pwc-archive/papers-with-abstracts huggingface dataset:
XAI Beyond Classification: Interpretable Neural Clustering
Authors: Xi Peng, Yunnan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou
Date: 2018-08-22 00:00:00
Tasks: Classification, Clustering, Explainable Artificial Intelligence (XAI), Online Clustering, Representation Learning

In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, rather than giving post-hoc explanations of a black-box model. The second is implementing discrete $k$-means with a differentiable neural network that embraces the advantages of parallel computing, online clustering, and clustering-favorable representation learning. To address these two challenges, we design a novel neural network, which is a differentiable reformulation of the vanilla $k$-means, called inTerpretable nEuraL cLustering (TELL). Our contributions are threefold. First, to the best of our knowledge, most existing XAI works focus on supervised learning paradigms. This work is one of the few XAI studies on unsupervised learning, in particular, data clustering. Second, TELL is an interpretable, or the so-called intrinsically explainable and transparent model. In contrast, most existing XAI studies resort to various means for understanding a black-box model with post-hoc explanations. Third, from the view of data clustering, TELL possesses many properties highly desired by $k$-means, including but not limited to online clustering, plug-and-play module, parallel computing, and provable convergence. Extensive experiments show that our method achieves superior performance comparing with 14 clustering approaches on three challenging data sets. The source code could be accessed at \url{www.pengxi.me}.
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
Authors: Dongxiang Zhang, Lei Wang, Luming Zhang, Bing Tian Dai, Heng Tao Shen
Date: 2018-08-22 00:00:00
Tasks: Math, Semantic Parsing, Survey

Solving mathematical word problems (MWPs) automatically is challenging,
primarily due to the semantic gap between human-readable words and
machine-understandable logics. Despite the long history dated back to the1960s,
MWPs have regained intensive attention in the past few years with the
advancement of Artificial Intelligence (AI). Solving MWPs successfully is
considered as a milestone towards general AI. Many systems have claimed
promising results in self-crafted and small-scale datasets. However, when
applied on large and diverse datasets, none of the proposed methods in the
literature achieves high precision, revealing that current MWP solvers still
have much room for improvement. This motivated us to present a comprehensive
survey to deliver a clear and complete picture of automatic math problem
solvers. In this survey, we emphasize on algebraic word problems, summarize
their extracted features and proposed techniques to bridge the semantic gap and
compare their performance in the publicly accessible datasets. We also cover
automatic solvers for other types of math problems such as geometric problems
that require the understanding of diagrams. Finally, we identify several
emerging research directions for the readers with interests in MWPs.
Clustering and Labelling Auction Fraud Data
Authors: Ahmad Alzahrani, Samira Sadaoui
Date: 2018-08-22 00:00:00
Tasks: Clustering, General Classification

Although shill bidding is a common auction fraud, it is however very tough to
detect. Due to the unavailability and lack of training data, in this study, we
build a high-quality labeled shill bidding dataset based on recently collected
auctions from eBay. Labeling shill biding instances with multidimensional
features is a critical phase for the fraud classification task. For this
purpose, we introduce a new approach to systematically label the fraud data
with the help of the hierarchical clustering CURE that returns remarkable
results as illustrated in the experiments.
DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning
Authors: Milad Nasr, Alireza Bahramali, Amir Houmansadr
Date: 2018-08-22 00:00:00
Tasks: Deep Learning

Flow correlation is the core technique used in a multitude of deanonymization
attacks on Tor. Despite the importance of flow correlation attacks on Tor,
existing flow correlation techniques are considered to be ineffective and
unreliable in linking Tor flows when applied at a large scale, i.e., they
impose high rates of false positive error rates or require impractically long
flow observations to be able to make reliable correlations. In this paper, we
show that, unfortunately, flow correlation attacks can be conducted on Tor
traffic with drastically higher accuracies than before by leveraging emerging
learning mechanisms. We particularly design a system, called DeepCorr, that
outperforms the state-of-the-art by significant margins in correlating Tor
connections. DeepCorr leverages an advanced deep learning architecture to learn
a flow correlation function tailored to Tor's complex network this is in
contrast to previous works' use of generic statistical correlation metrics to
correlated Tor flows. We show that with moderate learning, DeepCorr can
correlate Tor connections (and therefore break its anonymity) with accuracies
significantly higher than existing algorithms, and using substantially shorter
lengths of flow observations. For instance, by collecting only about 900
packets of each target Tor flow (roughly 900KB of Tor data), DeepCorr provides
a flow correlation accuracy of 96% compared to 4% by the state-of-the-art
system of RAPTOR using the same exact setting.
  We hope that our work demonstrates the escalating threat of flow correlation
attacks on Tor given recent advances in learning algorithms, calling for the
timely deployment of effective countermeasures by the Tor community.
A syllable based model for handwriting recognition
Authors: Wassim Swaileh, Thierry Paquet
Date: 2018-08-22 00:00:00
Tasks: Handwriting Recognition, model

In this paper, we introduce a new modeling approach of texts for handwriting
recognition based on syllables. We propose a supervised syllabification
approach for the French and English languages for building a vocabulary of
syllables. Statistical n-gram language models of syllables are trained on
French and English Wikipedia corpora. The handwriting recognition system, based
on optical HMM context independent character models, performs a two pass
decoding, integrating the proposed syllabic models. Evaluation is carried out
on the French RIMES dataset and English IAM dataset by analyzing the
performance for various coverage of the syllable models. We also compare the
syllable models with lexicon and character n-gram models. The proposed approach
reaches interesting performances thanks to its capacity to cover a large amount
of out of vocabulary words working with a limited amount of syllables combined
with statistical n-gram of reasonable order.
CentralNet: a Multilayer Approach for Multimodal Fusion
Authors: Valentin Vielzeuf, Alexis Lechervy, Stéphane Pateux, Frédéric Jurie
Date: 2018-08-22 00:00:00
Tasks: Multi-Task Learning

This paper proposes a novel multimodal fusion approach, aiming to produce
best possible decisions by integrating information coming from multiple media.
While most of the past multimodal approaches either work by projecting the
features of different modalities into the same space, or by coordinating the
representations of each modality through the use of constraints, our approach
borrows from both visions. More specifically, assuming each modality can be
processed by a separated deep convolutional network, allowing to take decisions
independently from each modality, we introduce a central network linking the
modality specific networks. This central network not only provides a common
feature embedding but also regularizes the modality specific networks through
the use of multi-task learning. The proposed approach is validated on 4
different computer vision tasks on which it consistently improves the accuracy
of existing multimodal fusion approaches.
Deep Adaptive Temporal Pooling for Activity Recognition
Authors: Sibo Song, Ngai-Man Cheung, Vijay Chandrasekhar, Bappaditya Mandal
Date: 2018-08-22 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Deep neural networks have recently achieved competitive accuracy for human
activity recognition. However, there is room for improvement, especially in
modeling long-term temporal importance and determining the activity relevance
of different temporal segments in a video. To address this problem, we propose
a learnable and differentiable module: Deep Adaptive Temporal Pooling (DATP).
DATP applies a self-attention mechanism to adaptively pool the classification
scores of different video segments. Specifically, using frame-level features,
DATP regresses importance of different temporal segments and generates weights
for them. Remarkably, DATP is trained using only the video-level label. There
is no need of additional supervision except video-level activity class label.
We conduct extensive experiments to investigate various input features and
different weight models. Experimental results show that DATP can learn to
assign large weights to key video segments. More importantly, DATP can improve
training of frame-level feature extractor. This is because relevant temporal
segments are assigned large weights during back-propagation. Overall, we
achieve state-of-the-art performance on UCF101, HMDB51 and Kinetics datasets.
Learning to Support: Exploiting Structure Information in Support Sets for One-Shot Learning
Authors: Jinchao Liu, Stuart J. Gibson, Margarita Osadchy
Date: 2018-08-22 00:00:00
Tasks: General Classification, Meta-Learning, One-Shot Learning

Deep Learning shows very good performance when trained on large labeled data
sets. The problem of training a deep net on a few or one sample per class
requires a different learning approach which can generalize to unseen classes
using only a few representatives of these classes. This problem has previously
been approached by meta-learning. Here we propose a novel meta-learner which
shows state-of-the-art performance on common benchmarks for one/few shot
classification. Our model features three novel components: First is a
feed-forward embedding that takes random class support samples (after a
customary CNN embedding) and transfers them to a better class representation in
terms of a classification problem. Second is a novel attention mechanism,
inspired by competitive learning, which causes class representatives to compete
with each other to become a temporary class prototype with respect to the query
point. This mechanism allows switching between representatives depending on the
position of the query point. Once a prototype is chosen for each class, the
predicated label is computed using a simple attention mechanism over prototypes
of all considered classes. The third feature is the ability of our meta-learner
to incorporate deeper CNN embedding, enabling larger capacity. Finally, to ease
the training procedure and reduce overfitting, we averages the top $t$ models
(evaluated on the validation) over the optimization trajectory. We show that
this approach can be viewed as an approximation to an ensemble, which saves the
factor of $t$ in training and test times and the factor of of $t$ in the
storage of the final model.
A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber
Authors: MicroBooNE collaboration, C. Adams, M. Alrashed, R. An, J. Anthony, J. Asaadi, A. Ashkenazi, M. Auger, S. Balasubramanian, B. Baller, C. Barnes, G. Barr, M. Bass, F. Bay, A. Bhat, K. Bhattacharya, M. Bishai, A. Blake, T. Bolton, L. Camilleri, D. Caratelli, I. Caro Terrazas, R. Carr, R. Castillo Fernandez, F. Cavanna, G. Cerati, Y. Chen, E. Church, D. Cianci, E. Cohen, G. H. Collin, J. M. Conrad, M. Convery, L. Cooper-Troendle, J. I. Crespo-Anadon, M. Del Tutto, D. Devitt, A. Diaz, K. Duffy, S. Dytman, B. Eberly, A. Ereditato, L. Escudero Sanchez, J. Esquivel, J. J. Evans, A. A. Fadeeva, R. S. Fitzpatrick, B. T. Fleming, D. Franco, A. P. Furmanski, D. Garcia-Gamez, G. T. Garvey, V. Genty, D. Goeldi, S. Gollapinni, O. Goodwin, E. Gramellini, H. Greenlee, R. Grosso, R. Guenette, P. Guzowski, A. Hackenburg, P. Hamilton, O. Hen, J. Hewes, C. Hill, G. A. Horton-Smith, A. Hourlier, E. -C. Huang, C. James, J. Jan de Vries, L. Jiang, R. A. Johnson, J. Joshi, H. Jostlein, Y. -J. Jwa, G. Karagiorgi, W. Ketchum, B. Kirby, M. Kirby, T. Kobilarcik, I. Kreslo, Y. Li, A. Lister, B. R. Littlejohn, S. Lockwitz, D. Lorca, W. C. Louis, M. Luethi, B. Lundberg, X. Luo, A. Marchionni, S. Marcocci, C. Mariani, J. Marshall, J. Martin-Albo, D. A. Martinez Caicedo, A. Mastbaum, V. Meddage, T. Mettler, G. B. Mills, K. Mistry, A. Mogan, J. Moon, M. Mooney, C. D. Moore, J. Mousseau, M. Murphy, R. Murrells, D. Naples, P. Nienaber, J. Nowak, O. Palamara, V. Pandey, V. Paolone, A. Papadopoulou, V. Papavassiliou, S. F. Pate, Z. Pavlovic, E. Piasetzky, D. Porzio, G. Pulliam, X. Qian, J. L. Raaf, A. Rafique, L. Rochester, M. Ross-Lonergan, C. Rudolf von Rohr, B. Russell, D. W. Schmitz, A. Schukraft, W. Seligman, M. H. Shaevitz, R. Sharankova, J. Sinclair, A. Smith, E. L. Snider, M. Soderberg, S. Soldner-Rembold, S. R. Soleti, P. Spentzouris, J. Spitz, J. St. John, T. Strauss, K. Sutton, S. Sword-Fehlberg, A. M. Szelc, N. Tagg, W. Tang, K. Terao, M. Thomson, R. T. Thornton, M. Toups, Y. -T. Tsai, S. Tufanli, T. Usher, W. Van De Pontseele, R. G. Van de Water, B. Viren, M. Weber, H. Wei, D. A. Wickremasinghe, K. Wierman, Z. Williams, S. Wolbers, T. Wongjirad, K. Woodruff, T. Yang, G. Yarbrough, L. E. Yates, G. P. Zeller, J. Zennamo, C. Zhang
Date: 2018-08-22 00:00:00

We have developed a convolutional neural network (CNN) that can make a
pixel-level prediction of objects in image data recorded by a liquid argon time
projection chamber (LArTPC) for the first time. We describe the network design,
training techniques, and software tools developed to train this network. The
goal of this work is to develop a complete deep neural network based data
reconstruction chain for the MicroBooNE detector. We show the first
demonstration of a network's validity on real LArTPC data using MicroBooNE
collection plane images. The demonstration is performed for stopping muon and a
$\nu_\mu$ charged current neutral pion data samples.
FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity
Authors: Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu, David Piorkowski, Jason Tsay, Kush R. Varshney
Date: 2018-08-22 00:00:00
Tasks: Fairness

Accuracy is an important concern for suppliers of artificial intelligence
(AI) services, but considerations beyond accuracy, such as safety (which
includes fairness and explainability), security, and provenance, are also
critical elements to engender consumers' trust in a service. Many industries
use transparent, standardized, but often not legally required documents called
supplier's declarations of conformity (SDoCs) to describe the lineage of a
product along with the safety and performance testing it has undergone. SDoCs
may be considered multi-dimensional fact sheets that capture and quantify
various aspects of the product and its development to make it worthy of
consumers' trust. Inspired by this practice, we propose FactSheets to help
increase trust in AI services. We envision such documents to contain purpose,
performance, safety, security, and provenance information to be completed by AI
service providers for examination by consumers. We suggest a comprehensive set
of declaration items tailored to AI and provide examples for two fictitious AI
services in the appendix of the paper.
On an improvement of LASSO by scaling
Authors: Katsuyuki Hagiwara
Date: 2018-08-22 00:00:00
Tasks: Model Selection

A sparse modeling is a major topic in machine learning and statistics. LASSO
(Least Absolute Shrinkage and Selection Operator) is a popular sparse modeling
method while it has been known to yield unexpected large bias especially at a
sparse representation. There have been several studies for improving this
problem such as the introduction of non-convex regularization terms. The
important point is that this bias problem directly affects model selection in
applications since a sparse representation cannot be selected by a prediction
error based model selection even if it is a good representation. In this
article, we considered to improve this problem by introducing a scaling that
expands LASSO estimator to compensate excessive shrinkage, thus a large bias in
LASSO estimator. We here gave an empirical value for the amount of scaling.
There are two advantages of this scaling method as follows. Since the proposed
scaling value is calculated by using LASSO estimator, we only need LASSO
estimator that is obtained by a fast and stable optimization procedure such as
LARS (Least Angle Regression) under LASSO modification or coordinate descent.
And, the simplicity of our scaling method enables us to derive SURE (Stein's
Unbiased Risk Estimate) under the modified LASSO estimator with scaling. Our
scaling method together with model selection based on SURE is fully empirical
and do not need additional hyper-parameters. In a simple numerical example, we
verified that our scaling method actually improves LASSO and the SURE based
model selection criterion can stably choose an appropriate sparse model.
Escaping from Collapsing Modes in a Constrained Space
Authors: Chia-Che Chang, Chieh Hubert Lin, Che-Rung Lee, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen
Date: 2018-08-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generative Adversarial Network, Image Generation

Generative adversarial networks (GANs) often suffer from unpredictable
mode-collapsing during training. We study the issue of mode collapse of
Boundary Equilibrium Generative Adversarial Network (BEGAN), which is one of
the state-of-the-art generative models. Despite its potential of generating
high-quality images, we find that BEGAN tends to collapse at some modes after a
period of training. We propose a new model, called \emph{BEGAN with a
Constrained Space} (BEGAN-CS), which includes a latent-space constraint in the
loss function. We show that BEGAN-CS can significantly improve training
stability and suppress mode collapse without either increasing the model
complexity or degrading the image quality. Further, we visualize the
distribution of latent vectors to elucidate the effect of latent-space
constraint. The experimental results show that our method has additional
advantages of being able to train on small datasets and to generate images
similar to a given real image yet with variations of designated attributes
on-the-fly.
A Survey of Modern Object Detection Literature using Deep Learning
Authors: Karanbir Singh Chahal, Kuntal Dey
Date: 2018-08-22 00:00:00
Tasks: Deep Learning, General Classification, Object, object-detection, Object Detection, Survey

Object detection is the identification of an object in the image along with
its localisation and classification. It has wide spread applications and is a
critical component for vision based software systems. This paper seeks to
perform a rigorous survey of modern object detection algorithms that use deep
learning. As part of the survey, the topics explored include various
algorithms, quality metrics, speed/size trade offs and training methodologies.
This paper focuses on the two types of object detection algorithms- the SSD
class of single step detectors and the Faster R-CNN class of two step
detectors. Techniques to construct detectors that are portable and fast on low
powered devices are also addressed by exploring new lightweight convolutional
base architectures. Ultimately, a rigorous review of the strengths and
weaknesses of each detector leads us to the present state of the art.
Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplace
Authors: Murat Ali Bayir, Mingsen Xu, Yaojia Zhu, Yifan Shi
Date: 2018-08-22 00:00:00
Tasks: counterfactual

In this paper, we propose an offline counterfactual policy estimation
framework called Genie to optimize Sponsored Search Marketplace. Genie employs
an open box simulation engine with click calibration model to compute the KPI
impact of any modification to the system. From the experimental results on Bing
traffic, we showed that Genie performs better than existing observational
approaches that employs randomized experiments for traffic slices that have
frequent policy updates. We also show that Genie can be used to tune completely
new policies efficiently without creating risky randomized experiments due to
cold start problem. As time of today, Genie hosts more than 10000 optimization
jobs yearly which runs more than 30 Million processing node hours of big data
jobs for Bing Ads. For the last 3 years, Genie has been proven to be the one of
the major platforms to optimize Bing Ads Marketplace due to its reliability
under frequent policy changes and its efficiency to minimize risks in real
experiments.
Analysis of Network Lasso for Semi-Supervised Regression
Authors: A. Jung, N. Vesselinova
Date: 2018-08-22 00:00:00
Tasks: Clustering, regression

We apply network Lasso to semi-supervised regression problems involving
network structured data. This approach lends quite naturally to highly scalable
learning algorithms in the form of message passing over an empirical graph
which represents the network structure of the data. By using a simple
non-parametric regression model, which is motivated by a clustering hypothesis,
we provide an analysis of the estimation error incurred by network Lasso. This
analysis reveals conditions on the the network structure and the available
training data which guarantee network Lasso to be accurate. Remarkably, the
accuracy of network Lasso is related to the existence of sufficiently large
network flows over the empirical graph. Thus, our analysis reveals a connection
between network Lasso and maximum flow problems.
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection
Authors: Chongyang Tao, Wei Wu, Can Xu, Yansong Feng, Dongyan Zhao, Rui Yan
Date: 2018-08-22 00:00:00
Tasks: Decoder, Dialogue Generation, Retrieval, Sentence

In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots. Existing models, such as Cove and ELMo, are trained with limited context (often a single sentence or paragraph), and may not work well on multi-turn conversations, due to the hierarchical nature, informal language, and domain-specific words. To address the challenges, we propose pre-training hierarchical contextualized representations, including contextual word-level and sentence-level representations, by learning a dialogue generation model from large-scale conversations with a hierarchical encoder-decoder architecture. Then the two levels of representations are blended into the input and output layer of a matching model respectively. Experimental results on two benchmark conversation datasets indicate that the proposed hierarchical contextualized representations can bring significantly and consistently improvement to existing matching models for response selection.