pwc-archive/papers-with-abstracts huggingface dataset:
Testing Preferential Domains Using Sampling
Authors: Palash Dey, Swaprava Nath, Garima Shakya
Date: 2019-02-24 00:00:00

A preferential domain is a collection of sets of preferences which are linear
orders over a set of alternatives. These domains have been studied extensively
in social choice theory due to both its practical importance and theoretical
elegance. Examples of some extensively studied preferential domains include
single peaked, single crossing, Euclidean, etc. In this paper, we study the
sample complexity of testing whether a given preference profile is close to
some specific domain. We consider two notions of closeness: (a) closeness via
preferences, and (b) closeness via alternatives. We further explore the effect
of assuming that the {\em outlier} preferences/alternatives to be random
(instead of arbitrary) on the sample complexity of the testing problem. In most
cases, we show that the above testing problem can be solved with high
probability for all commonly used domains by observing only a small number of
samples (independent of the number of preferences, $n$, and often the number of
alternatives, $m$). In the remaining few cases, we prove either impossibility
results or $\Omega(n)$ lower bound on the sample complexity. We complement our
theoretical findings with extensive simulations to figure out the actual
constant factors of our asymptotic sample complexity bounds.
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
Authors: Hongjoon Ahn, Taesup Moon
Date: 2019-02-24 00:00:00
Tasks: Denoising

We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transition matrix as well as the neural network parameters in an alternating manner until convergence. While we do not make any probabilistic assumption on the underlying clean data, our ICE resembles Expectation-Maximization (EM) with variational approximation, and it takes advantage of the property of N-DUDE being locally robust around the true channel. With extensive experiments on several radically different types of data, we show that the ICE equipped N-DUDE (dubbed as ICE-N-DUDE) can perform \emph{universally} well regardless of the uncertainties in both the channel and the clean source. Moreover, we show ICE-N-DUDE becomes extremely robust to its hyperparameters and significantly outperforms the strong baseline that can deal with the channel uncertainties for denoising, the widely used Baum-Welch (BW) algorithm for hidden Markov models (HMM).
Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations
Authors: Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn
Date: 2019-02-24 00:00:00

Crowd-sourcing is a cheap and popular means of creating training and
evaluation datasets for machine learning, however it poses the problem of
`truth inference', as individual workers cannot be wholly trusted to provide
reliable annotations. Research into models of annotation aggregation attempts
to infer a latent `true' annotation, which has been shown to improve the
utility of crowd-sourced data. However, existing techniques beat simple
baselines only in low redundancy settings, where the number of annotations per
instance is low ($\le 3$), or in situations where workers are unreliable and
produce low quality annotations (e.g., through spamming, random, or adversarial
behaviours.) As we show, datasets produced by crowd-sourcing are often not of
this type: the data is highly redundantly annotated ($\ge 5$ annotations per
instance), and the vast majority of workers produce high quality outputs. In
these settings, the majority vote heuristic performs very well, and most truth
inference models underperform this simple baseline. We propose a novel
technique, based on a Bayesian graphical model with conjugate priors, and
simple iterative expectation-maximisation inference. Our technique produces
competitive performance to the state-of-the-art benchmark methods, and is the
only method that significantly outperforms the majority vote heuristic at
one-sided level 0.025, shown by significance tests. Moreover, our technique is
simple, is implemented in only 50 lines of code, and trains in seconds.
Bi-Skip: A Motion Deblurring Network Using Self-paced Learning
Authors: Yiwei Zhang, Chunbiao Zhu, Ge Li, Yuan Zhao, Haifeng Shen
Date: 2019-02-24 00:00:00
Tasks: Deblurring

A fast and effective motion deblurring method has great application values in
real life. This work presents an innovative approach in which a self-paced
learning is combined with GAN to deblur image. First, We explain that a proper
generator can be used as deep priors and point out that the solution for
pixel-based loss is not same with the one for perception-based loss. By using
these ideas as starting points, a Bi-Skip network is proposed to improve the
generating ability and a bi-level loss is adopted to solve the problem that
common conditions are non-identical. Second, considering that the complex
motion blur will perturb the network in the training process, a self-paced
mechanism is adopted to enhance the robustness of the network. Through
extensive evaluations on both qualitative and quantitative criteria, it is
demonstrated that our approach has a competitive advantage over
state-of-the-art methods.
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
Authors: Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, Felix Heide
Date: 2019-02-24 00:00:00
Proceeding: CVPR 2020 6
Tasks: 2D Object Detection, Autonomous Vehicles, Decision Making, object-detection, Object Detection, Sensor Fusion

The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare "edge-case" scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here https://github.com/princeton-computational-imaging/SeeingThroughFog.
Unlexicalized Transition-based Discontinuous Constituency Parsing
Authors: Maximin Coavoux, Benoît Crabbé, Shay B. Cohen
Date: 2019-02-24 00:00:00
Proceeding: TACL 2019 3
Tasks: Constituency Parsing

Lexicalized parsing models are based on the assumptions that (i) constituents
are organized around a lexical head (ii) bilexical statistics are crucial to
solve ambiguities. In this paper, we introduce an unlexicalized
transition-based parser for discontinuous constituency structures, based on a
structure-label transition system and a bi-LSTM scoring system. We compare it
to lexicalized parsing models in order to address the question of
lexicalization in the context of discontinuous constituency parsing. Our
experiments show that unlexicalized models systematically achieve higher
results than lexicalized models, and provide additional empirical evidence that
lexicalization is not necessary to achieve strong parsing results. Our best
unlexicalized model sets a new state of the art on English and German
discontinuous constituency treebanks. We further provide a per-phenomenon
analysis of its errors on discontinuous constituents.
On the Use of Emojis to Train Emotion Classifiers
Authors: Wegdan Hussien, Mahmoud Al-Ayyoub, Yahya Tashtoush, Mohammed Al-Kabi
Date: 2019-02-24 00:00:00
Tasks: Humor Detection

Nowadays, the automatic detection of emotions is employed by many
applications in different fields like security informatics, e-learning, humor
detection, targeted advertising, etc. Many of these applications focus on
social media and treat this problem as a classification problem, which requires
preparing training data. The typical method for annotating the training data by
human experts is considered time consuming, labor intensive and sometimes prone
to error. Moreover, such an approach is not easily extensible to new
domains/languages since such extensions require annotating new training data.
In this study, we propose a distant supervised learning approach where the
training sentences are automatically annotated based on the emojis they have.
Such training data would be very cheap to produce compared with the manually
created training data, thus, much larger training data can be easily obtained.
On the other hand, this training data would naturally have lower quality as it
may contain some errors in the annotation. Nonetheless, we experimentally show
that training classifiers on cheap, large and possibly erroneous data annotated
using this approach leads to more accurate results compared with training the
same classifiers on the more expensive, much smaller and error-free manually
annotated training data. Our experiments are conducted on an in-house dataset
of emotional Arabic tweets and the classifiers we consider are: Support Vector
Machine (SVM), Multinomial Naive Bayes (MNB) and Random Forest (RF). In
addition to experimenting with single classifiers, we also consider using an
ensemble of classifiers. The results show that using an automatically annotated
training data (that is only one order of magnitude larger than the manually
annotated one) gives better results in almost all settings considered.
3D Guided Fine-Grained Face Manipulation
Authors: Zhenglin Geng, Chen Cao, Sergey Tulyakov
Date: 2019-02-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Model

We present a method for fine-grained face manipulation. Given a face image
with an arbitrary expression, our method can synthesize another arbitrary
expression by the same person. This is achieved by first fitting a 3D face
model and then disentangling the face into a texture and a shape. We then learn
different networks in these two spaces. In the texture space, we use a
conditional generative network to change the appearance, and carefully design
input formats and loss functions to achieve the best results. In the shape
space, we use a fully connected network to predict the accurate shapes and use
the available depth data for supervision. Both networks are conditioned on
expression coefficients rather than discrete labels, allowing us to generate an
unlimited amount of expressions. We show the superiority of this disentangling
approach through both quantitative and qualitative studies. In a user study,
our method is preferred in 85% of cases when compared to the most recent work.
When compared to the ground truth, annotators cannot reliably distinguish
between our synthesized images and real images, preferring our method in 53% of
the cases.
The ARIEL-CMU Systems for LoReHLT18
Authors: Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown
Date: 2019-02-24 00:00:00
Tasks: Machine Translation, Translation

This paper describes the ARIEL-CMU submissions to the Low Resource Human
Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine
Translation (MT), Entity Discovery and Linking (EDL), and detection of
Situation Frames in Text and Speech (SF Text and Speech).
TBNet:Pulmonary Tuberculosis Diagnosing System using Deep Neural Networks
Authors: Ram Srivatsav Ghorakavi
Date: 2019-02-24 00:00:00
Tasks: Data Augmentation, Diagnostic

Tuberculosis is a deadly infectious disease prevalent around the world. Due
to the lack of proper technology in place, the early detection of this disease
is unattainable. Also, the available methods to detect Tuberculosis is not
up-to a commendable standards due to their dependency on unnecessary features,
this make such technology obsolete for a reliable health-care technology. In
this paper, I propose a deep-learning based system which diagnoses tuberculosis
based on the important features in Chest X-rays along with original chest
X-rays. Employing our system will accelerate the process of tuberculosis
diagnosis by overcoming the need to perform the time-consuming sputum-based
testing method (Diagnostic Microbiology). In contrast to the previous methods
\cite{kant2018towards, melendez2016automated}, our work utilizes the
state-of-the-art ResNet \cite{he2016deep} with proper data augmentation using
traditional robust features like Haar \cite{viola2005detecting,viola2001rapid}
and LBP \cite{ojala1994performance,ojala1996comparative}. I observed that such
a procedure enhances the rate of tuberculosis detection to a highly
satisfactory level. Our work uses the publicly available pulmonary chest X-ray
dataset to train our network \cite{jaeger2014two}. Nevertheless, the publicly
available dataset is very small and is inadequate to achieve the best accuracy.
To overcome this issue I have devised an intuitive feature based data
augmentation pipeline. Our approach shall help the deep neural network
\cite{lecun2015deep,he2016deep,krizhevsky2012imagenet} to focus its training on
tuberculosis affected regions making it more robust and accurate, when compared
to other conventional methods that use procedures like mirroring and rotation.
By using our simple yet powerful techniques, I observed a 10\% boost in
performance accuracy.
A Circuit-Level Amoeba-Inspired SAT Solver
Authors: N. Takeuchi, M. Aono, Y. Hara-Azumi, C. L. Ayala
Date: 2018-12-15 00:00:00

AmbSAT (or AmoebaSAT) is a biologically-inspired stochastic local search (SLS) solver to explore solutions to the Boolean satisfiability problem (SAT). AmbSAT updates multiple variables in parallel at every iteration step, and thus AmbSAT can find solutions with a fewer number of iteration steps than some other conventional SLS solvers for a specific set of SAT instances. However, the parallelism of AmbSAT is not compatible with general-purpose microprocessors in that many clock cycles are required to execute each iteration; thus, AmbSAT requires special hardware that can exploit the parallelism of AmbSAT to quickly find solutions. In this paper, we propose a circuit model (hardware-friendly algorithm) that explores solutions to SAT in a similar way to AmbSAT, which we call circuit-level AmbSAT (CL-AmbSAT). We conducted numerical simulation to evaluate the search performance of CL-AmbSAT for a set of randomly generated SAT instances that was designed to estimate the scalability of our approach. Simulation results showed that CL-AmbSAT finds solutions with a fewer iteration number than a powerful SLS solver, ProbSAT, and outperforms even AmbSAT. Since CL-AmbSAT uses simple combinational logic to update variables, CL-AmbSAT can be easily implemented in various hardware.
Medical Multimodal Classifiers Under Scarce Data Condition
Authors: Faik Aydin, Maggie Zhang, Michelle Ananda-Rajah, Gholamreza Haffari
Date: 2019-02-24 00:00:00
Tasks: Clustering, Transfer Learning

Data is one of the essential ingredients to power deep learning research.
Small datasets, especially specific to medical institutes, bring challenges to
deep learning training stage. This work aims to develop a practical deep
multimodal that can classify patients into abnormal and normal categories
accurately as well as assist radiologists to detect visual and textual
anomalies by locating areas of interest. The detection of the anomalies is
achieved through a novel technique which extends the integrated gradients
methodology with an unsupervised clustering algorithm. This technique also
introduces a tuning parameter which trades off true positive signals to denoise
false positive signals in the detection process. To overcome the challenges of
the small training dataset which only has 3K frontal X-ray images and medical
reports in pairs, we have adopted transfer learning for the multimodal which
concatenates the layers of image and text submodels. The image submodel was
trained on the vast ChestX-ray14 dataset, while the text submodel transferred a
pertained word embedding layer from a hospital-specific corpus. Experimental
results show that our multimodal improves the accuracy of the classification by
4% and 7% on average of 50 epochs, compared to the individual text and image
model, respectively.
De-Biasing The Lasso With Degrees-of-Freedom Adjustment
Authors: Pierre C. Bellec, Cun-Hui Zhang
Date: 2019-02-24 00:00:00
Tasks: LEMMA

This paper studies schemes to de-bias the Lasso in a linear model $y=X\beta+\epsilon$ where the goal is to construct confidence intervals for $a_0^T\beta$ in a direction $a_0$, where $X$ has iid $N(0,\Sigma)$ rows. We show that previously analyzed propositions to de-bias the Lasso require a modification in order to enjoy efficiency in a full range of sparsity. This modification takes the form of a degrees-of-freedom adjustment that accounts for the dimension of the model selected by Lasso. Let $s_0$ be the true sparsity. If $\Sigma$ is known and the ideal score vector proportional to $X\Sigma^{-1}a_0$ is used, the unadjusted de-biasing schemes proposed previously enjoy efficiency if $s_0\lll n^{2/3}$. However, if $s_0\ggg n^{2/3}$, the unadjusted schemes cannot be efficient in certain $a_0$: then it is necessary to modify existing procedures by a degrees-of-freedom adjustment. This modification grants asymptotic efficiency for any $a_0$ when $s_0/p\to 0$ and $s_0\log(p/s_0)/n \to 0$. If $\Sigma$ is unknown, efficiency is granted for general $a_0$ when $$\frac{s_0\log p}{n}+\min\Big\{\frac{s_\Omega\log p}{n},\frac{\|\Sigma^{-1}a_0\|_1\sqrt{\log p}}{\|\Sigma^{-1/2}a_0\|_2 \sqrt n}\Big\}+\frac{\min(s_\Omega,s_0)\log p}{\sqrt n}\to0$$ where $s_\Omega=\|\Sigma^{-1}a_0\|_0$, provided that the de-biased estimate is modified with the degrees-of-freedom adjustment. The dependence in $s_0,s_\Omega$ and $\|\Sigma^{-1}a_0\|_1$ is optimal. Our estimated score vector provides a novel methodology to handle dense $a_0$. Our analysis shows that the degrees-of-freedom adjustment is not needed when the initial bias in direction $a_0$ is small, which is granted under stringent conditions on $\Sigma^{-1}$. The main proof argument is an interpolation path similar to that typically used to derive Slepian's lemma. It yields a new $\ell_\infty$ error bound for the Lasso which is of independent interest.
Aggregating E-commerce Search Results from Heterogeneous Sources via Hierarchical Reinforcement Learning
Authors: Ryuichi Takanobu, Tao Zhuang, Minlie Huang, Jun Feng, Haihong Tang, Bo Zheng
Date: 2019-02-24 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), slot-filling, Slot Filling

In this paper, we investigate the task of aggregating search results from
heterogeneous sources in an E-commerce environment. First, unlike traditional
aggregated web search that merely presents multi-sourced results in the first
page, this new task may present aggregated results in all pages and has to
dynamically decide which source should be presented in the current page.
Second, as pointed out by many existing studies, it is not trivial to rank
items from heterogeneous sources because the relevance scores from different
source systems are not directly comparable. To address these two issues, we
decompose the task into two subtasks in a hierarchical structure: a high-level
task for source selection where we model the sequential patterns of user
behaviors onto aggregated results in different pages so as to understand user
intents and select the relevant sources properly; and a low-level task for item
presentation where we formulate a slot filling process to sequentially present
the items instead of giving each item a relevance score when deciding the
presentation order of heterogeneous items. Since both subtasks can be naturally
formulated as sequential decision problems and learn from the future user
feedback on search results, we build our model with hierarchical reinforcement
learning. Extensive experiments demonstrate that our model obtains remarkable
improvements in search performance metrics, and achieves a higher user
satisfaction.
Evaluating Differentially Private Machine Learning in Practice
Authors: Bargav Jayaraman, David Evans
Date: 2019-02-24 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $\epsilon$, about how much information is leaked by a mechanism. However, implementations of privacy-preserving machine learning often select large values of $\epsilon$ in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, differential privacy variants that offer tighter analyses are used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is a huge gap between the upper bounds on privacy loss that can be guaranteed, even with advanced mechanisms, and the effective privacy loss that can be measured using current inference attacks. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs with guarantees for complex learning tasks: settings that provide limited accuracy loss provide meaningless privacy guarantees, and settings that provide strong privacy guarantees result in useless models. Code for the experiments can be found here: https://github.com/bargavj/EvaluatingDPML
Statistical Method to Model the Quality Inconsistencies of the Welding Process
Authors: Mohammad Aminisharifabad, Qingyu Yang
Date: 2019-02-24 00:00:00

Resistance Spot Welding (RSW) is an important manufacturing process that
attracts increasing attention in automotive industry. However, due to the
complexity of the manufacturing process, the corresponding product quality
shows significant inconsistencies even under the same process setup. This paper
develops a statistical method to capture the inconsistence of welding quality
measurements (e.g., nugget width) based on process parameters to efficiently
monitor product quality. The proposed method provides engineering efficiency
and cost saving benefit through reduction of physical testing required for
weldability and verification. The developed method is applied to the real-world
welding process.