pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network
Authors: Lei Zhang, Peng Wang, Chunhua Shen, Lingqiao Liu, Wei Wei, Yanning Zhang, Anton Van Den Hengel
Date: 2018-06-05 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep neural networks have achieved remarkable success in single image
super-resolution (SISR). The computing and memory requirements of these methods
have hindered their application to broad classes of real devices with limited
computing power, however. One approach to this problem has been lightweight
network architectures that bal- ance the super-resolution performance and the
computation burden. In this study, we revisit this problem from an orthog- onal
view, and propose a novel learning strategy to maxi- mize the pixel-wise
fitting capacity of a given lightweight network architecture. Considering that
the initial capacity of the lightweight network is very limited, we present an
adaptive importance learning scheme for SISR that trains the network with an
easy-to-complex paradigm by dynam- ically updating the importance of image
pixels on the basis of the training loss. Specifically, we formulate the
network training and the importance learning into a joint optimization problem.
With a carefully designed importance penalty function, the importance of
individual pixels can be gradu- ally increased through solving a convex
optimization problem. The training process thus begins with pixels that are
easy to reconstruct, and gradually proceeds to more complex pixels as fitting
improves.
Multi-sensor data fusion based on a generalised belief divergence measure
Authors: Fuyuan Xiao
Date: 2018-06-05 00:00:00
Tasks: Fault Diagnosis

Multi-sensor data fusion technology plays an important role in real
applications. Because of the flexibility and effectiveness in modelling and
processing the uncertain information regardless of prior probabilities,
Dempster-Shafer evidence theory is widely applied in a variety of fields of
information fusion. However, counter-intuitive results may come out when fusing
the highly conflicting evidences. In order to deal with this problem, a novel
method for multi-sensor data fusion based on a new generalised belief
divergence measure of evidences is proposed. Firstly, the reliability weights
of evidences are determined by considering the sufficiency and importance of
the evidences. After that, on account of the reliability weights of evidences,
a new Generalised Belief Jensen-Shannon divergence (GBJS) is designed to
measure the discrepancy and conflict degree among multiple evidences, which can
be utilised to measure the support degrees of evidences. Afterwards, the
support degrees of evidences are used to adjust the bodies of the evidences
before using the Dempster's combination rule. Finally, an application in fault
diagnosis demonstrates the validity of the proposed method.
Cost-Effective Training of Deep CNNs with Active Model Adaptation
Authors: Sheng-Jun Huang, Jia-Wei Zhao, Zhao-Yang Liu
Date: 2018-02-15 00:00:00

Deep convolutional neural networks have achieved great success in various
applications. However, training an effective DNN model for a specific task is
rather challenging because it requires a prior knowledge or experience to
design the network architecture, repeated trial-and-error process to tune the
parameters, and a large set of labeled data to train the model. In this paper,
we propose to overcome these challenges by actively adapting a pre-trained
model to a new task with less labeled examples. Specifically, the pre-trained
model is iteratively fine tuned based on the most useful examples. The examples
are actively selected based on a novel criterion, which jointly estimates the
potential contribution of an instance on optimizing the feature representation
as well as improving the classification model for the target task. On one hand,
the pre-trained model brings plentiful information from its original task,
avoiding redesign of the network architecture or training from scratch; and on
the other hand, the labeling cost can be significantly reduced by active label
querying. Experiments on multiple datasets and different pre-trained models
demonstrate that the proposed approach can achieve cost-effective training of
DNNs.
A Visual Quality Index for Fuzzy C-Means
Authors: Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont
Date: 2018-06-05 00:00:00
Tasks: Clustering, Text Categorization

Cluster analysis is widely used in the areas of machine learning and data
mining. Fuzzy clustering is a particular method that considers that a data
point can belong to more than one cluster. Fuzzy clustering helps obtain
flexible clusters, as needed in such applications as text categorization. The
performance of a clustering algorithm critically depends on the number of
clusters, and estimating the optimal number of clusters is a challenging task.
Quality indices help estimate the optimal number of clusters. However, there is
no quality index that can obtain an accurate number of clusters for different
datasets. Thence, in this paper, we propose a new cluster quality index
associated with a visual, graph-based solution that helps choose the optimal
number of clusters in fuzzy partitions. Moreover, we validate our theoretical
results through extensive comparison experiments against state-of-the-art
quality indices on a variety of numerical real-world and artificial datasets.
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare
Authors: Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang
Date: 2018-06-05 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN).
TS-Net: Combining modality specific and common features for multimodal patch matching
Authors: Sovann En, Alexis Lechervy, Frédéric Jurie
Date: 2018-06-05 00:00:00
Tasks: Multimodal Patch Matching, Patch Matching

Multimodal patch matching addresses the problem of finding the
correspondences between image patches from two different modalities, e.g. RGB
vs sketch or RGB vs near-infrared. The comparison of patches of different
modalities can be done by discovering the information common to both modalities
(Siamese like approaches) or the modality-specific information (Pseudo-Siamese
like approaches). We observed that none of these two scenarios is optimal. This
motivates us to propose a three-stream architecture, dubbed as TS-Net,
combining the benefits of the two. In addition, we show that adding extra
constraints in the intermediate layers of such networks further boosts the
performance. Experimentations on three multimodal datasets show significant
performance gains in comparison with Siamese and Pseudo-Siamese networks.
Sensitivity Analysis for Mirror-Stratifiable Convex Functions
Authors: Jalal Fadili, Jérôme Malick, Gabriel Peyré
Date: 2017-07-11 00:00:00
Tasks: Sensitivity

This paper provides a set of sensitivity analysis and activity identification
results for a class of convex functions with a strong geometric structure, that
we coined "mirror-stratifiable". These functions are such that there is a
bijection between a primal and a dual stratification of the space into
partitioning sets, called strata. This pairing is crucial to track the strata
that are identifiable by solutions of parametrized optimization problems or by
iterates of optimization algorithms. This class of functions encompasses all
regularizers routinely used in signal and image processing, machine learning,
and statistics. We show that this "mirror-stratifiable" structure enjoys a nice
sensitivity theory, allowing us to study stability of solutions of optimization
problems to small perturbations, as well as activity identification of
first-order proximal splitting-type algorithms. Existing results in the
literature typically assume that, under a non-degeneracy condition, the active
set associated to a minimizer is stable to small perturbations and is
identified in finite time by optimization schemes. In contrast, our results do
not require any non-degeneracy assumption: in consequence, the optimal active
set is not necessarily stable anymore, but we are able to track precisely the
set of identifiable strata.We show that these results have crucial implications
when solving challenging ill-posed inverse problems via regularization, a
typical scenario where the non-degeneracy condition is not fulfilled. Our
theoretical results, illustrated by numerical simulations, allow to
characterize the instability behaviour of the regularized solutions, by
locating the set of all low-dimensional strata that can be potentially
identified by these solutions.
Semi-Supervised Clustering with Neural Networks
Authors: Ankita Shukla, Gullal Singh Cheema, Saket Anand
Date: 2018-06-05 00:00:00
Tasks: Clustering, Deep Clustering, Semantic Similarity, Semantic Textual Similarity

Clustering using neural networks has recently demonstrated promising
performance in machine learning and computer vision applications. However, the
performance of current approaches is limited either by unsupervised learning or
their dependence on large set of labeled data samples. In this paper, we
propose ClusterNet that uses pairwise semantic constraints from very few
labeled data samples (<5% of total data) and exploits the abundant unlabeled
data to drive the clustering approach. We define a new loss function that uses
pairwise semantic similarity between objects combined with constrained k-means
clustering to efficiently utilize both labeled and unlabeled data in the same
framework. The proposed network uses convolution autoencoder to learn a latent
representation that groups data into k specified clusters, while also learning
the cluster centers simultaneously. We evaluate and compare the performance of
ClusterNet on several datasets and state of the art deep clustering approaches.
Combining Multiple Algorithms in Classifier Ensembles using Generalized Mixture Functions
Authors: Valdigleis S. Costaa, Antonio Diego S. Farias, Benjamín Bedregal, Regivan H. N. Santiago, Anne Magaly de P. Canuto
Date: 2018-06-05 00:00:00
Tasks: Classification, General Classification

Classifier ensembles are pattern recognition structures composed of a set of
classification algorithms (members), organized in a parallel way, and a
combination method with the aim of increasing the classification accuracy of a
classification system. In this study, we investigate the application of a
generalized mixture (GM) functions as a new approach for providing an efficient
combination procedure for these systems through the use of dynamic weights in
the combination process. Therefore, we present three GM functions to be applied
as a combination method. The main advantage of these functions is that they can
define dynamic weights at the member outputs, making the combination process
more efficient. In order to evaluate the feasibility of the proposed approach,
an empirical analysis is conducted, applying classifier ensembles to 25
different classification data sets. In this analysis, we compare the use of the
proposed approaches to ensembles using traditional combination methods as well
as the state-of-the-art ensemble methods. Our findings indicated gains in terms
of performance when comparing the proposed approaches to the traditional ones
as well as comparable results with the state-of-the-art methods.
BAGAN: Data Augmentation with Balancing GAN
Authors: Giovanni Mariani, Florian Scheidegger, Roxana Istrate, Costas Bekas, Cristiano Malossi
Date: 2018-03-26 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification

Image classification datasets are often imbalanced, characteristic that
negatively affects the accuracy of deep-learning classifiers. In this work we
propose balancing GAN (BAGAN) as an augmentation tool to restore balance in
imbalanced datasets. This is challenging because the few minority-class images
may not be enough to train a GAN. We overcome this issue by including during
the adversarial training all available images of majority and minority classes.
The generative model learns useful features from majority classes and uses
these to generate images for minority classes. We apply class conditioning in
the latent space to drive the generation process towards a target class. The
generator in the GAN is initialized with the encoder module of an autoencoder
that enables us to learn an accurate class-conditioning in the latent space. We
compare the proposed methodology with state-of-the-art GANs and demonstrate
that BAGAN generates images of superior quality when trained with an imbalanced
dataset.
Deep Mixture of Experts via Shallow Embedding
Authors: Xin Wang, Fisher Yu, Lisa Dunlap, Yi-An Ma, Ruth Wang, Azalia Mirhoseini, Trevor Darrell, Joseph E. Gonzalez
Date: 2018-06-05 00:00:00
Tasks: Few-Shot Learning, Meta-Learning, Mixture-of-Experts, Reinforcement Learning, Zero-Shot Learning

Larger networks generally have greater representational power at the cost of
increased computational complexity. Sparsifying such networks has been an
active area of research but has been generally limited to static regularization
or dynamic approaches using reinforcement learning. We explore a mixture of
experts (MoE) approach to deep dynamic routing, which activates certain experts
in the network on a per-example basis. Our novel DeepMoE architecture increases
the representational power of standard convolutional networks by adaptively
sparsifying and recalibrating channel-wise features in each convolutional
layer. We employ a multi-headed sparse gating network to determine the
selection and scaling of channels for each input, leveraging exponential
combinations of experts within a single convolutional network. Our proposed
architecture is evaluated on four benchmark datasets and tasks, and we show
that Deep-MoEs are able to achieve higher accuracy with lower computation than
standard convolutional networks.
The universal approximation power of finite-width deep ReLU networks
Authors: Dmytro Perekrestenko, Philipp Grohs, Dennis Elbrächter, Helmut Bölcskei
Date: 2018-06-05 00:00:00
Proceeding: ICLR 2019 5

We show that finite-width deep ReLU neural networks yield rate-distortion
optimal approximation (B\"olcskei et al., 2018) of polynomials, windowed
sinusoidal functions, one-dimensional oscillatory textures, and the Weierstrass
function, a fractal function which is continuous but nowhere differentiable.
Together with their recently established universal approximation property of
affine function systems (B\"olcskei et al., 2018), this shows that deep neural
networks approximate vastly different signal structures generated by the affine
group, the Weyl-Heisenberg group, or through warping, and even certain
fractals, all with approximation error decaying exponentially in the number of
neurons. We also prove that in the approximation of sufficiently smooth
functions finite-width deep networks require strictly smaller connectivity than
finite-depth wide networks.
Leolani: a reference machine with a theory of mind for social communication
Authors: Piek Vossen, Selene Baez, Lenka Bajčetić, Bram Kraaijeveld
Date: 2018-06-05 00:00:00
Tasks: 10-shot image generation, 16k, 2D Cyclist Detection, 2D Human Pose Estimation, 2D Object Detection, 3D Action Recognition

Our state of mind is based on experiences and what other people tell us. This
may result in conflicting information, uncertainty, and alternative facts. We
present a robot that models relativity of knowledge and perception within
social interaction following principles of the theory of mind. We utilized
vision and speech capabilities on a Pepper robot to build an interaction model
that stores the interpretations of perceptions and conversations in combination
with provenance on its sources. The robot learns directly from what people tell
it, possibly in relation to its perception. We demonstrate how the robot's
communication is driven by hunger to acquire more knowledge from and on people
and objects, to resolve uncertainties and conflicts, and to share awareness of
the per- ceived environment. Likewise, the robot can make reference to the
world and its knowledge about the world and the encounters with people that
yielded this knowledge.
Construction of all-in-focus images assisted by depth sensing
Authors: Hang Liu, Hengyu Li, Jun Luo, Shaorong Xie, Yu Sun
Date: 2018-06-05 00:00:00
Tasks: All, Multi Focus Image Fusion

Multi-focus image fusion is a technique for obtaining an all-in-focus image
in which all objects are in focus to extend the limited depth of field (DoF) of
an imaging system. Different from traditional RGB-based methods, this paper
presents a new multi-focus image fusion method assisted by depth sensing. In
this work, a depth sensor is used together with a color camera to capture
images of a scene. A graph-based segmentation algorithm is used to segment the
depth map from the depth sensor, and the segmented regions are used to guide a
focus algorithm to locate in-focus image blocks from among multi-focus source
images to construct the reference all-in-focus image. Five test scenes and six
evaluation metrics were used to compare the proposed method and representative
state-of-the-art algorithms. Experimental results quantitatively demonstrate
that this method outperforms existing methods in both speed and quality (in
terms of comprehensive fusion metrics). The generated images can potentially be
used as reference all-in-focus images.
Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
Authors: Fariz Ikhwantri, Samuel Louvan, Kemal Kurniawan, Bagas Abisena, Valdi Rachman, Alfan Farizki Wicaksono, Rahmad Mahendra
Date: 2018-06-05 00:00:00
Proceeding: WS 2018 7
Tasks: Active Learning, Multi-Task Learning, Semantic Role Labeling

Most Semantic Role Labeling (SRL) approaches are supervised methods which
require a significant amount of annotated corpus, and the annotation requires
linguistic expertise. In this paper, we propose a Multi-Task Active Learning
framework for Semantic Role Labeling with Entity Recognition (ER) as the
auxiliary task to alleviate the need for extensive data and use additional
information from ER to help SRL. We evaluate our approach on Indonesian
conversational dataset. Our experiments show that multi-task active learning
can outperform single-task active learning method and standard multi-task
learning. According to our results, active learning is more efficient by using
12% less of training data compared to passive learning in both single-task and
multi-task setting. We also introduce a new dataset for SRL in Indonesian
conversational domain to encourage further research in this area.
Text to brain: predicting the spatial distribution of neuroimaging observations from text reports
Authors: Jérôme Dockès, Demian Wassermann, Russell Poldrack, Fabian Suchanek, Bertrand Thirion, Gaël Varoquaux
Date: 2018-06-04 00:00:00

Despite the digital nature of magnetic resonance imaging, the resulting
observations are most frequently reported and stored in text documents. There
is a trove of information untapped in medical health records, case reports, and
medical publications. In this paper, we propose to mine brain medical
publications to learn the spatial distribution associated with anatomical
terms. The problem is formulated in terms of minimization of a risk on
distributions which leads to a least-deviation cost function. An efficient
algorithm in the dual then learns the mapping from documents to brain
structures. Empirical results using coordinates extracted from the
brain-imaging literature show that i) models must adapt to semantic variation
in the terms used to describe a given anatomical structure, ii) voxel-wise
parameterization leads to higher likelihood of locations reported in unseen
documents, iii) least-deviation cost outperforms least-square. As a proof of
concept for our method, we use our model of spatial distributions to predict
the distribution of specific neurological conditions from text-only reports.
DeepDTA: Deep Drug-Target Binding Affinity Prediction
Authors: Hakime Öztürk, Elif Ozkirimli, Arzucan Özgür
Date: 2018-01-30 00:00:00
Tasks: Binary Classification, Drug Discovery, Prediction

The identification of novel drug-target (DT) interactions is a substantial
part of the drug discovery process. Most of the computational methods that have
been proposed to predict DT interactions have focused on binary classification,
where the goal is to determine whether a DT pair interacts or not. However,
protein-ligand interactions assume a continuum of binding strength values, also
called binding affinity and predicting this value still remains a challenge.
The increase in the affinity data available in DT knowledge-bases allows the
use of advanced learning techniques such as deep learning architectures in the
prediction of binding affinities. In this study, we propose a deep-learning
based model that uses only sequence information of both targets and drugs to
predict DT interaction binding affinities. The few studies that focus on DT
binding affinity prediction use either 3D structures of protein-ligand
complexes or 2D features of compounds. One novel approach used in this work is
the modeling of protein sequences and compound 1D representations with
convolutional neural networks (CNNs). The results show that the proposed deep
learning based model that uses the 1D representations of targets and drugs is
an effective approach for drug target binding affinity prediction. The model in
which high-level representations of a drug and a target are constructed via
CNNs achieved the best Concordance Index (CI) performance in one of our larger
benchmark data sets, outperforming the KronRLS algorithm and SimBoost, a
state-of-the-art method for DT binding affinity prediction.
Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory
Authors: Lei Huang, Guang-jie Ren, Shun Jiang, Raphael Arar, Eric Young Liu
Date: 2018-06-05 00:00:00

Business Architecture (BA) plays a significant role in helping organizations
understand enterprise structures and processes, and align them with strategic
objectives. However, traditional BAs are represented in fixed structure with
static model elements and fail to dynamically capture business insights based
on internal and external data. To solve this problem, this paper introduces the
graph theory into BAs with aim of building extensible data-driven analytics and
automatically generating business insights. We use IBM's Component Business
Model (CBM) as an example to illustrate various ways in which graph theory can
be leveraged for data-driven analytics, including what and how business
insights can be obtained. Future directions for applying graph theory to
business architecture analytics are discussed.