pwc-archive/papers-with-abstracts huggingface dataset:
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
Authors: Liye Fu, Jonathan P. Chang, Cristian Danescu-Niculescu-Mizil
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Cloze Test, Common Sense Reasoning

People often share personal narratives in order to seek advice from others.
To properly infer the narrator's intention, one needs to apply a certain degree
of common sense and social intuition. To test the capabilities of NLP systems
to recover such intuition, we introduce the new task of inferring what is the
advice-seeking goal behind a personal narrative. We formulate this as a cloze
test, where the goal is to identify which of two advice-seeking questions was
removed from a given narrative.
  The main challenge in constructing this task is finding pairs of semantically
plausible advice-seeking questions for given narratives. To address this
challenge, we devise a method that exploits commonalities in experiences people
share online to automatically extract pairs of questions that are appropriate
candidates for the cloze task. This results in a dataset of over 20,000
personal narratives, each matched with a pair of related advice-seeking
questions: one actually intended by the narrator, and the other one not. The
dataset covers a very broad array of human experiences, from dating, to career
options, to stolen iPads. We use human annotation to determine the degree to
which the task relies on common sense and social intuition in addition to a
semantic understanding of the narrative. By introducing several baselines for
this new task we demonstrate its feasibility and identify avenues for better
modeling the intention of the narrator.
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
Authors: Christopher Morris, Gaurav Rattan, Petra Mutzel
Date: 2019-04-02 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: General Classification, Graph Classification, Graph Learning, Graph Regression

Graph kernels based on the $1$-dimensional Weisfeiler-Leman algorithm and corresponding neural architectures recently emerged as powerful tools for (supervised) learning with graphs. However, due to the purely local nature of the algorithms, they might miss essential patterns in the given data and can only handle binary relations. The $k$-dimensional Weisfeiler-Leman algorithm addresses this by considering $k$-tuples, defined over the set of vertices, and defines a suitable notion of adjacency between these vertex tuples. Hence, it accounts for the higher-order interactions between vertices. However, it does not scale and may suffer from overfitting when used in a machine learning setting. Hence, it remains an important open problem to design WL-based graph learning methods that are simultaneously expressive, scalable, and non-overfitting. Here, we propose local variants and corresponding neural architectures, which consider a subset of the original neighborhood, making them more scalable, and less prone to overfitting. The expressive power of (one of) our algorithms is strictly higher than the original algorithm, in terms of ability to distinguish non-isomorphic graphs. Our experimental study confirms that the local algorithms, both kernel and neural architectures, lead to vastly reduced computation times, and prevent overfitting. The kernel version establishes a new state-of-the-art for graph classification on a wide range of benchmark datasets, while the neural version shows promising performance on large-scale molecular regression tasks.
End-to-End Visual Speech Recognition for Small-Scale Datasets
Authors: Stavros Petridis, Yujiang Wang, Pingchuan Ma, Zuwei Li, Maja Pantic
Date: 2019-04-02 00:00:00
Tasks: General Classification, speech-recognition, Speech Recognition, Visual Speech Recognition

Visual speech recognition models traditionally consist of two stages, feature extraction and classification. Several deep learning approaches have been recently presented aiming to replace the feature extraction stage by automatically extracting features from mouth images. However, research on joint learning of features and classification remains limited. In addition, most of the existing methods require large amounts of data in order to achieve state-of-the-art performance, otherwise they under-perform. In this work, we present an end-to-end visual speech recognition system based on fully-connected layers and Long-Short Memory (LSTM) networks which is suitable for small-scale datasets. The model consists of two streams which extract features directly from the mouth and difference images, respectively. The temporal dynamics in each stream are modelled by a Bidirectional LSTM (BLSTM) and the fusion of the two streams takes place via another BLSTM. An absolute improvement of 0.6%, 3.4%, 3.9%, 11.4% over the state-of-the-art is reported on the OuluVS2, CUAVE, AVLetters and AVLetters2 databases, respectively.
A Benchmark for Edge-Preserving Image Smoothing
Authors: Feida Zhu, Zhetong Liang, Xixi Jia, Lei Zhang, Yizhou Yu
Date: 2019-04-02 00:00:00
Tasks: image smoothing

Edge-preserving image smoothing is an important step for many low-level
vision problems. Though many algorithms have been proposed, there are several
difficulties hindering its further development. First, most existing algorithms
cannot perform well on a wide range of image contents using a single parameter
setting. Second, the performance evaluation of edge-preserving image smoothing
remains subjective, and there lacks a widely accepted datasets to objectively
compare the different algorithms. To address these issues and further advance
the state of the art, in this work we propose a benchmark for edge-preserving
image smoothing. This benchmark includes an image dataset with groundtruth
image smoothing results as well as baseline algorithms that can generate
competitive edge-preserving smoothing results for a wide range of image
contents. The established dataset contains 500 training and testing images with
a number of representative visual object categories, while the baseline methods
in our benchmark are built upon representative deep convolutional network
architectures, on top of which we design novel loss functions well suited for
edge-preserving image smoothing. The trained deep networks run faster than most
state-of-the-art smoothing algorithms with leading smoothing results both
qualitatively and quantitatively. The benchmark is publicly accessible via
https://github.com/zhufeida/Benchmark_EPS.
Unsupervised training of neural mask-based beamforming
Authors: Lukas Drude, Jahn Heymann, Reinhold Haeb-Umbach
Date: 2019-04-02 00:00:00
Tasks: speech-recognition, Speech Recognition

We present an unsupervised training approach for a neural network-based mask
estimator in an acoustic beamforming application. The network is trained to
maximize a likelihood criterion derived from a spatial mixture model of the
observations. It is trained from scratch without requiring any parallel data
consisting of degraded input and clean training targets. Thus, training can be
carried out on real recordings of noisy speech rather than simulated ones. In
contrast to previous work on unsupervised training of neural mask estimators,
our approach avoids the need for a possibly pre-trained teacher model entirely.
We demonstrate the effectiveness of our approach by speech recognition
experiments on two different datasets: one mainly deteriorated by noise (CHiME
4) and one by reverberation (REVERB). The results show that the performance of
the proposed system is on par with a supervised system using oracle target
masks for training and with a system trained using a model-based teacher.
A Game of Dice: Machine Learning and the Question Concerning Art
Authors: Paul Todorov
Date: 2019-04-02 00:00:00
Tasks: BIG-bench Machine Learning

We review some practical and philosophical questions raised by the use of
machine learning in creative practice. Beyond the obvious problems regarding
plagiarism and authorship, we argue that the novelty in AI Art relies mostly on
a narrow machine learning contribution : manifold approximation. Nevertheless,
this contribution creates a radical shift in the way we have to consider this
movement. Is this omnipotent tool a blessing or a curse for the artists?
Automatic diagnosis of the 12-lead ECG using a deep neural network
Authors: Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz P. Ribeiro
Date: 2019-04-02 00:00:00
Tasks: ECG Classification, Electrocardiography (ECG), Specificity

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.
BARISTA: Efficient and Scalable Serverless Serving System for Deep Learning Prediction Services
Authors: Anirban Bhattacharjee, Ajay Dev Chhokra, Zhuangwei Kang, Hongyang Sun, Aniruddha Gokhale, Gabor Karsai
Date: 2019-04-02 00:00:00
Tasks: Deep Learning, Management, Prediction

Pre-trained deep learning models are increasingly being used to offer a
variety of compute-intensive predictive analytics services such as fitness
tracking, speech and image recognition. The stateless and highly parallelizable
nature of deep learning models makes them well-suited for serverless computing
paradigm. However, making effective resource management decisions for these
services is a hard problem due to the dynamic workloads and diverse set of
available resource configurations that have their deployment and management
costs. To address these challenges, we present a distributed and scalable
deep-learning prediction serving system called Barista and make the following
contributions. First, we present a fast and effective methodology for
forecasting workloads by identifying various trends. Second, we formulate an
optimization problem to minimize the total cost incurred while ensuring bounded
prediction latency with reasonable accuracy. Third, we propose an efficient
heuristic to identify suitable compute resource configurations. Fourth, we
propose an intelligent agent to allocate and manage the compute resources by
horizontal and vertical scaling to maintain the required prediction latency.
Finally, using representative real-world workloads for urban transportation
service, we demonstrate and validate the capabilities of Barista.
Contrastive Predictive Coding Based Feature for Automatic Speaker Verification
Authors: Cheng-I Lai
Date: 2019-04-01 00:00:00
Tasks: Representation Learning, Speaker Verification

This thesis describes our ongoing work on Contrastive Predictive Coding (CPC)
features for speaker verification. CPC is a recently proposed representation
learning framework based on predictive coding and noise contrastive estimation.
We focus on incorporating CPC features into the standard automatic speaker
verification systems, and we present our methods, experiments, and analysis.
This thesis also details necessary background knowledge in past and recent work
on automatic speaker verification systems, conventional speech features, and
the motivation and techniques behind CPC.
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
Authors: Andreas Kofler, Marc Dewey, Tobias Schaeffter, Christian Wald, Christoph Kolbitsch
Date: 2019-04-01 00:00:00
Tasks: compressed sensing, Data Augmentation, Dictionary Learning

In this work we reduce undersampling artefacts in two-dimensional ($2D$) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on $2D$ spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two $2D$ and a $3D$ Deep Learning-based post processing methods and to three iterative reconstruction methods for dynamic cardiac MRI. Our method outperforms the $2D$ spatially trained U-net and the $2D$ spatio-temporal U-net. Compared to the $3D$ spatio-temporal U-net, our method delivers comparable results, but with shorter training times and less training data. Compared to the Compressed Sensing-based methods $kt$-FOCUSS and a total variation regularised reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to an iterative reconstruction method based on adaptive regularization with Dictionary Learning and total variation, while only requiring a small fraction of the computational time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial $2D$ U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required.
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Authors: Abdelrahim Kasem Ahmad, Assef Jafar, Kadan Aljoumaa
Date: 2019-04-01 00:00:00
Tasks: BIG-bench Machine Learning

Customer churn is a major problem and one of the most important concerns for
large companies. Due to the direct effect on the revenues of the companies,
especially in the telecom field, companies are seeking to develop means to
predict potential customer to churn. Therefore, finding factors that increase
customer churn is important to take necessary actions to reduce this churn. The
main contribution of our work is to develop a churn prediction model which
assists telecom operators to predict customers who are most likely subject to
churn. The model developed in this work uses machine learning techniques on big
data platform and builds a new way of features' engineering and selection. In
order to measure the performance of the model, the Area Under Curve (AUC)
standard measure is adopted, and the AUC value obtained is 93.3%. Another main
contribution is to use customer social network in the prediction model by
extracting Social Network Analysis (SNA) features. The use of SNA enhanced the
performance of the model from 84 to 93.3% against AUC standard. The model was
prepared and tested through Spark environment by working on a large dataset
created by transforming big raw data provided by SyriaTel telecom company. The
dataset contained all customers' information over 9 months, and was used to
train, test, and evaluate the system at SyriaTel. The model experimented four
algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM"
and Extreme Gradient Boosting "XGBOOST". However, the best results were
obtained by applying XGBOOST algorithm. This algorithm was used for
classification in this churn predictive model.
Learning Shared Encoding Representation for End-to-End Speech Recognition Models
Authors: Thai-Son Nguyen, Sebastian Stueker, Alex Waibel
Date: 2019-03-31 00:00:00
Tasks: Deep Attention, General Classification, speech-recognition, Speech Recognition

In this work, we learn a shared encoding representation for a multi-task
neural network model optimized with connectionist temporal classification (CTC)
and conventional framewise cross-entropy training criteria. Our experiments
show that the multi-task training not only tackles the complexity of optimizing
CTC models such as acoustic-to-word but also results in significant improvement
compared to the plain-task training with an optimal setup. Furthermore, we
propose to use the encoding representation learned by the multi-task network to
initialize the encoder of attention-based models. Thereby, we train a deep
attention-based end-to-end model with 10 long short-term memory (LSTM) layers
of encoder which produces 12.2\% and 22.6\% word-error-rate on Switchboard and
CallHome subsets of the Hub5 2000 evaluation.
TonY: An Orchestrator for Distributed Machine Learning Jobs
Authors: Anthony Hsu, Keqiu Hu, Jonathan Hung, Arun Suresh, Zhe Zhang
Date: 2019-03-24 00:00:00
Tasks: BIG-bench Machine Learning

Training machine learning (ML) models on large datasets requires considerable
computing power. To speed up training, it is typical to distribute training
across several machines, often with specialized hardware like GPUs or TPUs.
Managing a distributed training job is complex and requires dealing with
resource contention, distributed configurations, monitoring, and fault
tolerance. In this paper, we describe TonY, an open-source orchestrator for
distributed ML jobs built at LinkedIn to address these challenges.
The Landscape of R Packages for Automated Exploratory Data Analysis
Authors: Mateusz Staniak, Przemyslaw Biecek
Date: 2019-03-27 00:00:00
Tasks: Feature Engineering

The increasing availability of large but noisy data sets with a large number of heterogeneous variables leads to the increasing interest in the automation of common tasks for data analysis. The most time-consuming part of this process is the Exploratory Data Analysis, crucial for better domain understanding, data cleaning, data validation, and feature engineering. There is a growing number of libraries that attempt to automate some of the typical Exploratory Data Analysis tasks to make the search for new insights easier and faster. In this paper, we present a systematic review of existing tools for Automated Exploratory Data Analysis (autoEDA). We explore the features of twelve popular R packages to identify the parts of analysis that can be effectively automated with the current tools and to point out new directions for further autoEDA development.
A Posteriori Probabilistic Bounds of Convex Scenario Programs with Validation Tests
Authors: Chao Shang, Fengqi You
Date: 2019-03-27 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

Scenario programs have established themselves as efficient tools towards decision-making under uncertainty. To assess the quality of scenario-based solutions a posteriori, validation tests based on Bernoulli trials have been widely adopted in practice. However, to reach a theoretically reliable judgement of risk, one typically needs to collect massive validation samples. In this work, we propose new a posteriori bounds for convex scenario programs with validation tests, which are dependent on both realizations of support constraints and performance on out-of-sample validation data. The proposed bounds enjoy wide generality in that many existing theoretical results can be incorporated as particular cases. To facilitate practical use, a systematic approach for parameterizing a posteriori probability bounds is also developed, which is shown to possess a variety of desirable properties allowing for easy implementations and clear interpretations. By synthesizing comprehensive information about support constraints and validation tests, improved risk evaluation can be achieved for randomized solutions in comparison with existing a posteriori bounds. Case studies on controller design of aircraft lateral motion are presented to validate the effectiveness of the proposed a posteriori bounds.
Robust Model Predictive Control of Irrigation Systems with Active Uncertainty Learning and Data Analytics
Authors: Chao Shang, Wei-Han Chen, Abraham Duncan Stroock, Fengqi You
Date: 2018-10-14 00:00:00
Tasks: Clustering, Model Predictive Control

We develop a novel data-driven robust model predictive control (DDRMPC) approach for automatic control of irrigation systems. The fundamental idea is to integrate both mechanistic models, which describe dynamics in soil moisture variations, and data-driven models, which characterize uncertainty in forecast errors of evapotranspiration and precipitation, into a holistic systems control framework. To better capture the support of uncertainty distribution, we take a new learning-based approach by constructing uncertainty sets from historical data. For evapotranspiration forecast error, the support vector clustering-based uncertainty set is adopted, which can be conveniently built from historical data. As for precipitation forecast errors, we analyze the dependence of their distribution on forecast values, and further design a tailored uncertainty set based on the properties of this type of uncertainty. In this way, the overall uncertainty distribution can be elaborately described, which finally contributes to rational and efficient control decisions. To assure the quality of data-driven uncertainty sets, a training-calibration scheme is used to provide theoretical performance guarantees. A generalized affine decision rule is adopted to obtain tractable approximations of optimal control problems, thereby ensuring the practicability of DDRMPC. Case studies using real data show that, DDRMPC can reliably maintain soil moisture above the safety level and avoid crop devastation. The proposed DDRMPC approach leads to a 40% reduction of total water consumption compared to the fine-tuned open-loop control strategy. In comparison with the carefully tuned rule-based control and certainty equivalent model predictive control, the proposed DDRMPC approach can significantly reduce the total water consumption and improve the control performance.
Agent-based Vs Agent-less Sandbox for Dynamic Behavioral Analysis
Authors: Muhammad Ali, Stavros Shiaeles, Maria Papadaki, Bogdan Ghita
Date: 2019-03-12 00:00:00
Tasks: Malware Detection

Malicious software is detected and classified by either static analysis or
dynamic analysis. In static analysis, malware samples are reverse engineered
and analyzed so that signatures of malware can be constructed. These techniques
can be easily thwarted through polymorphic, metamorphic malware, obfuscation
and packing techniques, whereas in dynamic analysis malware samples are
executed in a controlled environment using the sandboxing technique, in order
to model the behavior of malware. In this paper, we have analyzed Petya,
Spyeye, VolatileCedar, PAFISH etc. through Agent-based and Agentless dynamic
sandbox systems in order to investigate and benchmark their efficiency in
advanced malware detection.