pwc-archive/papers-with-abstracts huggingface dataset:
Emotional Contribution Analysis of Online Reviews
Authors: Elisa Claire Alemán Carreón, Hirofumi Nonaka, Toru Hiraoka, Minoru Kumano, Takao Ito, Masaharu Hirota
Date: 2019-05-01 00:00:00
Tasks: BIG-bench Machine Learning

In response to the constant increase in population and tourism worldwide,
there is a need for the development of cross-language market research tools
that are more cost and time effective than surveys or interviews. Focusing on
the Chinese tourism boom and the hotel industry in Japan, we extracted the most
influential keywords in emotional judgement from Chinese online reviews of
Japanese hotels in the portal site Ctrip. Using an entropy based mathematical
model and a machine learning algorithm, we determined the words that most
closely represent the demands and emotions of this customer base.
Dropping Pixels for Adversarial Robustness
Authors: Hossein Hosseini, Sreeram Kannan, Radha Poovendran
Date: 2019-05-01 00:00:00
Tasks: Adversarial Robustness

Deep neural networks are vulnerable against adversarial examples. In this
paper, we propose to train and test the networks with randomly subsampled
images with high drop rates. We show that this approach significantly improves
robustness against adversarial examples in all cases of bounded L0, L2 and
L_inf perturbations, while reducing the standard accuracy by a small value. We
argue that subsampling pixels can be thought to provide a set of robust
features for the input image and, thus, improves robustness without performing
adversarial training.
Boosting: Why You Can Use the HP Filter
Authors: Peter C. B. Phillips, Zhentao Shi
Date: 2019-05-01 00:00:00
Tasks: Time Series Analysis

The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning. As recent research (Phillips and Jin, 2015) has shown, standard settings may not be adequate in removing trends, particularly stochastic trends, in economic data. This paper proposes an easy-to-implement practical procedure of iterating the HP smoother that is intended to make the filter a smarter smoothing device for trend estimation and trend elimination. We call this iterated HP technique the boosted HP filter in view of its connection to $L_{2}$-boosting in machine learning. The paper develops limit theory to show that the boosted HP (bHP) filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks. A stopping criterion is used to automate the iterative HP algorithm, making it a data-determined method that is ready for modern data-rich environments in economic research. The methodology is illustrated using three real data examples that highlight the differences between simple HP filtering, the data-determined boosted filter, and an alternative autoregressive approach. These examples show that the bHP filter is helpful in analyzing a large collection of heterogeneous macroeconomic time series that manifest various degrees of persistence, trend behavior, and volatility.
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
Authors: Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão, Christian Gagné
Date: 2019-05-01 00:00:00
Tasks: Autonomous Driving, Decision Making, Lesion Detection, Medical Diagnosis

The great performances of deep learning are undeniable, with impressive results over a wide range of tasks. However, the output confidence of these models is usually not well-calibrated, which can be an issue for applications where confidence on the decisions is central to providing trust and reliability (e.g., autonomous driving or medical diagnosis). For models using softmax at the last layer, Temperature Scaling (TS) is a state-of-the-art calibration method, with low time and memory complexity as well as demonstrated effectiveness. TS relies on a T parameter to rescale and calibrate values of the softmax layer, whose parameter value is computed from a labelled dataset. We are proposing an Unsupervised Temperature Scaling (UTS) approach, which does not depend on labelled samples to calibrate the model, which allows, for example, the use of a part of a test samples to calibrate the pre-trained model before going into inference mode. We provide theoretical justifications for UTS and assess its effectiveness on a wide range of deep models and datasets. We also demonstrate calibration results of UTS on skin lesion detection, a problem where a well-calibrated output can play an important role for accurate decision-making.
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
Authors: Braden Hurl, Krzysztof Czarnecki, Steven Waslander
Date: 2019-05-01 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection, Semantic Segmentation

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy
Towards Sampling from Nondirected Probabilistic Graphical models using a D-Wave Quantum Annealer
Authors: Yaroslav Koshka, M. A. Novotny
Date: 2019-05-01 00:00:00
Tasks: General Classification, Missing Labels, Playing the Game of 2048

A D-Wave quantum annealer (QA) having a 2048 qubit lattice, with no missing
qubits and couplings, allowed embedding of a complete graph of a Restricted
Boltzmann Machine (RBM). A handwritten digit OptDigits data set having 8x7
pixels of visible units was used to train the RBM using a classical Contrastive
Divergence. Embedding of the classically-trained RBM into the D-Wave lattice
was used to demonstrate that the QA offers a high-efficiency alternative to the
classical Markov Chain Monte Carlo (MCMC) for reconstructing missing labels of
the test images as well as a generative model. At any training iteration, the
D-Wave-based classification had classification error more than two times lower
than MCMC. The main goal of this study was to investigate the quality of the
sample from the RBM model distribution and its comparison to a classical MCMC
sample. For the OptDigits dataset, the states in the D-Wave sample belonged to
about two times more local valleys compared to the MCMC sample. All the
lowest-energy (the highest joint probability) local minima in the MCMC sample
were also found by the D-Wave. The D-Wave missed many of the higher-energy
local valleys, while finding many "new" local valleys consistently missed by
the MCMC. It was established that the "new" local valleys that the D-Wave finds
are important for the model distribution in terms of the energy of the
corresponding local minima, the width of the local valleys, and the height of
the escape barrier.
On Scalable and Efficient Computation of Large Scale Optimal Transport
Authors: Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha
Date: 2019-05-01 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Domain Adaptation

Optimal Transport (OT) naturally arises in many machine learning applications, yet the heavy computational burden limits its wide-spread uses. To address the scalability issue, we propose an implicit generative learning-based framework called SPOT (Scalable Push-forward of Optimal Transport). Specifically, we approximate the optimal transport plan by a pushforward of a reference distribution, and cast the optimal transport problem into a minimax problem. We then can solve OT problems efficiently using primal dual stochastic gradient-type algorithms. We also show that we can recover the density of the optimal transport plan using neural ordinary differential equations. Numerical experiments on both synthetic and real datasets illustrate that SPOT is robust and has favorable convergence behavior. SPOT also allows us to efficiently sample from the optimal transport plan, which benefits downstream applications such as domain adaptation.
Self-Supervised Convolutional Subspace Clustering Network
Authors: Junjian Zhang, Chun-Guang Li, Chong You, Xianbiao Qi, Honggang Zhang, Jun Guo, Zhouchen Lin
Date: 2019-05-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Image Clustering

Subspace clustering methods based on data self-expression have become very
popular for learning from data that lie in a union of low-dimensional linear
subspaces. However, the applicability of subspace clustering has been limited
because practical visual data in raw form do not necessarily lie in such linear
subspaces. On the other hand, while Convolutional Neural Network (ConvNet) has
been demonstrated to be a powerful tool for extracting discriminative features
from visual data, training such a ConvNet usually requires a large amount of
labeled data, which are unavailable in subspace clustering applications. To
achieve simultaneous feature learning and subspace clustering, we propose an
end-to-end trainable framework, called Self-Supervised Convolutional Subspace
Clustering Network (S$^2$ConvSCN), that combines a ConvNet module (for feature
learning), a self-expression module (for subspace clustering) and a spectral
clustering module (for self-supervision) into a joint optimization framework.
Particularly, we introduce a dual self-supervision that exploits the output of
spectral clustering to supervise the training of the feature learning module
(via a classification loss) and the self-expression module (via a spectral
clustering loss). Our experiments on four benchmark datasets show the
effectiveness of the dual self-supervision and demonstrate superior performance
of our proposed approach.
Fair Classification and Social Welfare
Authors: Lily Hu, Yi-Ling Chen
Date: 2019-05-01 00:00:00
Tasks: Classification, Fairness, General Classification

Now that machine learning algorithms lie at the center of many resource
allocation pipelines, computer scientists have been unwittingly cast as partial
social planners. Given this state of affairs, important questions follow. What
is the relationship between fairness as defined by computer scientists and
notions of social welfare? In this paper, we present a welfare-based analysis
of classification and fairness regimes. We translate a loss minimization
program into a social welfare maximization problem with a set of implied
welfare weights on individuals and groups--weights that can be analyzed from a
distribution justice lens. In the converse direction, we ask what the space of
possible labelings is for a given dataset and hypothesis class. We provide an
algorithm that answers this question with respect to linear hyperplanes in
$\mathbb{R}^d$ that runs in $O(n^dd)$. Our main findings on the relationship
between fairness criteria and welfare center on sensitivity analyses of
fairness-constrained empirical risk minimization programs. We characterize the
ranges of $\Delta \epsilon$ perturbations to a fairness parameter $\epsilon$
that yield better, worse, and neutral outcomes in utility for individuals and
by extension, groups. We show that applying more strict fairness criteria that
are codified as parity constraints, can worsen welfare outcomes for both
groups. More generally, always preferring "more fair" classifiers does not
abide by the Pareto Principle---a fundamental axiom of social choice theory and
welfare economics. Recent work in machine learning has rallied around these
notions of fairness as critical to ensuring that algorithmic systems do not
have disparate negative impact on disadvantaged social groups. By showing that
these constraints often fail to translate into improved outcomes for these
groups, we cast doubt on their effectiveness as a means to ensure justice.
ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
Authors: Xiaolong Ma, Geng Yuan, Sheng Lin, Zhengang Li, Hao Sun, Yanzhi Wang
Date: 2019-04-30 00:00:00

The state-of-art DNN structures involve high computation and great demand for
memory storage which pose intensive challenge on DNN framework resources. To
mitigate the challenges, weight pruning techniques has been studied. However,
high accuracy solution for extreme structured pruning that combines different
types of structured sparsity still waiting for unraveling due to the extremely
reduced weights in DNN networks. In this paper, we propose a DNN framework
which combines two different types of structured weight pruning (filter and
column prune) by incorporating alternating direction method of multipliers
(ADMM) algorithm for better prune performance. We are the first to find
non-optimality of ADMM process and unused weights in a structured pruned model,
and further design an optimization framework which contains the first proposed
Network Purification and Unused Path Removal algorithms which are dedicated to
post-processing an structured pruned model after ADMM steps. Some high lights
shows we achieve 232x compression on LeNet-5, 60x compression on ResNet-18
CIFAR-10 and over 5x compression on AlexNet. We share our models at anonymous
link http://bit.ly/2VJ5ktv.
Harmonic Networks with Limited Training Samples
Authors: Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Date: 2019-04-30 00:00:00
Tasks: Image Classification

Convolutional neural networks (CNNs) are very popular nowadays for image
processing. CNNs allow one to learn optimal filters in a (mostly) supervised
machine learning context. However this typically requires abundant labelled
training data to estimate the filter parameters. Alternative strategies have
been deployed for reducing the number of parameters and / or filters to be
learned and thus decrease overfitting. In the context of reverting to preset
filters, we propose here a computationally efficient harmonic block that uses
Discrete Cosine Transform (DCT) filters in CNNs. In this work we examine the
performance of harmonic networks in limited training data scenario. We validate
experimentally that its performance compares well against scattering networks
that use wavelets as preset filters.
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)
Authors: Bhanu Pratap Singh, Iman Deznabi, Bharath Narasimhan, Bryon Kucharski, Rheeya Uppaal, Akhila Josyula, Madalina Fiterau
Date: 2019-04-30 00:00:00
Tasks: Imputation, Irregular Time Series, Missing Values, Time Series, Time Series Analysis

Missing values, irregularly collected samples, and multi-resolution signals
commonly occur in multivariate time series data, making predictive tasks
difficult. These challenges are especially prevalent in the healthcare domain,
where patients' vital signs and electronic records are collected at different
frequencies and have occasionally missing information due to the imperfections
in equipment or patient circumstances. Researchers have handled each of these
issues differently, often handling missing data through mean value imputation
and then using sequence models over the multivariate signals while ignoring the
different resolution of signals. We propose a unified model named
Multi-resolution Flexible Irregular Time series Network (Multi-FIT). The
building block for Multi-FIT is the FIT network. The FIT network creates an
informative dense representation at each time step using signal information
such as last observed value, time difference since the last observed time stamp
and overall mean for the signal. Vertical FIT (FIT-V) is a variant of FIT which
also models the relationship between different temporal signals while creating
the informative dense representations for the signal. The multi-FIT model uses
multiple FIT networks for sets of signals with different resolutions, further
facilitating the construction of flexible representations. Our model has three
main contributions: a.) it does not impute values but rather creates
informative representations to provide flexibility to the model for creating
task-specific representations b.) it models the relationship between different
signals in the form of support signals c.) it models different resolutions in
parallel before merging them for the final prediction task. The FIT, FIT-V and
Multi-FIT networks improve upon the state-of-the-art models for three
predictive tasks, including the forecasting of patient survival.
Source Coding Based Millimeter-Wave Channel Estimation with Deep Learning Based Decoding
Authors: Yahia Shabara, Eylem Ekici, C. Emre Koksal
Date: 2019-04-30 00:00:00
Tasks: compressed sensing

The speed at which millimeter-Wave (mmWave) channel estimation can be carried out is critical for the adoption of mmWave technologies. This is particularly crucial because mmWave transceivers are equipped with large antenna arrays to combat severe path losses, which consequently creates large channel matrices, whose estimation may incur significant overhead. This paper focuses on the mmWave channel estimation problem. Our objective is to reduce the number of measurements required to reliably estimate the channel. Specifically, channel estimation is posed as a "source compression" problem in which measurements mimic an encoded (compressed) version of the channel. Decoding the observed measurements, a task which is traditionally computationally intensive, is performed using a deep-learning-based approach, facilitating a high-performance channel discovery. Our solution not only outperforms state-of-the-art compressed sensing methods, but it also determines the lower bound on the number of measurements required for reliable channel discovery.
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis
Authors: Li Chen, Carter Yagemann, Evan Downing
Date: 2019-04-30 00:00:00
Tasks: Classification, General Classification, Malware Analysis, Malware Classification, Transfer Learning

Converting malware into images followed by vision-based deep learning
algorithms has shown superior threat detection efficacy compared with classical
machine learning algorithms. When malware are visualized as images,
visual-based interpretation schemes can also be applied to extract insights of
why individual samples are classified as malicious. In this work, via two case
studies of dynamic malware classification, we extend the local interpretable
model-agnostic explanation algorithm to explain image-based dynamic malware
classification and examine its interpretation fidelity. For both case studies,
we first train deep learning models via transfer learning on malware images,
demonstrate high classification effectiveness, apply an explanation method on
the images, and correlate the results back to the samples to validate whether
the algorithmic insights are consistent with security domain expertise. In our
first case study, the interpretation framework identifies indirect calls that
uniquely characterize the underlying exploit behavior of a malware family. In
our second case study, the interpretation framework extracts insightful
information such as cryptography-related APIs when applied on images created
from API existence, but generate ambiguous interpretation on images created
from API sequences and frequencies. Our findings indicate that current
image-based interpretation techniques are promising for explaining vision-based
malware classification. We continue to develop image-based interpretation
schemes specifically for security applications.
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
Authors: Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes
Date: 2019-04-30 00:00:00
Tasks: CPU, GPU, Neural Architecture Search

AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for
automatically learning high-quality ensembles with minimal expert intervention.
Our framework is inspired by the AdaNet algorithm (Cortes et al., 2017) which
learns the structure of a neural network as an ensemble of subnetworks. We
designed it to: (1) integrate with the existing TensorFlow ecosystem, (2) offer
sensible default search spaces to perform well on novel datasets, (3) present a
flexible API to utilize expert information when available, and (4) efficiently
accelerate training with distributed CPU, GPU, and TPU hardware. The code is
open-source and available at: https://github.com/tensorflow/adanet.
FastContext: an efficient and scalable implementation of the ConText algorithm
Authors: Jianlin Shi, John F. Hurdle
Date: 2019-04-30 00:00:00
Tasks: Negation

Objective: To develop and evaluate FastContext, an efficient, scalable
implementation of the ConText algorithm suitable for very large-scale clinical
natural language processing. Background: The ConText algorithm performs with
state-of-art accuracy in detecting the experiencer, negation status, and
temporality of concept mentions in clinical narratives. However, the speed
limitation of its current implementations hinders its use in big data
processing. Methods: We developed FastContext through hashing the ConText's
rules, then compared its speed and accuracy with JavaConText and
GeneralConText, two widely used Java implementations. Results: FastContext ran
two orders of magnitude faster and was less decelerated by rule increase than
the other two implementations used in this study for comparison. Additionally,
FastContext consistently gained accuracy improvement as the rules increased
(the desired outcome of adding new rules), while the other two implementations
did not. Conclusions: FastContext is an efficient, scalable implementation of
the popular ConText algorithm, suitable for natural language applications on
very large clinical corpora.