pwc-archive/papers-with-abstracts huggingface dataset:
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
Authors: Wenting Li, Deepjyoti Deka, Michael Chertkov, Meng Wang
Date: 2018-10-11 00:00:00

Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a Convolutional Neural Network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units (PMU) placement strategy is proposed and validated against other methods. A significant aspect of our methodology is that under very low observability (7% of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68-bus power systems under varying uncertain conditions, system observability, and measurement quality.
Piano Genie
Authors: Chris Donahue, Ian Simon, Sander Dieleman
Date: 2018-10-11 00:00:00
Tasks: Decoder

We present Piano Genie, an intelligent controller which allows non-musicians
to improvise on the piano. With Piano Genie, a user performs on a simple
interface with eight buttons, and their performance is decoded into the space
of plausible piano music in real time. To learn a suitable mapping procedure
for this problem, we train recurrent neural network autoencoders with discrete
bottlenecks: an encoder learns an appropriate sequence of buttons corresponding
to a piano piece, and a decoder learns to map this sequence back to the
original piece. During performance, we substitute a user's input for the
encoder output, and play the decoder's prediction each time the user presses a
button. To improve the intuitiveness of Piano Genie's performance behavior, we
impose musically meaningful constraints over the encoder's outputs.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
Authors: Xingdi Yuan, Tong Wang, Rui Meng, Khushboo Thaker, Peter Brusilovsky, Daqing He, Adam Trischler
Date: 2018-10-11 00:00:00
Proceeding: ACL 2020 6
Tasks: All, Decoder, Diversity, Keyphrase Generation

Different texts shall by nature correspond to different number of keyphrases. This desideratum is largely missing from existing neural keyphrase generation models. In this study, we address this problem from both modeling and evaluation perspectives. We first propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences. Generation diversity is further enhanced with two novel techniques by manipulating decoder hidden states. In contrast to previous approaches, our model is capable of generating diverse keyphrases and controlling number of outputs. We further propose two evaluation metrics tailored towards the variable-number generation. We also introduce a new dataset StackEx that expands beyond the only existing genre (i.e., academic writing) in keyphrase generation tasks. With both previous and new evaluation metrics, our model outperforms strong baselines on all datasets.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
Authors: Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev
Date: 2018-10-11 00:00:00
Tasks: Decoder, Semantic Parsing, Text to SQL, Text-To-SQL

Most existing studies in text-to-SQL tasks do not require generating complex
SQL queries with multiple clauses or sub-queries, and generalizing to new,
unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network
to address the complex and cross-domain text-to-SQL generation task.
SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL
generation path history and table-aware column attention encoders. We evaluate
SyntaxSQLNet on the Spider text-to-SQL task, which contains databases with
multiple tables and complex SQL queries with multiple SQL clauses and nested
queries. We use a database split setting where databases in the test set are
unseen during training. Experimental results show that SyntaxSQLNet can handle
a significantly greater number of complex SQL examples than prior work,
outperforming the previous state-of-the-art model by 7.3% in exact matching
accuracy. We also show that SyntaxSQLNet can further improve the performance by
an additional 7.5% using a cross-domain augmentation method, resulting in a
14.8% improvement in total. To our knowledge, we are the first to study this
complex and cross-domain text-to-SQL task.
Practical Design Space Exploration
Authors: Luigi Nardi, David Koeplinger, Kunle Olukotun
Date: 2018-10-11 00:00:00

Multi-objective optimization is a crucial matter in computer systems design space exploration because real-world applications often rely on a trade-off between several objectives. Derivatives are usually not available or impractical to compute and the feasibility of an experiment can not always be determined in advance. These problems are particularly difficult when the feasible region is relatively small, and it may be prohibitive to even find a feasible experiment, let alone an optimal one. We introduce a new methodology and corresponding software framework, HyperMapper 2.0, which handles multi-objective optimization, unknown feasibility constraints, and categorical/ordinal variables. This new methodology also supports injection of the user prior knowledge in the search when available. All of these features are common requirements in computer systems but rarely exposed in existing design space exploration systems. The proposed methodology follows a white-box model which is simple to understand and interpret (unlike, for example, neural networks) and can be used by the user to better understand the results of the automatic search. We apply and evaluate the new methodology to the automatic static tuning of hardware accelerators within the recently introduced Spatial programming language, with minimization of design run-time and compute logic under the constraint of the design fitting in a target field-programmable gate array chip. Our results show that HyperMapper 2.0 provides better Pareto fronts compared to state-of-the-art baselines, with better or competitive hypervolume indicator and with 8x improvement in sampling budget for most of the benchmarks explored.
Efficient Augmentation via Data Subsampling
Authors: Michael Kuchnik, Virginia Smith
Date: 2018-10-11 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Augmentation, Image Augmentation

Data augmentation is commonly used to encode invariances in learning methods.
However, this process is often performed in an inefficient manner, as
artificial examples are created by applying a number of transformations to all
points in the training set. The resulting explosion of the dataset size can be
an issue in terms of storage and training costs, as well as in selecting and
tuning the optimal set of transformations to apply. In this work, we
demonstrate that it is possible to significantly reduce the number of data
points included in data augmentation while realizing the same accuracy and
invariance benefits of augmenting the entire dataset. We propose a novel set of
subsampling policies, based on model influence and loss, that can achieve a 90%
reduction in augmentation set size while maintaining the accuracy gains of
standard data augmentation.
MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks
Authors: Yotam Intrator, Gilad Katz, Asaf Shabtai
Date: 2018-10-11 00:00:00
Tasks: Anomaly Detection, valid

Anomaly detection is often considered a challenging field of machine learning
due to the difficulty of obtaining anomalous samples for training and the need
to obtain a sufficient amount of training data. In recent years, autoencoders
have been shown to be effective anomaly detectors that train only on "normal"
data. Generative adversarial networks (GANs) have been used to generate
additional training samples for classifiers, thus making them more accurate and
robust. However, in anomaly detection GANs are only used to reconstruct
existing samples rather than to generate additional ones. This stems both from
the small amount and lack of diversity of anomalous data in most domains. In
this study we propose MDGAN, a novel GAN architecture for improving anomaly
detection through the generation of additional samples. Our approach uses two
discriminators: a dense network for determining whether the generated samples
are of sufficient quality (i.e., valid) and an autoencoder that serves as an
anomaly detector. MDGAN enables us to reconcile two conflicting goals: 1)
generate high-quality samples that can fool the first discriminator, and 2)
generate samples that can eventually be effectively reconstructed by the second
discriminator, thus improving its performance. Empirical evaluation on a
diverse set of datasets demonstrates the merits of our approach.
FeatureLego: Volume Exploration Using Exhaustive Clustering of Super-Voxels
Authors: Shreeraj Jadhav, Saad Nadeem, Arie Kaufman
Date: 2018-10-11 00:00:00
Tasks: Clustering

We present a volume exploration framework, FeatureLego, that uses a novel voxel clustering approach for efficient selection of semantic features. We partition the input volume into a set of compact super-voxels that represent the finest selection granularity. We then perform an exhaustive clustering of these super-voxels using a graph-based clustering method. Unlike the prevalent brute-force parameter sampling approaches, we propose an efficient algorithm to perform this exhaustive clustering. By computing an exhaustive set of clusters, we aim to capture as many boundaries as possible and ensure that the user has sufficient options for efficiently selecting semantically relevant features. Furthermore, we merge all the computed clusters into a single tree of meta-clusters that can be used for hierarchical exploration. We implement an intuitive user-interface to interactively explore volumes using our clustering approach. Finally, we show the effectiveness of our framework on multiple real-world datasets of different modalities.
On Kernel Derivative Approximation with Random Fourier Features
Authors: Zoltan Szabo, Bharath K. Sriperumbudur
Date: 2018-10-11 00:00:00

Random Fourier features (RFF) represent one of the most popular and
wide-spread techniques in machine learning to scale up kernel algorithms.
Despite the numerous successful applications of RFFs, unfortunately, quite
little is understood theoretically on their optimality and limitations of their
performance. Only recently, precise statistical-computational trade-offs have
been established for RFFs in the approximation of kernel values, kernel ridge
regression, kernel PCA and SVM classification. Our goal is to spark the
investigation of optimality of RFF-based approximations in tasks involving not
only function values but derivatives, which naturally lead to optimization
problems with kernel derivatives. Particularly, in this paper, we focus on the
approximation quality of RFFs for kernel derivatives and prove that the
existing finite-sample guarantees can be improved exponentially in terms of the
domain where they hold, using recent tools from unbounded empirical process
theory. Our result implies that the same approximation guarantee is attainable
for kernel derivatives using RFF as achieved for kernel values.
MeshAdv: Adversarial Meshes for Visual Recognition
Authors: Chaowei Xiao, Dawei Yang, Bo Li, Jia Deng, Mingyan Liu
Date: 2018-10-11 00:00:00
Proceeding: CVPR 2019 6

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.
Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns
Authors: Kellie Webster, Marta Recasens, Vera Axelrod, Jason Baldridge
Date: 2018-10-11 00:00:00
Proceeding: TACL 2018 1
Tasks: Coreference Resolution, Diversity, Natural Language Understanding

Coreference resolution is an important task for natural language
understanding, and the resolution of ambiguous pronouns a longstanding
challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in
sufficient volume or diversity to accurately indicate the practical utility of
models. Furthermore, we find gender bias in existing corpora and systems
favoring masculine entities. To address this, we present and release GAP, a
gender-balanced labeled corpus of 8,908 ambiguous pronoun-name pairs sampled to
provide diverse coverage of challenges posed by real-world text. We explore a
range of baselines which demonstrate the complexity of the challenge, the best
achieving just 66.9% F1. We show that syntactic structure and continuous neural
models provide promising, complementary cues for approaching the challenge.
Understanding Priors in Bayesian Neural Networks at the Unit Level
Authors: Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel
Date: 2018-10-11 00:00:00

We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities. Bayesian neural networks with Gaussian priors are well known to induce an L2, "weight decay", regularization. Our results characterize a more intricate regularization effect at the level of the unit activations. Our main result establishes that the induced prior distribution on the units before and after activation becomes increasingly heavy-tailed with the depth of the layer. We show that first layer units are Gaussian, second layer units are sub-exponential, and units in deeper layers are characterized by sub-Weibull distributions. Our results provide new theoretical insight on deep Bayesian neural networks, which we corroborate with simulation experiments.
Fighting Contextual Bandits with Stochastic Smoothing
Authors: Young Hun Jung, Ambuj Tewari
Date: 2018-10-11 00:00:00
Tasks: Multi-Armed Bandits

We introduce a new stochastic smoothing perspective to study adversarial contextual bandit problems. We propose a general algorithm template that represents random perturbation based algorithms and identify several perturbation distributions that lead to strong regret bounds. Using the idea of smoothness, we provide an $O(\sqrt{T})$ zero-order bound for the vanilla algorithm and an $O(L^{*2/3}_{T})$ first-order bound for the clipped version. These bounds hold when the algorithms use with a variety of distributions that have a bounded hazard rate. Our algorithm template includes EXP4 as a special case corresponding to the Gumbel perturbation. Our regret bounds match existing results for EXP4 without relying on the specific properties of the algorithm.
The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews
Authors: Faiz Ali Shah, Kairit Sirts, Dietmar Pfahl
Date: 2018-10-11 00:00:00

Annotation guidelines used to guide the annotation of training and evaluation
datasets can have a considerable impact on the quality of machine learning
models. In this study, we explore the effects of annotation guidelines on the
quality of app feature extraction models. As a main result, we propose several
changes to the existing annotation guidelines with a goal of making the
extracted app features more useful and informative to the app developers. We
test the proposed changes via simulating the application of the new annotation
guidelines and then evaluating the performance of the supervised machine
learning models trained on datasets annotated with initial and simulated
guidelines. While the overall performance of automatic app feature extraction
remains the same as compared to the model trained on the dataset with initial
annotations, the features extracted by the model trained on the dataset with
simulated new annotations are less noisy and more informative to the app
developers. Secondly, we are interested in what kind of annotated training data
is necessary for training an automatic app feature extraction model. In
particular, we explore whether the training set should contain annotated app
reviews from those apps/app categories on which the model is subsequently
planned to be applied, or is it sufficient to have annotated app reviews from
any app available for training, even when these apps are from very different
categories compared to the test app. Our experiments show that having annotated
training reviews from the test app is not necessary although including them
into training set helps to improve recall. Furthermore, we test whether
augmenting the training set with annotated product reviews helps to improve the
performance of app feature extraction. We find that the models trained on
augmented training set lead to improved recall but at the cost of the drop in
precision.
Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications
Authors: Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, Zhouchen Lin
Date: 2018-10-11 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

The heavy-tailed distributions of corrupted outliers and singular values of
all channels in low-level vision have proven effective priors for many
applications such as background modeling, photometric stereo and image
alignment. And they can be well modeled by a hyper-Laplacian. However, the use
of such distributions generally leads to challenging non-convex, non-smooth and
non-Lipschitz problems, and makes existing algorithms very slow for large-scale
applications. Together with the analytic solutions to lp-norm minimization with
two specific values of p, i.e., p=1/2 and p=2/3, we propose two novel bilinear
factor matrix norm minimization models for robust principal component analysis.
We first define the double nuclear norm and Frobenius/nuclear hybrid norm
penalties, and then prove that they are in essence the Schatten-1/2 and 2/3
quasi-norms, respectively, which lead to much more tractable and scalable
Lipschitz optimization problems. Our experimental analysis shows that both our
methods yield more accurate solutions than original Schatten quasi-norm
minimization, even when the number of observations is very limited. Finally, we
apply our penalties to various low-level vision problems, e.g., text removal,
moving object detection, image alignment and inpainting, and show that our
methods usually outperform the state-of-the-art methods.
Energy Flow Networks: Deep Sets for Particle Jets
Authors: Patrick T. Komiske, Eric M. Metodiev, Jesse Thaler
Date: 2018-10-11 00:00:00
Tasks: BIG-bench Machine Learning

A key question for machine learning approaches in particle physics is how to
best represent and learn from collider events. As an event is intrinsically a
variable-length unordered set of particles, we build upon recent machine
learning efforts to learn directly from sets of features or "point clouds".
Adapting and specializing the "Deep Sets" framework to particle physics, we
introduce Energy Flow Networks, which respect infrared and collinear safety by
construction. We also develop Particle Flow Networks, which allow for general
energy dependence and the inclusion of additional particle-level information
such as charge and flavor. These networks feature a per-particle internal
(latent) representation, and summing over all particles yields an overall
event-level latent representation. We show how this latent space decomposition
unifies existing event representations based on detector images and radiation
moments. To demonstrate the power and simplicity of this set-based approach, we
apply these networks to the collider task of discriminating quark jets from
gluon jets, finding similar or improved performance compared to existing
methods. We also show how the learned event representation can be directly
visualized, providing insight into the inner workings of the model. These
architectures lend themselves to efficiently processing and analyzing events
for a wide variety of tasks at the Large Hadron Collider. Implementations and
examples of our architectures are available online in our EnergyFlow package.
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis
Authors: Mohammad Amin Nabian, Hadi Meidani
Date: 2018-10-11 00:00:00

In this paper, we introduce a physics-driven regularization method for training of deep neural networks (DNNs) for use in engineering design and analysis problems. In particular, we focus on prediction of a physical system, for which in addition to training data, partial or complete information on a set of governing laws is also available. These laws often appear in the form of differential equations, derived from first principles, empirically-validated laws, or domain expertise, and are usually neglected in data-driven prediction of engineering systems. We propose a training approach that utilizes the known governing laws and regularizes data-driven DNN models by penalizing divergence from those laws. The first two numerical examples are synthetic examples, where we show that in constructing a DNN model that best fits the measurements from a physical system, the use of our proposed regularization results in DNNs that are more interpretable with smaller generalization errors, compared to other common regularization methods. The last two examples concern metamodeling for a random Burgers' system and for aerodynamic analysis of passenger vehicles, where we demonstrate that the proposed regularization provides superior generalization accuracy compared to other common alternatives.