pwc-archive/papers-with-abstracts huggingface dataset:
New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling
Authors: Ian Goodfellow
Date: 2018-11-08 00:00:00
Tasks: Adversarial Robustness

This technical report describes a new feature of the CleverHans library
called "attack bundling". Many papers about adversarial examples present lists
of error rates corresponding to different attack algorithms. A common approach
is to take the maximum across this list and compare defenses against that error
rate. We argue that a better approach is to use attack bundling: the max should
be taken across many examples at the level of individual examples, then the
error rate should be calculated by averaging after this maximization operation.
Reporting the bundled attacker error rate provides a lower bound on the true
worst-case error rate. The traditional approach of reporting the maximum error
rate across attacks can underestimate the true worst-case error rate by an
amount approaching 100\% as the number of attacks approaches infinity. Attack
bundling can be used with different prioritization schemes to optimize
quantities such as error rate on adversarial examples, perturbation size needed
to cause misclassification, or failure rate when using a specific confidence
threshold.
Finding and Following of Honeycombing Regions in Computed Tomography Lung Images by Deep Learning
Authors: Emre Eğriboz, Furkan Kaynar, Songül Varlı Albayrak, Benan Müsellim, Tuba Selçuk
Date: 2018-10-31 00:00:00
Tasks: Computed Tomography (CT), Decision Making

In recent years, besides the medical treatment methods in medical field,
Computer Aided Diagnosis (CAD) systems which can facilitate the decision making
phase of the physician and can detect the disease at an early stage have
started to be used frequently. The diagnosis of Idiopathic Pulmonary Fibrosis
(IPF) disease by using CAD systems is very important in that it can be followed
by doctors and radiologists. It has become possible to diagnose and follow up
the disease with the help of CAD systems by the development of high resolution
computed imaging scanners and increasing size of computation power. The purpose
of this project is to design a tool that will help specialists diagnose and
follow up the IPF disease by identifying areas of honeycombing and ground glass
patterns in High Resolution Computed Tomography (HRCT) lung images. Creating a
program module that segments the lung pair and creating a self-learner deep
learning model from given Computed Tomography (CT) images for the specific
diseased regions thanks to doctors are the main purposes of this work. Through
the created model, program module will be able to find special regions in given
new CT images. In this study, the performance of lung segmentation was tested
by the S{\o}rensen-Dice coefficient method and the mean performance was
measured as 90.7%, testing of the created model was performed with data not
used in the training stage of the CNN network, and the average performance was
measured as 87.8% for healthy regions, 73.3% for ground-glass areas and 69.1%
for honeycombing zones.
Gender Effect on Face Recognition for a Large Longitudinal Database
Authors: Caroline Werther, Morgan Ferguson, Kevin Park, Troy Kling, Cuixian Chen, Yishi Wang
Date: 2018-11-08 00:00:00
Tasks: Face Recognition, MORPH

Aging or gender variation can affect the face recognition performance
dramatically. While most of the face recognition studies are focused on the
variation of pose, illumination and expression, it is important to consider the
influence of gender effect and how to design an effective matching framework.
In this paper, we address these problems on a very large longitudinal database
MORPH-II which contains 55,134 face images of 13,617 individuals. First, we
consider four comprehensive experiments with different combination of gender
distribution and subset size, including: 1) equal gender distribution; 2) a
large highly unbalanced gender distribution; 3) consider different gender
combinations, such as male only, female only, or mixed gender; and 4) the
effect of subset size in terms of number of individuals. Second, we consider
eight nearest neighbor distance metrics and also Support Vector Machine (SVM)
for classifiers and test the effect of different classifiers. Last, we consider
different fusion techniques for an effective matching framework to improve the
recognition performance.
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods
Authors: Samuel Kessler, Arnold Salas, Vincent W. C. Tan, Stefan Zohren, Stephen Roberts
Date: 2018-11-08 00:00:00
Tasks: Multi-Armed Bandits, Thompson Sampling

We introduce a novel framework for the estimation of the posterior distribution over the weights of a neural network, based on a new probabilistic interpretation of adaptive optimisation algorithms such as AdaGrad and Adam. We demonstrate the effectiveness of our Bayesian Adam method, Badam, by experimentally showing that the learnt uncertainties correctly relate to the weights' predictive capabilities by weight pruning. We also demonstrate the quality of the derived uncertainty measures by comparing the performance of Badam to standard methods in a Thompson sampling setting for multi-armed bandits, where good uncertainty measures are required for an agent to balance exploration and exploitation.
Statistical Characteristics of Deep Representations: An Empirical Investigation
Authors: Daeyoung Choi, Kyungeun Lee, Duhun Hwang, Wonjong Rhee
Date: 2018-11-08 00:00:00

In this study, the effects of eight representation regularization methods are investigated, including two newly developed rank regularizers (RR). The investigation shows that the statistical characteristics of representations such as correlation, sparsity, and rank can be manipulated as intended, during training. Furthermore, it is possible to improve the baseline performance simply by trying all the representation regularizers and fine-tuning the strength of their effects. In contrast to performance improvement, no consistent relationship between performance and statistical characteristics was observable. The results indicate that manipulation of statistical characteristics can be helpful for improving performance, but only indirectly through its influence on learning dynamics or its tuning effects.
Plug-In Stochastic Gradient Method
Authors: Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov
Date: 2018-11-08 00:00:00

Plug-and-play priors (PnP) is a popular framework for regularized signal
reconstruction by using advanced denoisers within an iterative algorithm. In
this paper, we discuss our recent online variant of PnP that uses only a subset
of measurements at every iteration, which makes it scalable to very large
datasets. We additionally present novel convergence results for both batch and
online PnP algorithms.
How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness
Authors: Nripsuta Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David Parkes, Yang Liu
Date: 2018-11-08 00:00:00
Tasks: Fairness

What is the best way to define algorithmic fairness? While many definitions
of fairness have been proposed in the computer science literature, there is no
clear agreement over a particular definition. In this work, we investigate
ordinary people's perceptions of three of these fairness definitions. Across
two online experiments, we test which definitions people perceive to be the
fairest in the context of loan decisions, and whether fairness perceptions
change with the addition of sensitive information (i.e., race of the loan
applicants). Overall, one definition (calibrated fairness) tends to be more
preferred than the others, and the results also provide support for the
principle of affirmative action.
Stovepiping and Malicious Software: A Critical Review of AGI Containment
Authors: Jason M. Pittman, Jesus P. Espinoza, Courtney Crosby
Date: 2018-11-08 00:00:00

Awareness of the possible impacts associated with artificial intelligence has risen in proportion to progress in the field. While there are tremendous benefits to society, many argue that there are just as many, if not more, concerns related to advanced forms of artificial intelligence. Accordingly, research into methods to develop artificial intelligence safely is increasingly important. In this paper, we provide an overview of one such safety paradigm: containment with a critical lens aimed toward generative adversarial networks and potentially malicious artificial intelligence. Additionally, we illuminate the potential for a developmental blindspot in the stovepiping of containment mechanisms.
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Authors: Wenting Xiong, Iftitahu Ni'mah, Juan M. G. Huesca, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy
Date: 2018-11-08 00:00:00
Tasks: Sentence, text-classification, Text Classification

Layer-wise Relevance Propagation (LRP) and saliency maps have been recently
used to explain the predictions of Deep Learning models, specifically in the
domain of text classification. Given different attribution-based explanations
to highlight relevant words for a predicted class label, experiments based on
word deleting perturbation is a common evaluation method. This word removal
approach, however, disregards any linguistic dependencies that may exist
between words or phrases in a sentence, which could semantically guide a
classifier to a particular prediction. In this paper, we present a
feature-based evaluation framework for comparing the two attribution methods on
customer reviews (public data sets) and Customer Due Diligence (CDD) extracted
reports (corporate data set). Instead of removing words based on the relevance
score, we investigate perturbations based on embedded features removal from
intermediate layers of Convolutional Neural Networks. Our experimental study is
carried out on embedded-word, embedded-document, and embedded-ngrams
explanations. Using the proposed framework, we provide a visualization tool to
assist analysts in reasoning toward the model's final prediction.
The Evolution of Gene Dominance through the Baldwin Effect
Authors: Larry Bull
Date: 2018-11-08 00:00:00

It has recently been suggested that the fundamental haploid-diploid cycle of
eukaryotic sex exploits a rudimentary form of the Baldwin effect. Thereafter
the other associated phenomena can be explained as evolution tuning the amount
and frequency of learning experienced by an organism. Using the well-known NK
model of fitness landscapes it is here shown that the emergence of dominance
can also be explained under this view of eukaryotic evolution.
Satyam: Democratizing Groundtruth for Machine Vision
Authors: Hang Qiu, Krishna Chintalapudi, Ramesh Govindan
Date: 2018-11-08 00:00:00
Tasks: Autonomous Driving

The democratization of machine learning (ML) has led to ML-based machine
vision systems for autonomous driving, traffic monitoring, and video
surveillance. However, true democratization cannot be achieved without greatly
simplifying the process of collecting groundtruth for training and testing
these systems. This groundtruth collection is necessary to ensure good
performance under varying conditions. In this paper, we present the design and
evaluation of Satyam, a first-of-its-kind system that enables a layperson to
launch groundtruth collection tasks for machine vision with minimal effort.
Satyam leverages a crowdtasking platform, Amazon Mechanical Turk, and automates
several challenging aspects of groundtruth collection: creating and launching
of custom web-UI tasks for obtaining the desired groundtruth, controlling
result quality in the face of spammers and untrained workers, adapting prices
to match task complexity, filtering spammers and workers with poor performance,
and processing worker payments. We validate Satyam using several popular
benchmark vision datasets, and demonstrate that groundtruth obtained by Satyam
is comparable to that obtained from trained experts and provides matching ML
performance when used for training.
Election with Bribed Voter Uncertainty: Hardness and Approximation Algorithm
Authors: Lin Chen, Lei Xu, Shouhuai Xu, Zhimin Gao, Weidong Shi
Date: 2018-11-07 00:00:00

Bribery in election (or computational social choice in general) is an
important problem that has received a considerable amount of attention. In the
classic bribery problem, the briber (or attacker) bribes some voters in
attempting to make the briber's designated candidate win an election. In this
paper, we introduce a novel variant of the bribery problem, "Election with
Bribed Voter Uncertainty" or BVU for short, accommodating the uncertainty that
the vote of a bribed voter may or may not be counted. This uncertainty occurs
either because a bribed voter may not cast its vote in fear of being caught, or
because a bribed voter is indeed caught and therefore its vote is discarded. As
a first step towards ultimately understanding and addressing this important
problem, we show that it does not admit any multiplicative $O(1)$-approximation
algorithm modulo standard complexity assumptions. We further show that there is
an approximation algorithm that returns a solution with an additive-$\epsilon$
error in FPT time for any fixed $\epsilon$.
A Fully Time-domain Neural Model for Subband-based Speech Synthesizer
Authors: Azam Rabiee, Soo-Young Lee
Date: 2018-10-12 00:00:00
Tasks: text-to-speech, Text to Speech

This paper introduces a deep neural network model for subband-based speech
synthesizer. The model benefits from the short bandwidth of the subband signals
to reduce the complexity of the time-domain speech generator. We employed the
multi-level wavelet analysis/synthesis to decompose/reconstruct the signal to
subbands in time domain. Inspired from the WaveNet, a convolutional neural
network (CNN) model predicts subband speech signals fully in time domain. Due
to the short bandwidth of the subbands, a simple network architecture is enough
to train the simple patterns of the subbands accurately. In the ground truth
experiments with teacher forcing, the subband synthesizer outperforms the
fullband model significantly. In addition, by conditioning the model on the
phoneme sequence using a pronunciation dictionary, we have achieved the first
fully time-domain neural text-to-speech (TTS) system. The generated speech of
the subband TTS shows comparable quality as the fullband one with a slighter
network architecture for each subband.
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Authors: Zeyuan Allen-Zhu, Yuanzhi Li, YIngyu Liang
Date: 2018-11-12 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Learning Theory, Vocal Bursts Valence Prediction

The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn? Why doesn't the trained network overfit when it is overparameterized? In this work, we prove that overparameterized neural networks can learn some notable concept classes, including two and three-layer networks with fewer parameters and smooth activations. Moreover, the learning can be simply done by SGD (stochastic gradient descent) or its variants in polynomial time using polynomially many samples. The sample complexity can also be almost independent of the number of parameters in the network. On the technique side, our analysis goes beyond the so-called NTK (neural tangent kernel) linearization of neural networks in prior works. We establish a new notion of quadratic approximation of the neural network (that can be viewed as a second-order variant of NTK), and connect it to the SGD theory of escaping saddle points.
Boosting Model Performance through Differentially Private Model Aggregation
Authors: Sophia Collet, Robert Dadashi, Zahi N. Karam, Chang Liu, Parinaz Sobhani, Yevgeniy Vahlis, Ji Chao Zhang
Date: 2018-11-12 00:00:00
Tasks: model

A key factor in developing high performing machine learning models is the
availability of sufficiently large datasets. This work is motivated by
applications arising in Software as a Service (SaaS) companies where there
exist numerous similar yet disjoint datasets from multiple client companies. To
overcome the challenges of insufficient data without explicitly aggregating the
clients' datasets due to privacy concerns, one solution is to collect more data
for each individual client, another is to privately aggregate information from
models trained on each client's data. In this work, two approaches for private
model aggregation are proposed that enable the transfer of knowledge from
existing models trained on other companies' datasets to a new company with
limited labeled data while protecting each client company's underlying
individual sensitive information. The two proposed approaches are based on
state-of-the-art private learning algorithms: Differentially Private
Permutation-based Stochastic Gradient Descent and Approximate Minima
Perturbation. We empirically show that by leveraging differentially private
techniques, we can enable private model aggregation and augment data utility
while providing provable mathematical guarantees on privacy. The proposed
methods thus provide significant business value for SaaS companies and their
clients, specifically as a solution for the cold-start problem.
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Authors: Yannick Suter, Alain Jungo, Michael Rebsamen, Urspeter Knecht, Evelyn Herrmann, Roland Wiest, Mauricio Reyes
Date: 2018-11-12 00:00:00
Tasks: Deep Learning, regression, Survival Prediction

Deep learning for regression tasks on medical imaging data has shown
promising results. However, compared to other approaches, their power is
strongly linked to the dataset size. In this study, we evaluate
3D-convolutional neural networks (CNNs) and classical regression methods with
hand-crafted features for survival time regression of patients with high grade
brain tumors. The tested CNNs for regression showed promising but unstable
results. The best performing deep learning approach reached an accuracy of
51.5% on held-out samples of the training set. All tested deep learning
experiments were outperformed by a Support Vector Classifier (SVC) using 30
radiomic features. The investigated features included intensity, shape,
location and deep features. The submitted method to the BraTS 2018 survival
prediction challenge is an ensemble of SVCs, which reached a cross-validated
accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set,
and 42.9% on the testing set. The results suggest that more training data is
necessary for a stable performance of a CNN model for direct regression from
magnetic resonance images, and that non-imaging clinical patient information is
crucial along with imaging information.
Stream attention-based multi-array end-to-end speech recognition
Authors: Xiaofei Wang, Ruizhi Li, Sri Harish Mallid, Takaaki Hori, Shinji Watanabe, Hynek Hermansky
Date: 2018-11-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Automatic Speech Recognition (ASR) using multiple microphone arrays has
achieved great success in the far-field robustness. Taking advantage of all the
information that each array shares and contributes is crucial in this task.
Motivated by the advances of joint Connectionist Temporal Classification
(CTC)/attention mechanism in the End-to-End (E2E) ASR, a stream attention-based
multi-array framework is proposed in this work. Microphone arrays, acting as
information streams, are activated by separate encoders and decoded under the
instruction of both CTC and attention networks. In terms of attention, a
hierarchical structure is adopted. On top of the regular attention networks,
stream attention is introduced to steer the decoder toward the most informative
encoders. Experiments have been conducted on AMI and DIRHA multi-array corpora
using the encoder-decoder architecture. Compared with the best single-array
results, the proposed framework has achieved relative Word Error Rates (WERs)
reduction of 3.7% and 9.7% in the two datasets, respectively, which is better
than conventional strategies as well.
Multi-encoder multi-resolution framework for end-to-end speech recognition
Authors: Ruizhi Li, Xiaofei Wang, Sri Harish Mallidi, Takaaki Hori, Shinji Watanabe, Hynek Hermansky
Date: 2018-11-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Attention-based methods and Connectionist Temporal Classification (CTC)
network have been promising research directions for end-to-end Automatic Speech
Recognition (ASR). The joint CTC/Attention model has achieved great success by
utilizing both architectures during multi-task training and joint decoding. In
this work, we present a novel Multi-Encoder Multi-Resolution (MEMR) framework
based on the joint CTC/Attention model. Two heterogeneous encoders with
different architectures, temporal resolutions and separate CTC networks work in
parallel to extract complimentary acoustic information. A hierarchical
attention mechanism is then used to combine the encoder-level information. To
demonstrate the effectiveness of the proposed model, experiments are conducted
on Wall Street Journal (WSJ) and CHiME-4, resulting in relative Word Error Rate
(WER) reduction of 18.0-32.1%. Moreover, the proposed MEMR model achieves 3.6%
WER in the WSJ eval92 test set, which is the best WER reported for an
end-to-end system on this benchmark.
TED: Teaching AI to Explain its Decisions
Authors: Michael Hind, Dennis Wei, Murray Campbell, Noel C. F. Codella, Amit Dhurandhar, Aleksandra Mojsilović, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
Date: 2018-11-12 00:00:00
Tasks: Fairness

Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions (TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.
Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery
Authors: Ritwik Gupta, Carson D. Sestili, Javier A. Vazquez-Trejo, Matthew E. Gaston
Date: 2018-11-12 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification

Deep learning tasks are often complicated and require a variety of components
working together efficiently to perform well. Due to the often large scale of
these tasks, there is a necessity to iterate quickly in order to attempt a
variety of methods and to find and fix bugs. While participating in IARPA's
Functional Map of the World challenge, we identified challenges along the
entire deep learning pipeline and found various solutions to these challenges.
In this paper, we present the performance, engineering, and deep learning
considerations with processing and modeling data, as well as underlying
infrastructure considerations that support large-scale deep learning tasks. We
also discuss insights and observations with regard to satellite imagery and
deep learning for image classification.