pwc-archive/papers-with-abstracts huggingface dataset:
Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets
Authors: Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera
Date: 2018-02-14 00:00:00

Multilabel classification is an emergent data mining task with a broad range
of real world applications. Learning from imbalanced multilabel data is being
deeply studied latterly, and several resampling methods have been proposed in
the literature. The unequal label distribution in most multilabel datasets,
with disparate imbalance levels, could be a handicap while learning new
classifiers. In addition, this characteristic challenges many of the existent
preprocessing algorithms. Furthermore, the concurrence between imbalanced
labels can make harder the learning from certain labels. These are what we call
\textit{difficult} labels. In this work, the problem of difficult labels is
deeply analyzed, its influence in multilabel classifiers is studied, and a
novel way to solve this problem is proposed. Specific metrics to assess this
trait in multilabel datasets, called \textit{SCUMBLE} (\textit{Score of
ConcUrrence among iMBalanced LabEls}) and \textit{SCUMBLELbl}, are presented
along with REMEDIAL (\textit{REsampling MultilabEl datasets by Decoupling
highly ImbAlanced Labels}), a new algorithm aimed to relax label concurrence.
How to deal with this problem using the R mldr package is also outlined.
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization
Authors: Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera
Date: 2018-02-14 00:00:00
Tasks: General Classification

The learning from imbalanced data is a deeply studied problem in standard
classification and, in recent times, also in multilabel classification. A
handful of multilabel resampling methods have been proposed in late years,
aiming to balance the labels distribution. However these methods have to face a
new obstacle, specific for multilabel data, as is the joint appearance of
minority and majority labels in the same data patterns. We proposed recently a
new algorithm designed to decouple imbalanced labels concurring in the same
instance, called REMEDIAL (\textit{REsampling MultilabEl datasets by Decoupling
highly ImbAlanced Labels}). The goal of this work is to propose a procedure to
hybridize this method with some of the best resampling algorithms available in
the literature, including random oversampling, heuristic undersampling and
synthetic sample generation techniques. These hybrid methods are then
empirically analyzed, determining how their behavior is influenced by the label
decoupling process. As a result, a noteworthy set of guidelines on the combined
use of these techniques can be drawn from the conducted experimentation.
Interpretable Convolutional Neural Networks
Authors: Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu
Date: 2017-10-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, single catogory classification

This paper proposes a method to modify traditional convolutional neural
networks (CNNs) into interpretable CNNs, in order to clarify knowledge
representations in high conv-layers of CNNs. In an interpretable CNN, each
filter in a high conv-layer represents a certain object part. We do not need
any annotations of object parts or textures to supervise the learning process.
Instead, the interpretable CNN automatically assigns each filter in a high
conv-layer with an object part during the learning process. Our method can be
applied to different types of CNNs with different structures. The clear
knowledge representation in an interpretable CNN can help people understand the
logics inside a CNN, i.e., based on which patterns the CNN makes the decision.
Experiments showed that filters in an interpretable CNN were more semantically
meaningful than those in traditional CNNs.
Recursive Chaining of Reversible Image-to-image Translators For Face Aging
Authors: Ari Heljakka, Arno Solin, Juho Kannala
Date: 2018-02-14 00:00:00
Tasks: Translation

This paper addresses the modeling and simulation of progressive changes over
time, such as human face aging. By treating the age phases as a sequence of
image domains, we construct a chain of transformers that map images from one
age domain to the next. Leveraging recent adversarial image translation
methods, our approach requires no training samples of the same individual at
different ages. Here, the model must be flexible enough to translate a child
face to a young adult, and all the way through the adulthood to old age. We
find that some transformers in the chain can be recursively applied on their
own output to cover multiple phases, compressing the chain. The structure of
the chain also unearths information about the underlying physical process. We
demonstrate the performance of our method with precise and intuitive metrics,
and visually match with the face aging state-of-the-art.
Distributional Term Set Expansion
Authors: Amaru Cuba Gyllensten, Magnus Sahlgren
Date: 2018-02-14 00:00:00
Proceeding: LREC 2018 5
Tasks: Active Learning, Classification, General Classification

This paper is a short empirical study of the performance of centrality and
classification based iterative term set expansion methods for distributional
semantic models. Iterative term set expansion is an interactive process using
distributional semantics models where a user labels terms as belonging to some
sought after term set, and a system uses this labeling to supply the user with
new, candidate, terms to label, trying to maximize the number of positive
examples found. While centrality based methods have a long history in term set
expansion, we compare them to classification methods based on the the Simple
Margin method, an Active Learning approach to classification using Support
Vector Machines. Examining the performance of various centrality and
classification based methods for a variety of distributional models over five
different term sets, we can show that active learning based methods
consistently outperform centrality based methods.
Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Authors: Seyyed Hossein Hasanpour, Mohammad Rouhani, Mohsen Fayyaz, Mohammad Sabokrou
Date: 2016-08-22 00:00:00
Tasks: Image Classification

Major winning Convolutional Neural Networks (CNNs), such as AlexNet, VGGNet, ResNet, GoogleNet, include tens to hundreds of millions of parameters, which impose considerable computation and memory overhead. This limits their practical use for training, optimization and memory efficiency. On the contrary, light-weight architectures, being proposed to address this issue, mainly suffer from low accuracy. These inefficiencies mostly stem from following an ad hoc procedure. We propose a simple architecture, called SimpleNet, based on a set of designing principles, with which we empirically show, a well-crafted yet simple and reasonably deep architecture can perform on par with deeper and more complex architectures. SimpleNet provides a good tradeoff between the computation/memory efficiency and the accuracy. Our simple 13-layer architecture outperforms most of the deeper and complex architectures to date such as VGGNet, ResNet, and GoogleNet on several well-known benchmarks while having 2 to 25 times fewer number of parameters and operations. This makes it very handy for embedded systems or systems with computational and memory limitations. We achieved state-of-the-art result on CIFAR10 outperforming several heavier architectures, near state of the art on MNIST and competitive results on CIFAR100 and SVHN. We also outperformed the much larger and deeper architectures such as VGGNet and popular variants of ResNets among others on the ImageNet dataset. Models are made available at: https://github.com/Coderx7/SimpleNet
Automatic Conflict Detection in Police Body-Worn Audio
Authors: Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu
Date: 2017-11-14 00:00:00

Automatic conflict detection has grown in relevance with the advent of
body-worn technology, but existing metrics such as turn-taking and overlap are
poor indicators of conflict in police-public interactions. Moreover, standard
techniques to compute them fall short when applied to such diversified and
noisy contexts. We develop a pipeline catered to this task combining adaptive
noise removal, non-speech filtering and new measures of conflict based on the
repetition and intensity of phrases in speech. We demonstrate the effectiveness
of our approach on body-worn audio data collected by the Los Angeles Police
Department.
Convolutional Neural Networks over Control Flow Graphs for Software Defect Prediction
Authors: Anh Viet Phan, Minh Le Nguyen, Lam Thu Bui
Date: 2018-02-14 00:00:00

Existing defects in software components is unavoidable and leads to not only
a waste of time and money but also many serious consequences. To build
predictive models, previous studies focus on manually extracting features or
using tree representations of programs, and exploiting different machine
learning algorithms. However, the performance of the models is not high since
the existing features and tree structures often fail to capture the semantics
of programs. To explore deeply programs' semantics, this paper proposes to
leverage precise graphs representing program execution flows, and deep neural
networks for automatically learning defect features. Firstly, control flow
graphs are constructed from the assembly instructions obtained by compiling
source code; we thereafter apply multi-view multi-layer directed graph-based
convolutional neural networks (DGCNNs) to learn semantic features. The
experiments on four real-world datasets show that our method significantly
outperforms the baselines including several other deep learning approaches.
M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance
Authors: Kunfeng Wang, Chao Gou, Fei-Yue Wang
Date: 2018-02-14 00:00:00
Tasks: Change Detection

In this paper, we propose a robust change detection method for intelligent
visual surveillance. This method, named M4CD, includes three major steps.
Firstly, a sample-based background model that integrates color and texture cues
is built and updated over time. Secondly, multiple heterogeneous features
(including brightness variation, chromaticity variation, and texture variation)
are extracted by comparing the input frame with the background model, and a
multi-source learning strategy is designed to online estimate the probability
distributions for both foreground and background. The three features are
approximately conditionally independent, making multi-source learning feasible.
Pixel-wise foreground posteriors are then estimated with Bayes rule. Finally,
the Markov random field (MRF) optimization and heuristic post-processing
techniques are used sequentially to improve accuracy. In particular, a
two-layer MRF model is constructed to represent pixel-based and
superpixel-based contextual constraints compactly. Experimental results on the
CDnet dataset indicate that M4CD is robust under complex environments and ranks
among the top methods.
Few-Example Object Detection with Model Communication
Authors: Xuanyi Dong, Liang Zheng, Fan Ma, Yi Yang, Deyu Meng
Date: 2017-06-26 00:00:00
Tasks: model, Object, object-detection, Object Detection

In this paper, we study object detection using a large pool of unlabeled
images and only a few labeled images per category, named "few-example object
detection". The key challenge consists in generating trustworthy training
samples as many as possible from the pool. Using few training examples as
seeds, our method iterates between model training and high-confidence sample
selection. In training, easy samples are generated first and, then the poorly
initialized model undergoes improvement. As the model becomes more
discriminative, challenging but reliable samples are selected. After that,
another round of model improvement takes place. To further improve the
precision and recall of the generated training samples, we embed multiple
detection models in our framework, which has proven to outperform the single
model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS
COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples
selected for each category, our method produces very competitive results when
compared to the state-of-the-art weakly-supervised approaches using a large
number of image-level labels.
Paraphrasing Complex Network: Network Compression via Factor Transfer
Authors: Jangho Kim, SeongUk Park, Nojun Kwak
Date: 2018-02-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Model Compression, Transfer Learning

Many researchers have sought ways of model compression to reduce the size of a deep neural network (DNN) with minimal performance degradation in order to use DNNs in embedded systems. Among the model compression methods, a method called knowledge transfer is to train a student network with a stronger teacher network. In this paper, we propose a novel knowledge transfer method which uses convolutional operations to paraphrase teacher's knowledge and to translate it for the student. This is done by two convolutional modules, which are called a paraphraser and a translator. The paraphraser is trained in an unsupervised manner to extract the teacher factors which are defined as paraphrased information of the teacher network. The translator located at the student network extracts the student factors and helps to translate the teacher factors by mimicking them. We observed that our student network trained with the proposed factor transfer method outperforms the ones trained with conventional knowledge transfer methods.
Make Hawkes Processes Explainable by Decomposing Self-Triggering Kernels
Authors: Rafael Lima, Jaesik Choi
Date: 2017-03-27 00:00:00

Hawkes Processes capture self-excitation and mutual-excitation between events when the arrival of an event makes future events more likely to happen. Identification of such temporal covariance can reveal the underlying structure to better predict future events. In this paper, we present a new framework to decompose discrete events with a composition of multiple self-triggering kernels. The composition scheme allows us to decompose empirical covariance densities into the sum or the product of base kernels which are easily interpretable. Here, we present the first multiplicative kernel composition methods for Hawkes Processes. We demonstrate that the new automatic kernel decomposition procedure outperforms the existing methods on the prediction of discrete events in real-world data.
Learning Task Specifications from Demonstrations
Authors: Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit A. Seshia
Date: 2017-10-11 00:00:00
Proceeding: NeurIPS 2018 12

Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks
Authors: Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, Youngjin Yoon, In So Kweon
Date: 2018-02-14 00:00:00
Tasks: Action Classification, General Classification, Multi-Task Learning, Retrieval, Sentence, Sentence Retrieval

Human behavior understanding is arguably one of the most important mid-level
components in artificial intelligence. In order to efficiently make use of
data, multi-task learning has been studied in diverse computer vision tasks
including human behavior understanding. However, multi-task learning relies on
task specific datasets and constructing such datasets can be cumbersome. It
requires huge amounts of data, labeling efforts, statistical consideration etc.
In this paper, we leverage existing single-task datasets for human action
classification and captioning data for efficient human behavior learning. Since
the data in each dataset has respective heterogeneous annotations, traditional
multi-task learning is not effective in this scenario. To this end, we propose
a novel alternating directional optimization method to efficiently learn from
the heterogeneous data. We demonstrate the effectiveness of our model and show
performance improvements on both classification and sentence retrieval tasks in
comparison to the models trained on each of the single-task datasets.
Memory Augmented Control Networks
Authors: Arbaaz Khan, Clark Zhang, Nikolay Atanasov, Konstantinos Karydis, Vijay Kumar, Daniel D. Lee
Date: 2017-09-17 00:00:00
Proceeding: ICLR 2018 1

Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments.
Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages
Authors: Gyu-Hyeon Choi, Jong-Hun Shin, Young-Kil Kim
Date: 2017-09-26 00:00:00
Proceeding: LREC 2018 5
Tasks: Machine Translation, Translation

In machine translation, we often try to collect resources to improve
performance. However, most of the language pairs, such as Korean-Arabic and
Korean-Vietnamese, do not have enough resources to train machine translation
systems. In this paper, we propose the use of synthetic methods for extending a
low-resource corpus and apply it to a multi-source neural machine translation
model. We showed the improvement of machine translation performance through
corpus extension using the synthetic method. We specifically focused on how to
create source sentences that can make better target sentences, including the
use of synthetic methods. We found that the corpus extension could also improve
the performance of multi-source neural machine translation. We showed the
corpus extension and multi-source model to be efficient methods for a
low-resource language pair. Furthermore, when both methods were used together,
we found better machine translation performance.
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
Authors: Jordan Yoder, Li Chen, Henry Pao, Eric Bridgeford, Keith Levin, Donniell Fishkind, Carey Priebe, Vince Lyzinski
Date: 2018-02-14 00:00:00
Tasks: Clustering, Stochastic Block Model

Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realized from the model and the observed block labels, the vertex nomination task is to order the vertices with unobserved block labels into a ranked nomination list with the goal of having an abundance of interesting vertices near the top of the list. There are vertex nomination schemes in the literature, including the optimally precise canonical nomination scheme~$\mathcal{L}^C$ and the consistent spectral partitioning nomination scheme~$\mathcal{L}^P$. While the canonical nomination scheme $\mathcal{L}^C$ is provably optimally precise, it is computationally intractable, being impractical to implement even on modestly sized graphs. With this in mind, an approximation of the canonical scheme---denoted the {\it canonical sampling nomination scheme} $\mathcal{L}^{CS}$---is introduced; $\mathcal{L}^{CS}$ relies on a scalable, Markov chain Monte Carlo-based approximation of $\mathcal{L}^{C}$, and converges to $\mathcal{L}^{C}$ as the amount of sampling goes to infinity. The spectral partitioning nomination scheme is also extended to the {\it extended spectral partitioning nomination scheme}, $\mathcal{L}^{EP}$, which introduces a novel semisupervised clustering framework to improve upon the precision of $\mathcal{L}^P$. Real-data and simulation experiments are employed to illustrate the precision of these vertex nomination schemes, as well as their empirical computational complexity. Keywords: vertex nomination, Markov chain Monte Carlo, spectral partitioning, Mclust MSC[2010]: 60J22, 65C40, 62H30, 62H25
Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing
Authors: Yuan Jin, Mark Carman, Ye Zhu, Wray Buntine
Date: 2018-02-12 00:00:00
Tasks: Clustering

The questions in a crowdsourcing task typically exhibit varying degrees of
difficulty and subjectivity. Their joint effects give rise to the variation in
responses to the same question by different crowd-workers. This variation is
low when the question is easy to answer and objective, and high when it is
difficult and subjective. Unfortunately, current quality control methods for
crowdsourcing consider only the question difficulty to account for the
variation. As a result,these methods cannot distinguish workers personal
preferences for different correct answers of a partially subjective question
from their ability/expertise to avoid objectively wrong answers for that
question. To address this issue, we present a probabilistic model which (i)
explicitly encodes question difficulty as a model parameter and (ii) implicitly
encodes question subjectivity via latent preference factors for crowd-workers.
We show that question subjectivity induces grouping of crowd-workers, revealed
through clustering of their latent preferences. Moreover, we develop a
quantitative measure of the subjectivity of a question. Experiments show that
our model(1) improves the performance of both quality control for crowd-sourced
answers and next answer prediction for crowd-workers,and (2) can potentially
provide coherent rankings of questions in terms of their difficulty and
subjectivity, so that task providers can refine their designs of the
crowdsourcing tasks, e.g. by removing highly subjective questions or
inappropriately difficult questions.