pwc-archive/papers-with-abstracts huggingface dataset:
Improved Search Strategies with Application to Estimating Facial Blendshape Parameters
Authors: Michael Bao, David Hyde, Xinru Hua, Ronald Fedkiw
Date: 2018-12-07 00:00:00

It is well known that popular optimization techniques can lead to overfitting or even a lack of convergence altogether; thus, practitioners often utilize ad hoc regularization terms added to the energy functional. When carefully crafted, these regularizations can produce compelling results. However, regularization changes both the energy landscape and the solution to the optimization problem, which can result in underfitting. Surprisingly, many practitioners both add regularization and claim that their model lacks the expressivity to fit the data. Motivated by a geometric interpretation of the linearized search space, we propose an approach that ameliorates overfitting without the need for regularization terms that restrict the expressiveness of the underlying model. We illustrate the efficacy of our approach on minimization problems related to three-dimensional facial expression estimation where overfitting clouds semantic understanding and regularization may lead to underfitting that misses or misinterprets subtle expressions.
Star Tracking using an Event Camera
Authors: Tat-Jun Chin, Samya Bagchi, Anders Eriksson, Andre van Schaik
Date: 2018-12-07 00:00:00

Star trackers are primarily optical devices that are used to estimate the
attitude of a spacecraft by recognising and tracking star patterns. Currently,
most star trackers use conventional optical sensors. In this application paper,
we propose the usage of event sensors for star tracking. There are potentially
two benefits of using event sensors for star tracking: lower power consumption
and higher operating speeds. Our main contribution is to formulate an
algorithmic pipeline for star tracking from event data that includes novel
formulations of rotation averaging and bundle adjustment. In addition, we also
release with this paper a dataset for star tracking using event cameras. With
this work, we introduce the problem of star tracking using event cameras to the
computer vision community, whose expertise in SLAM and geometric optimisation
can be brought to bear on this commercially important application.
The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
Authors: Yang-Hui He
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning

We present a pedagogical introduction to the recent advances in the computational geometry, physical implications, and data science of Calabi-Yau manifolds. Aimed at the beginning research student and using Calabi-Yau spaces as an exciting play-ground, we intend to teach some mathematics to the budding physicist, some physics to the budding mathematician, and some machine-learning to both. Based on various lecture series, colloquia and seminars given by the author in the past year, this writing is a very preliminary draft of a book to appear with Springer, by whose kind permission we post to ArXiv for comments and suggestions.
Adversarial Defense of Image Classification Using a Variational Auto-Encoder
Authors: Yi Luo, Henry Pfister
Date: 2018-12-07 00:00:00
Tasks: Adversarial Defense, General Classification, image-classification, Image Classification

Deep neural networks are known to be vulnerable to adversarial attacks. This
exposes them to potential exploits in security-sensitive applications and
highlights their lack of robustness. This paper uses a variational auto-encoder
(VAE) to defend against adversarial attacks for image classification tasks.
This VAE defense has a few nice properties: (1) it is quite flexible and its
use of randomness makes it harder to attack; (2) it can learn disentangled
representations that prevent blurry reconstruction; and (3) a patch-wise VAE
defense strategy is used that does not require retraining for different size
images. For moderate to severe attacks, this system outperforms or closely
matches the performance of JPEG compression, with the best quality parameter.
It also has more flexibility and potential for improvement via training.
Three Tools for Practical Differential Privacy
Authors: Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, Giorgio Patrini
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning

Differentially private learning on real-world data poses challenges for
standard machine learning practice: privacy guarantees are difficult to
interpret, hyperparameter tuning on private data reduces the privacy budget,
and ad-hoc privacy attacks are often required to test model privacy. We
introduce three tools to make differentially private machine learning more
practical: (1) simple sanity checks which can be carried out in a centralized
manner before training, (2) an adaptive clipping bound to reduce the effective
number of tuneable privacy parameters, and (3) we show that large-batch
training improves model performance.
Nonlinear Conjugate Gradients For Scaling Synchronous Distributed DNN Training
Authors: Saurabh Adya, Vinay Palakkode, Oncel Tuzel
Date: 2018-12-07 00:00:00
Tasks: 16k, General Classification

Nonlinear conjugate gradient (NLCG) based optimizers have shown superior loss convergence properties compared to gradient descent based optimizers for traditional optimization problems. However, in Deep Neural Network (DNN) training, the dominant optimization algorithm of choice is still Stochastic Gradient Descent (SGD) and its variants. In this work, we propose and evaluate the stochastic preconditioned nonlinear conjugate gradient algorithm for large scale DNN training tasks. We show that a nonlinear conjugate gradient algorithm improves the convergence speed of DNN training, especially in the large mini-batch scenario, which is essential for scaling synchronous distributed DNN training to large number of workers. We show how to efficiently use second order information in the NLCG pre-conditioner for improving DNN training convergence. For the ImageNet classification task, at extremely large mini-batch sizes of greater than 65k, NLCG optimizer is able to improve top-1 accuracy by more than 10 percentage points for standard training of the Resnet-50 model for 90 epochs. For the CIFAR-100 classification task, at extremely large mini-batch sizes of greater than 16k, NLCG optimizer is able to improve top-1 accuracy by more than 15 percentage points for standard training of the Resnet-32 model for 200 epochs.
Adversarial Attacks, Regression, and Numerical Stability Regularization
Authors: Andre T. Nguyen, Edward Raff
Date: 2018-12-07 00:00:00
Tasks: regression

Adversarial attacks against neural networks in a regression setting are a
critical yet understudied problem. In this work, we advance the state of the
art by investigating adversarial attacks against regression networks and by
formulating a more effective defense against these attacks. In particular, we
take the perspective that adversarial attacks are likely caused by numerical
instability in learned functions. We introduce a stability inducing,
regularization based defense against adversarial attacks in the regression
setting. Our new and easy to implement defense is shown to outperform prior
approaches and to improve the numerical stability of learned functions.
Neural Abstractive Text Summarization with Sequence-to-Sequence Models
Authors: Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy
Date: 2018-12-05 00:00:00
Tasks: Abstractive Text Summarization, Language Modeling, Language Modelling, Machine Translation, Survey, Text Summarization

In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and generate high-quality summaries. Generally speaking, most of these techniques differ in one of these three categories: network structure, parameter inference, and decoding/generation. There are also other concerns, such as efficiency and parallelism for training a model. In this paper, we provide a comprehensive literature survey on different seq2seq models for abstractive text summarization from the viewpoint of network structures, training strategies, and summary generation algorithms. Several models were first proposed for language modeling and generation tasks, such as machine translation, and later applied to abstractive text summarization. Hence, we also provide a brief review of these models. As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization. An extensive set of experiments have been conducted on the widely used CNN/Daily Mail dataset to examine the effectiveness of several different neural network components. Finally, we benchmark two models implemented in NATS on the two recently released datasets, namely, Newsroom and Bytecup.
A new multilayer optical film optimal method based on deep q-learning
Authors: Anqing Jiang, Osamu Yoshie, LiangYao Chen
Date: 2018-12-07 00:00:00
Tasks: Q-Learning

Multi-layer optical film has been found to afford important applications in
optical communication, optical absorbers, optical filters, etc. Different
algorithms of multi-layer optical film design has been developed, as simplex
method, colony algorithm, genetic algorithm. These algorithms rapidly promote
the design and manufacture of multi-layer films. However, traditional numerical
algorithms of converge to local optimum. This means that the algorithms can not
give a global optimal solution to the material researchers. In recent years,
due to the rapid development to far artificial intelligence, to optimize
optical film structure using AI algorithm has become possible. In this paper,
we will introduce a new optical film design algorithm based on the deep Q
learning. This model can converge the global optimum of the optical thin film
structure, this will greatly improve the design efficiency of multi-layer
films.
An Attempt towards Interpretable Audio-Visual Video Captioning
Authors: Yapeng Tian, Chenxiao Guan, Justin Goodman, Marc Moore, Chenliang Xu
Date: 2018-12-07 00:00:00
Tasks: Audio captioning, Audio-Visual Video Captioning, Image Captioning, Sentence, Video Captioning, Video Understanding

Automatically generating a natural language sentence to describe the content
of an input video is a very challenging problem. It is an essential multimodal
task in which auditory and visual contents are equally important. Although
audio information has been exploited to improve video captioning in previous
works, it is usually regarded as an additional feature fed into a black box
fusion machine. How are the words in the generated sentences associated with
the auditory and visual modalities? The problem is still not investigated. In
this paper, we make the first attempt to design an interpretable audio-visual
video captioning network to discover the association between words in sentences
and audio-visual sequences. To achieve this, we propose a multimodal
convolutional neural network-based audio-visual video captioning framework and
introduce a modality-aware module for exploring modality selection during
sentence generation. Besides, we collect new audio captioning and visual
captioning datasets for further exploring the interactions between auditory and
visual modalities for high-level video understanding. Extensive experiments
demonstrate that the modality-aware module makes our model interpretable on
modality selection during sentence generation. Even with the added
interpretability, our video captioning network can still achieve comparable
performance with recent state-of-the-art methods.
Measuring and Characterizing Generalization in Deep Reinforcement Learning
Authors: Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen
Date: 2018-12-07 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Deep reinforcement-learning methods have achieved remarkable performance on
challenging control tasks. Observations of the resulting behavior give the
impression that the agent has constructed a generalized representation that
supports insightful action decisions. We re-examine what is meant by
generalization in RL, and propose several definitions based on an agent's
performance in on-policy, off-policy, and unreachable states. We propose a set
of practical methods for evaluating agents with these definitions of
generalization. We demonstrate these techniques on a common benchmark task for
deep RL, and we show that the learned networks make poor decisions for states
that differ only slightly from on-policy states, even though those states are
not selected adversarially. Taken together, these results call into question
the extent to which deep Q-networks learn generalized representations, and
suggest that more experimentation and analysis is necessary before claims of
representation learning can be supported.
EEG Classification based on Image Configuration in Social Anxiety Disorder
Authors: Lubna Shibly Mokatren, Rashid Ansari, Ahmet Enis Cetin, Alex D. Leow, Olusola Ajilore, Heide Klumpp, Fatos T. Yarman Vural
Date: 2018-12-07 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

The problem of detecting the presence of Social Anxiety Disorder (SAD) using
Electroencephalography (EEG) for classification has seen limited study and is
addressed with a new approach that seeks to exploit the knowledge of EEG sensor
spatial configuration. Two classification models, one which ignores the
configuration (model 1) and one that exploits it with different interpolation
methods (model 2), are studied. Performance of these two models is examined for
analyzing 34 EEG data channels each consisting of five frequency bands and
further decomposed with a filter bank. The data are collected from 64 subjects
consisting of healthy controls and patients with SAD. Validity of our
hypothesis that model 2 will significantly outperform model 1 is borne out in
the results, with accuracy $6$--$7\%$ higher for model 2 for each machine
learning algorithm we investigated. Convolutional Neural Networks (CNN) were
found to provide much better performance than SVM and kNNs.
Privacy Partitioning: Protecting User Data During the Deep Learning Inference Phase
Authors: Jianfeng Chi, Emmanuel Owusu, Xuwang Yin, Tong Yu, William Chan, Patrick Tague, Yuan Tian
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Face Identification, General Classification

We present a practical method for protecting data during the inference phase
of deep learning based on bipartite topology threat modeling and an interactive
adversarial deep network construction. We term this approach \emph{Privacy
Partitioning}. In the proposed framework, we split the machine learning models
and deploy a few layers into users' local devices, and the rest of the layers
into a remote server. We propose an approach to protect user's data during the
inference phase, while still achieve good classification accuracy.
  We conduct an experimental evaluation of this approach on benchmark datasets
of three computer vision tasks. The experimental results indicate that this
approach can be used to significantly attenuate the capacity for an adversary
with access to the state-of-the-art deep network's intermediate states to learn
privacy-sensitive inputs to the network. For example, we demonstrate that our
approach can prevent attackers from inferring the private attributes such as
gender from the Face image dataset without sacrificing the classification
accuracy of the original machine learning task such as Face Identification.
Wireless Network Intelligence at the Edge
Authors: Jihong Park, Sumudu Samarakoon, Mehdi Bennis, Mérouane Debbah
Date: 2018-12-07 00:00:00
Tasks: Face Recognition, Medical Diagnosis

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-based machine learning (ML) have transformed every aspect of our lives from face recognition and medical diagnosis to natural language processing. However, classical ML exerts severe demands in terms of energy, memory and computing resources, limiting their adoption for resource constrained edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, etc.), requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML). In edge ML, training data is unevenly distributed over a large number of edge nodes, which have access to a tiny fraction of the data. Moreover training and inference is carried out collectively over wireless links, where edge devices communicate and exchange their learned models (not their private data). In a first of its kind, this article explores key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines. Finally, several case studies pertaining to various high-stake applications are presented demonstrating the effectiveness of edge ML in unlocking the full potential of 5G and beyond.
Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection
Authors: Xueqiang Zeng, Gang Luo
Date: 2018-12-06 00:00:00
Tasks: Automatic Machine Learning Model Selection, Bayesian Optimization, BIG-bench Machine Learning, Miscellaneous, Model Selection

Purpose: Machine learning is broadly used for clinical data analysis. Before
training a model, a machine learning algorithm must be selected. Also, the
values of one or more model parameters termed hyper-parameters must be set.
Selecting algorithms and hyper-parameter values requires advanced machine
learning knowledge and many labor-intensive manual iterations. To lower the bar
to machine learning, miscellaneous automatic selection methods for algorithms
and/or hyper-parameter values have been proposed. Existing automatic selection
methods are inefficient on large data sets. This poses a challenge for using
machine learning in the clinical big data era. Methods: To address the
challenge, this paper presents progressive sampling-based Bayesian
optimization, an efficient and automatic selection method for both algorithms
and hyper-parameter values. Results: We report an implementation of the method.
We show that compared to a state of the art automatic selection method, our
method can significantly reduce search time, classification error rate, and
standard deviation of error rate due to randomization. Conclusions: This is
major progress towards enabling fast turnaround in identifying high-quality
solutions required by many machine learning-based clinical data analysis tasks.
Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction
Authors: Gang Luo
Date: 2018-12-06 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Background: Predictive modeling is a key component of solutions to many
healthcare problems. Among all predictive modeling approaches, machine learning
methods often achieve the highest prediction accuracy, but suffer from a
long-standing open problem precluding their widespread use in healthcare. Most
machine learning models give no explanation for their prediction results,
whereas interpretability is essential for a predictive model to be adopted in
typical healthcare settings. Methods: This paper presents the first complete
method for automatically explaining results for any machine learning predictive
model without degrading accuracy. We did a computer coding implementation of
the method. Using the electronic medical record data set from the Practice
Fusion diabetes classification competition containing patient records from all
50 states in the United States, we demonstrated the method on predicting type 2
diabetes diagnosis within the next year. Results: For the champion machine
learning model of the competition, our method explained prediction results for
87.4% of patients who were correctly predicted by the model to have type 2
diabetes diagnosis within the next year. Conclusions: Our demonstration showed
the feasibility of automatically explaining results for any machine learning
predictive model without degrading accuracy.
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
Authors: John Foley, Emma Tosch, Kaleigh Clary, David Jensen
Date: 2018-12-06 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

It is a widely accepted principle that software without tests has bugs.
Testing reinforcement learning agents is especially difficult because of the
stochastic nature of both agents and environments, the complexity of
state-of-the-art models, and the sequential nature of their predictions.
Recently, the Arcade Learning Environment (ALE) has become one of the most
widely used benchmark suites for deep learning research, and state-of-the-art
Reinforcement Learning (RL) agents have been shown to routinely equal or exceed
human performance on many ALE tasks. Since ALE is based on emulation of
original Atari games, the environment does not provide semantically meaningful
representations of internal game state. This means that ALE has limited utility
as an environment for supporting testing or model introspection. We propose
ToyBox, a collection of reimplementations of these games that solves this
critical problem and enables robust testing of RL agents.
A Survey of Unsupervised Deep Domain Adaptation
Authors: Garrett Wilson, Diane J. Cook
Date: 2018-12-06 00:00:00
Tasks: Domain Adaptation, Survey, Transfer Learning, Unsupervised Domain Adaptation

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.