pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels
Authors: Kashyap Chitta, Jianwei Feng, Martial Hebert
Date: 2018-11-08 00:00:00
Tasks: Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

Training deep networks for semantic segmentation requires annotation of large
amounts of data, which can be time-consuming and expensive. Unfortunately,
these trained networks still generalize poorly when tested in domains not
consistent with the training data. In this paper, we show that by carefully
presenting a mixture of labeled source domain and proxy-labeled target domain
data to a network, we can achieve state-of-the-art unsupervised domain
adaptation results. With our design, the network progressively learns features
specific to the target domain using annotation from only the source domain. We
generate proxy labels for the target domain using the network's own
predictions. Our architecture then allows selective mining of easy samples from
this set of proxy labels, and hard samples from the annotated source domain. We
conduct a series of experiments with the GTA5, Cityscapes and BDD100k datasets
on synthetic-to-real domain adaptation and geographic domain adaptation,
showing the advantages of our method over baselines and existing approaches.
Uncovering the Social Interaction in Swarm Intelligence with Network Science
Authors: Marcos Oliveira, Diego Pinheiro, Mariana Macedo, Carmelo Bastos-Filho, Ronaldo Menezes
Date: 2018-11-08 00:00:00

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. Yet, we do not know why swarm-based algorithms work well and neither we can compare the different approaches in the literature. The lack of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here, we address this gap by introducing a network-based framework---the interaction network---to examine computational swarm-based systems via the optics of the social dynamics of such interaction network; a clear example of network science being applied to bring further clarity to a complicated field within artificial intelligence. We discuss the social interactions of four well-known swarm-based algorithms and provide an in-depth case study of the Particle Swarm Optimization. The interaction network enables researchers to study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the social interactions.
Iterative Classroom Teaching
Authors: Teresa Yeo, Parameswaran Kamalaruban, Adish Singla, Arpit Merchant, Thibault Asselborn, Louis Faucon, Pierre Dillenbourg, Volkan Cevher
Date: 2018-11-08 00:00:00
Tasks: Diversity

We consider the machine teaching problem in a classroom-like setting wherein
the teacher has to deliver the same examples to a diverse group of students.
Their diversity stems from differences in their initial internal states as well
as their learning rates. We prove that a teacher with full knowledge about the
learning dynamics of the students can teach a target concept to the entire
classroom using O(min{d,N} log(1/eps)) examples, where d is the ambient
dimension of the problem, N is the number of learners, and eps is the accuracy
parameter. We show the robustness of our teaching strategy when the teacher has
limited knowledge of the learners' internal dynamics as provided by a noisy
oracle. Further, we study the trade-off between the learners' workload and the
teacher's cost in teaching the target concept. Our experiments validate our
theoretical results and suggest that appropriately partitioning the classroom
into homogenous groups provides a balance between these two objectives.
Learning Dense Stereo Matching for Digital Surface Models from Satellite Imagery
Authors: Wayne Treible, Scott Sorensen, Andrew D. Gilliam, Chandra Kambhamettu, Joseph L. Mundy
Date: 2018-11-08 00:00:00
Tasks: Deep Learning, Self-Driving Cars, Stereo Matching, Stereo Matching Hand

Digital Surface Model generation from satellite imagery is a difficult task
that has been largely overlooked by the deep learning community. Stereo
reconstruction techniques developed for terrestrial systems including self
driving cars do not translate well to satellite imagery where image pairs vary
considerably. In this work we present neural network tailored for Digital
Surface Model generation, a ground truthing and training scheme which maximizes
available hardware, and we present a comparison to existing methods. The
resulting models are smooth, preserve boundaries, and enable further
processing. This represents one of the first attempts at leveraging deep
learning in this domain.
Scalable Robust Kidney Exchange
Authors: Duncan C. McElfresh, Hoda Bidkhori, John P. Dickerson
Date: 2018-11-08 00:00:00
Tasks: Combinatorial Optimization, Fairness, Stochastic Optimization

In barter exchanges, participants directly trade their endowed goods in a
constrained economic setting without money. Transactions in barter exchanges
are often facilitated via a central clearinghouse that must match participants
even in the face of uncertainty---over participants, existence and quality of
potential trades, and so on. Leveraging robust combinatorial optimization
techniques, we address uncertainty in kidney exchange, a real-world barter
market where patients swap (in)compatible paired donors. We provide two
scalable robust methods to handle two distinct types of uncertainty in kidney
exchange---over the quality and the existence of a potential match. The latter
case directly addresses a weakness in all stochastic-optimization-based methods
to the kidney exchange clearing problem, which all necessarily require explicit
estimates of the probability of a transaction existing---a still-unsolved
problem in this nascent market. We also propose a novel, scalable kidney
exchange formulation that eliminates the need for an exponential-time
constraint generation process in competing formulations, maintains provable
optimality, and serves as a subsolver for our robust approach. For each type of
uncertainty we demonstrate the benefits of robustness on real data from a
large, fielded kidney exchange in the United States. We conclude by drawing
parallels between robustness and notions of fairness in the kidney exchange
setting.
A Geometric Perspective on the Transferability of Adversarial Directions
Authors: Zachary Charles, Harrison Rosenberg, Dimitris Papailiopoulos
Date: 2018-11-08 00:00:00

State-of-the-art machine learning models frequently misclassify inputs that
have been perturbed in an adversarial manner. Adversarial perturbations
generated for a given input and a specific classifier often seem to be
effective on other inputs and even different classifiers. In other words,
adversarial perturbations seem to transfer between different inputs, models,
and even different neural network architectures. In this work, we show that in
the context of linear classifiers and two-layer ReLU networks, there provably
exist directions that give rise to adversarial perturbations for many
classifiers and data points simultaneously. We show that these "transferable
adversarial directions" are guaranteed to exist for linear separators of a
given set, and will exist with high probability for linear classifiers trained
on independent sets drawn from the same distribution. We extend our results to
large classes of two-layer ReLU networks. We further show that adversarial
directions for ReLU networks transfer to linear classifiers while the reverse
need not hold, suggesting that adversarial perturbations for more complex
models are more likely to transfer to other classifiers. We validate our
findings empirically, even for deeper ReLU networks.
Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition
Authors: Mubariz Zaffar, Shoaib Ehsan, Michael Milford, Klaus Mcdonald Maier
Date: 2018-11-08 00:00:00
Tasks: Visual Place Recognition

This paper presents a cognition-inspired agnostic framework for building a
map for Visual Place Recognition. This framework draws inspiration from
human-memorability, utilizes the traditional image entropy concept and computes
the static content in an image; thereby presenting a tri-folded criterion to
assess the 'memorability' of an image for visual place recognition. A dataset
namely 'ESSEX3IN1' is created, composed of highly confusing images from indoor,
outdoor and natural scenes for analysis. When used in conjunction with
state-of-the-art visual place recognition methods, the proposed framework
provides significant performance boost to these techniques, as evidenced by
results on ESSEX3IN1 and other public datasets.
Few-shot learning with attention-based sequence-to-sequence models
Authors: Bertrand Higy, Peter Bell
Date: 2018-11-08 00:00:00
Tasks: Decoder, Few-Shot Learning, speech-recognition, Speech Recognition

End-to-end approaches have recently become popular as a means of simplifying
the training and deployment of speech recognition systems. However, they often
require large amounts of data to perform well on large vocabulary tasks. With
the aim of making end-to-end approaches usable by a broader range of
researchers, we explore the potential to use end-to-end methods in small
vocabulary contexts where smaller datasets may be used. A significant drawback
of small-vocabulary systems is the difficulty of expanding the vocabulary
beyond the original training samples -- therefore we also study strategies to
extend the vocabulary with only few examples per new class (few-shot learning).
  Our results show that an attention-based encoder-decoder can be competitive
against a strong baseline on a small vocabulary keyword classification task,
reaching 97.5% of accuracy on Tensorflow's Speech Commands dataset. It also
shows promising results on the few-shot learning problem where a simple
strategy achieved 68.8\% of accuracy on new keywords with only 10 examples for
each new class. This score goes up to 88.4\% with a larger set of 100 examples.
Learning from Demonstration in the Wild
Authors: Feryal Behbahani, Kyriacos Shiarlis, Xi Chen, Vitaly Kurin, Sudhanshu Kasewa, Ciprian Stirbu, João Gomes, Supratik Paul, Frans A. Oliehoek, João Messias, Shimon Whiteson
Date: 2018-11-08 00:00:00

Learning from demonstration (LfD) is useful in settings where hand-coding
behaviour or a reward function is impractical. It has succeeded in a wide range
of problems but typically relies on manually generated demonstrations or
specially deployed sensors and has not generally been able to leverage the
copious demonstrations available in the wild: those that capture behaviours
that were occurring anyway using sensors that were already deployed for another
purpose, e.g., traffic camera footage capturing demonstrations of natural
behaviour of vehicles, cyclists, and pedestrians. We propose Video to Behaviour
(ViBe), a new approach to learn models of behaviour from unlabelled raw video
data of a traffic scene collected from a single, monocular, initially
uncalibrated camera with ordinary resolution. Our approach calibrates the
camera, detects relevant objects, tracks them through time, and uses the
resulting trajectories to perform LfD, yielding models of naturalistic
behaviour. We apply ViBe to raw videos of a traffic intersection and show that
it can learn purely from videos, without additional expert knowledge.
Deep Neural Networks for Query Expansion using Word Embeddings
Authors: Ayyoob Imani, Amir Vakili, Ali Montazer, Azadeh Shakery
Date: 2018-11-08 00:00:00
Tasks: Information Retrieval, Retrieval, Word Embeddings

Query expansion is a method for alleviating the vocabulary mismatch problem
present in information retrieval tasks. Previous works have shown that terms
selected for query expansion by traditional methods such as pseudo-relevance
feedback are not always helpful to the retrieval process. In this paper, we
show that this is also true for more recently proposed embedding-based query
expansion methods. We then introduce an artificial neural network classifier to
predict the usefulness of query expansion terms. This classifier uses term word
embeddings as inputs. We perform experiments on four TREC newswire and web
collections show that using terms selected by the classifier for expansion
significantly improves retrieval performance when compared to competitive
baselines. The results are also shown to be more robust than the baselines.
Effective Representation for Easy-First Dependency Parsing
Authors: Zuchao Li, Jiaxun Cai, Hai Zhao
Date: 2018-11-08 00:00:00
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Re-Ranking

Easy-first parsing relies on subtree re-ranking to build the complete parse tree. Whereas the intermediate state of parsing processing is represented by various subtrees, whose internal structural information is the key lead for later parsing action decisions, we explore a better representation for such subtrees. In detail, this work introduces a bottom-up subtree encoding method based on the child-sum tree-LSTM. Starting from an easy-first dependency parser without other handcraft features, we show that the effective subtree encoder does promote the parsing process, and can make a greedy search easy-first parser achieve promising results on benchmark treebanks compared to state-of-the-art baselines. Furthermore, with the help of the current pre-training language model, we further improve the state-of-the-art results of the easy-first approach.
A simple yet effective baseline for non-attributed graph classification
Authors: Chen Cai, Yusu Wang
Date: 2018-11-08 00:00:00
Tasks: Classification, Computational Efficiency, General Classification, Graph Classification, Graph Learning, Link Prediction, Representation Learning

Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on graphs in general. As the developed methodologies become more sophisticated, it is important to understand which components of the increasingly complex methods are necessary or most effective. As a first step, we develop a simple yet meaningful graph representation, and explore its effectiveness in graph classification. We test our baseline representation for the graph classification task on a range of graph datasets. Interestingly, this simple representation achieves similar performance as the state-of-the-art graph kernels and graph neural networks for non-attributed graph classification. Its performance on classifying attributed graphs is slightly weaker as it does not incorporate attributes. However, given its simplicity and efficiency, we believe that it still serves as an effective baseline for attributed graph classification. Our graph representation is efficient (linear-time) to compute. We also provide a simple connection with the graph neural networks. Note that these observations are only for the task of graph classification while existing methods are often designed for a broader scope including node embedding and link prediction. The results are also likely biased due to the limited amount of benchmark datasets available. Nevertheless, the good performance of our simple baseline calls for the development of new, more comprehensive benchmark datasets so as to better evaluate and analyze different graph learning methods. Furthermore, given the computational efficiency of our graph summary, we believe that it is a good candidate as a baseline method for future graph classification (or even other graph learning) studies.
Multi-Cycle Assignment Problems with Rotational Diversity
Authors: Helge Spieker, Arnaud Gotlieb, Morten Mossige
Date: 2018-11-08 00:00:00
Tasks: Diversity

Multi-cycle assignment problems address scenarios where a series of general assignment problems has to be solved sequentially. Subsequent cycles can differ from previous ones due to changing availability or creation of tasks and agents, which makes an upfront static schedule infeasible and introduces uncertainty in the task-agent assignment process. We consider the setting where, besides profit maximization, it is also desired to maintain diverse assignments for tasks and agents, such that all tasks have been assigned to all agents over subsequent cycles. This problem of multi-cycle assignment with rotational diversity is approached in two sub-problems: The outer problem which augments the original profit maximization objective with additional information about the state of rotational diversity while the inner problem solves the adjusted general assignment problem in a single execution of the model. We discuss strategies to augment the profit values and evaluate them experimentally. The method's efficacy is shown in three case studies: multi-cycle variants of the multiple knapsack and the multiple subset sum problems, and a real-world case study on the test case selection and assignment problem from the software engineering domain.
Triple consistency loss for pairing distributions in GAN-based face synthesis
Authors: Enrique Sanchez, Michel Valstar
Date: 2018-11-08 00:00:00
Tasks: Attribute, Face Generation, Face to Face Translation, Translation

Generative Adversarial Networks have shown impressive results for the task of
object translation, including face-to-face translation. A key component behind
the success of recent approaches is the self-consistency loss, which encourages
a network to recover the original input image when the output generated for a
desired attribute is itself passed through the same network, but with the
target attribute inverted. While the self-consistency loss yields
photo-realistic results, it can be shown that the input and target domains,
supposed to be close, differ substantially. This is empirically found by
observing that a network recovers the input image even if attributes other than
the inversion of the original goal are set as target. This stops one combining
networks for different tasks, or using a network to do progressive forward
passes. In this paper, we show empirical evidence of this effect, and propose a
new loss to bridge the gap between the distributions of the input and target
domains. This "triple consistency loss", aims to minimise the distance between
the outputs generated by the network for different routes to the target,
independent of any intermediate steps. To show this is effective, we
incorporate the triple consistency loss into the training of a new
landmark-guided face to face synthesis, where, contrary to previous works, the
generated images can simultaneously undergo a large transformation in both
expression and pose. To the best of our knowledge, we are the first to tackle
the problem of mismatching distributions in self-domain synthesis, and to
propose "in-the-wild" landmark-guided synthesis. Code will be available at
https://github.com/ESanchezLozano/GANnotation
Fashionable Modelling with Flux
Authors: Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, Viral Shah
Date: 2018-11-01 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning as a discipline has seen an incredible surge of interest in
recent years due in large part to a perfect storm of new theory, superior
tooling, renewed interest in its capabilities. We present in this paper a
framework named Flux that shows how further refinement of the core ideas of
machine learning, built upon the foundation of the Julia programming language,
can yield an environment that is simple, easily modifiable, and performant. We
detail the fundamental principles of Flux as a framework for differentiable
programming, give examples of models that are implemented within Flux to
display many of the language and framework-level features that contribute to
its ease of use and high productivity, display internal compiler techniques
used to enable the acceleration and performance that lies at the heart of Flux,
and finally give an overview of the larger ecosystem that Flux fits inside of.
Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition
Authors: Shenglan Liu, Shuai Guo, Hong Qiao, Yang Wang, Bin Wang, Wenbo Luo, Mingming Zhang, Keye Zhang, Bixuan Du
Date: 2018-11-08 00:00:00
Tasks: Emotion Recognition

Human emotion recognition is an important direction in the field of biometric
and information forensics. However, most existing human emotion research are
based on the single RGB view. In this paper, we introduce a RGBD video-emotion
dataset and a RGBD face-emotion dataset for research. To our best knowledge,
this may be the first RGBD video-emotion dataset. We propose a new supervised
nonlinear multi-view laplacian eigenmaps (MvLE) approach and a
multihidden-layer out-of-sample network (MHON) for RGB-D humanemotion
recognition. To get better representations of RGB view and depth view, MvLE is
used to map the training set of both views from original space into the common
subspace. As RGB view and depth view lie in different spaces, a new distance
metric bag of neighbors (BON) used in MvLE can get the similar distributions of
the two views. Finally, MHON is used to get the low-dimensional representations
of test data and predict their labels. MvLE can deal with the cases that RGB
view and depth view have different size of features, even different number of
samples and classes. And our methods can be easily extended to more than two
views. The experiment results indicate the effectiveness of our methods over
some state-of-art methods.
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
Authors: Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
Date: 2018-11-08 00:00:00
Tasks: Classification, General Classification, Nuclei Classification, Segmentation, whole slide images

Due to cellular heterogeneity, cell nuclei classification, segmentation, and
detection from pathological images are challenging tasks. In the last few
years, Deep Convolutional Neural Networks (DCNN) approaches have been shown
state-of-the-art (SOTA) performance on histopathological imaging in different
studies. In this work, we have proposed different advanced DCNN models and
evaluated for nuclei classification, segmentation, and detection. First, the
Densely Connected Recurrent Convolutional Network (DCRN) model is used for
nuclei classification. Second, Recurrent Residual U-Net (R2U-Net) is applied
for nuclei segmentation. Third, the R2U-Net regression model which is named
UD-Net is used for nuclei detection from pathological images. The experiments
are conducted with different datasets including Routine Colon Cancer(RCC)
classification and detection dataset, and Nuclei Segmentation Challenge 2018
dataset. The experimental results show that the proposed DCNN models provide
superior performance compared to the existing approaches for nuclei
classification, segmentation, and detection tasks. The results are evaluated
with different performance metrics including precision, recall, Dice
Coefficient (DC), Means Squared Errors (MSE), F1-score, and overall accuracy.
We have achieved around 3.4% and 4.5% better F-1 score for nuclei
classification and detection tasks compared to recently published DCNN based
method. In addition, R2U-Net shows around 92.15% testing accuracy in term of
DC. These improved methods will help for pathological practices for better
quantitative analysis of nuclei in Whole Slide Images(WSI) which ultimately
will help for better understanding of different types of cancer in clinical
workflow.