation algorithm is known to
provide speed-ups over the conventional algorithms for the eigenvalue-related
problems. Combining the quantum amplitude amplification with the phase
estimation algorithm, a quantum implementation model for artificial neural
networks using the Widrow-Hoff learning rule is presented. The complexity of
the model is found to be linear in the size of the weight matrix. This provides
a quadratic improvement over the classical algorithms.
Domain Adaptation with L2 constraints for classifying images from different endoscope systems
Authors: Toru Tamaki, Shoji Sonoyama, Takio Kurita, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka, Kazuaki Chayama
Date: 2016-11-08 00:00:00
Tasks: Domain Adaptation

This paper proposes a method for domain adaptation that extends the maximum
margin domain transfer (MMDT) proposed by Hoffman et al., by introducing L2
distance constraints between samples of different domains; thus, our method is
denoted as MMDTL2. Motivated by the differences between the images taken by
narrow band imaging (NBI) endoscopic devices, we utilize different NBI devices
as different domains and estimate the transformations between samples of
different domains, i.e., image samples taken by different NBI endoscope
systems. We first formulate the problem in the primal form, and then derive the
dual form with much lesser computational costs as compared to the naive
approach. From our experimental results using NBI image datasets from two
different NBI endoscopic devices, we find that MMDTL2 is better than MMDT and
also support vector machines without adaptation, especially when NBI image
features are high-dimensional and the per-class training samples are greater
than 20.
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks
Authors: Lijun Zhao, Huihui Bai, Feng Li, Anhong Wang, Yao Zhao
Date: 2018-02-02 00:00:00
Tasks: Image Compression, Quantization

In this paper, we propose an end-to-end mixed-resolution image compression
framework with convolutional neural networks. Firstly, given one input image,
feature description neural network (FDNN) is used to generate a new
representation of this image, so that this image representation can be more
efficiently compressed by standard codec, as compared to the input image.
Furthermore, we use post-processing neural network (PPNN) to remove the coding
artifacts caused by quantization of codec. Secondly, low-resolution image
representation is adopted for high efficiency compression in terms of most of
bit spent by image's structures under low bit-rate. However, more bits should
be assigned to image details in the high-resolution, when most of structures
have been kept after compression at the high bit-rate. This comes from a fact
that the low-resolution image representation can't burden more information than
high-resolution representation beyond a certain bit-rate. Finally, to resolve
the problem of error back-propagation from the PPNN network to the FDNN
network, we introduce to learn a virtual codec neural network to imitate two
continuous procedures of standard compression and post-processing. The
objective experimental results have demonstrated the proposed method has a
large margin improvement, when comparing with several state-of-the-art
approaches.
Augmented Space Linear Model
Authors: Zhengda Qin, Badong Chen, Nanning Zheng, Jose C. Principe
Date: 2018-02-01 00:00:00
Tasks: Computational Efficiency, model

The linear model uses the space defined by the input to project the target or
desired signal and find the optimal set of model parameters. When the problem
is nonlinear, the adaption requires nonlinear models for good performance, but
it becomes slower and more cumbersome. In this paper, we propose a linear model
called Augmented Space Linear Model (ASLM), which uses the full joint space of
input and desired signal as the projection space and approaches the performance
of nonlinear models. This new algorithm takes advantage of the linear solution,
and corrects the estimate for the current testing phase input with the error
assigned to the input space neighborhood in the training phase. This algorithm
can solve the nonlinear problem with the computational efficiency of linear
methods, which can be regarded as a trade off between accuracy and
computational complexity. Making full use of the training data, the proposed
augmented space model may provide a new way to improve many modeling tasks.