pwc-archive/papers-with-abstracts huggingface dataset:
Automated Directed Fairness Testing
Authors: Sakshi Udeshi, Pryanshu Arora, Sudipta Chattopadhyay
Date: 2018-07-02 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Fairness

Fairness is a critical trait in decision making. As machine-learning models
are increasingly being used in sensitive application domains (e.g. education
and employment) for decision making, it is crucial that the decisions computed
by such models are free of unintended bias. But how can we automatically
validate the fairness of arbitrary machine-learning models? For a given
machine-learning model and a set of sensitive input parameters, our AEQUITAS
approach automatically discovers discriminatory inputs that highlight fairness
violation. At the core of AEQUITAS are three novel strategies to employ
probabilistic search over the input space with the objective of uncovering
fairness violation. Our AEQUITAS approach leverages inherent robustness
property in common machine-learning models to design and implement scalable
test generation methodologies. An appealing feature of our generated test
inputs is that they can be systematically added to the training set of the
underlying model and improve its fairness. To this end, we design a fully
automated module that guarantees to improve the fairness of the underlying
model.
  We implemented AEQUITAS and we have evaluated it on six state-of-the-art
classifiers, including a classifier that was designed with fairness
constraints. We show that AEQUITAS effectively generates inputs to uncover
fairness violation in all the subject classifiers and systematically improves
the fairness of the respective models using the generated test inputs. In our
evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the
total number of inputs generated) and leverages these inputs to improve the
fairness up to 94%.
Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration
Authors: Jan Rühaak, Thomas Polzin, Stefan Heldmann, Ivor J. A. Simpson, Heinz Handels, Jan Modersitzki, Mattias P. Heinrich
Date: 2018-07-02 00:00:00
Tasks: Image Registration

We present a novel algorithm for the registration of pulmonary CT scans. Our
method is designed for large respiratory motion by integrating sparse keypoint
correspondences into a dense continuous optimization framework. The detection
of keypoint correspondences enables robustness against large deformations by
jointly optimizing over a large number of potential discrete displacements,
whereas the dense continuous registration achieves subvoxel alignment with
smooth transformations. Both steps are driven by the same normalized gradient
fields data term. We employ curvature regularization and a volume change
control mechanism to prevent foldings of the deformation grid and restrict the
determinant of the Jacobian to physiologically meaningful values. Keypoint
correspondences are integrated into the dense registration by a quadratic
penalty with adaptively determined weight. Using a parallel matrix-free
derivative calculation scheme, a runtime of about 5 min was realized on a
standard PC. The proposed algorithm ranks first in the EMPIRE10 challenge on
pulmonary image registration. Moreover, it achieves an average landmark
distance of 0.82 mm on the DIR-Lab COPD database, thereby improving upon the
state of the art in accuracy by 15%. Our algorithm is the first to reach the
inter-observer variability in landmark annotation on this dataset.
ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites
Authors: Jiankai Sun, Abhinav Vishnu, Aniket Chakrabarti, Charles Siegel, Srinivasan Parthasarathy
Date: 2018-07-02 00:00:00

Routing questions in Community Question Answer services (CQAs) such as Stack
Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena
observed when a new question is posted is not well addressed by existing
approaches. Additionally, cold questions posted by new askers present
significant challenges to state-of-the-art approaches. We propose ColdRoute to
address these challenges. ColdRoute is able to handle the task of routing cold
questions posted by new or existing askers to matching experts. Specifically,
we use Factorization Machines on the one-hot encoding of critical features such
as question tags and compare our approach to well-studied techniques such as
CQARank and semantic matching (LDA, BoW, and Doc2Vec). Using data from eight
stack exchange sites, we are able to improve upon the routing metrics
(Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as
semantic matching by $159.5\%$,$31.84\%$, and $40.36\%$ for cold questions
posted by existing askers, and $123.1\%$, $27.03\%$, and $34.81\%$ for cold
questions posted by new askers respectively.
How To Backdoor Federated Learning
Authors: Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, Vitaly Shmatikov
Date: 2018-07-02 00:00:00
Tasks: Anomaly Detection, Data Poisoning, Federated Learning, Model Poisoning

Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type. We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a word predictor completes certain sentences with an attacker-chosen word. We design and evaluate a new model-poisoning methodology based on model replacement. An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning. Our generic constrain-and-scale technique also evades anomaly detection-based defenses by incorporating the evasion into the attacker's loss function during training.
Adversarial Perturbations Against Real-Time Video Classification Systems
Authors: Shasha Li, Ajaya Neupane, Sujoy Paul, Chengyu Song, Srikanth V. Krishnamurthy, Amit K. Roy Chowdhury, Ananthram Swami
Date: 2018-07-02 00:00:00
Tasks: Classification, General Classification, Generative Adversarial Network, Video Classification

Recent research has demonstrated the brittleness of machine learning systems
to adversarial perturbations. However, the studies have been mostly limited to
perturbations on images and more generally, classification that does not deal
with temporally varying inputs. In this paper we ask "Are adversarial
perturbations possible in real-time video classification systems and if so,
what properties must they satisfy?" Such systems find application in
surveillance applications, smart vehicles, and smart elderly care and thus,
misclassification could be particularly harmful (e.g., a mishap at an elderly
care facility may be missed). We show that accounting for temporal structure is
key to generating adversarial examples in such systems. We exploit recent
advances in generative adversarial network (GAN) architectures to account for
temporal correlations and generate adversarial samples that can cause
misclassification rates of over 80% for targeted activities. More importantly,
the samples also leave other activities largely unaffected making them
extremely stealthy. Finally, we also surprisingly find that in many scenarios,
the same perturbation can be applied to every frame in a video clip that makes
the adversary's ability to achieve misclassification relatively easy.
Evenly Cascaded Convolutional Networks
Authors: Chengxi Ye, Chinmaya Devaraj, Michael Maynord, Cornelia Fermüller, Yiannis Aloimonos
Date: 2018-07-02 00:00:00

We introduce Evenly Cascaded convolutional Network (ECN), a neural network
taking inspiration from the cascade algorithm of wavelet analysis. ECN employs
two feature streams - a low-level and high-level steam. At each layer these
streams interact, such that low-level features are modulated using advanced
perspectives from the high-level stream. ECN is evenly structured through
resizing feature map dimensions by a consistent ratio, which removes the burden
of ad-hoc specification of feature map dimensions. ECN produces easily
interpretable features maps, a result whose intuition can be understood in the
context of scale-space theory. We demonstrate that ECN's design facilitates the
training process through providing easily trainable shortcuts. We report new
state-of-the-art results for small networks, without the need for additional
treatment such as pruning or compression - a consequence of ECN's simple
structure and direct training. A 6-layered ECN design with under 500k
parameters achieves 95.24% and 78.99% accuracy on CIFAR-10 and CIFAR-100
datasets, respectively, outperforming the current state-of-the-art on small
parameter networks, and a 3 million parameter ECN produces results competitive
to the state-of-the-art.
Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision
Authors: Yue Bai, Shuvra S. Bhattacharyya, Antti P. Happonen, Heikki Huttunen
Date: 2018-07-02 00:00:00
Tasks: Age Estimation, General Classification, image-classification, Image Classification

We propose a new framework for image classification with deep neural
networks. The framework introduces intermediate outputs to the computational
graph of a network. This enables flexible control of the computational load and
balances the tradeoff between accuracy and execution time.
  Moreover, we present an interesting finding that the intermediate outputs can
act as a regularizer at training time, improving the prediction accuracy. In
the experimental section we demonstrate the performance of our proposed
framework with various commonly used pretrained deep networks in the use case
of apparent age estimation.
Multi-distance Support Matrix Machines
Authors: Yunfei Ye, Dong Han
Date: 2018-07-02 00:00:00
Tasks: Generalization Bounds

Real-world data such as digital images, MRI scans and electroencephalography
signals are naturally represented as matrices with structural information. Most
existing classifiers aim to capture these structures by regularizing the
regression matrix to be low-rank or sparse. Some other methodologies introduce
factorization technique to explore nonlinear relationships of matrix data in
kernel space. In this paper, we propose a multi-distance support matrix machine
(MDSMM), which provides a principled way of solving matrix classification
problems. The multi-distance is introduced to capture the correlation within
matrix data, by means of intrinsic information in rows and columns of input
data. A complex hyperplane is established upon these values to separate
distinct classes. We further study the generalization bounds for i.i.d.
processes and non i.i.d. process based on both SVM and SMM classifiers. For
typical hypothesis classes where matrix norms are constrained, MDSMM achieves a
faster learning rate than traditional classifiers. We also provide a more
general approach for samples without prior knowledge. We demonstrate the merits
of the proposed method by conducting exhaustive experiments on both simulation
study and a number of real-word datasets.
Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design
Authors: Hua-Lin He, Chun-Xiang Pan, Qing Da, An-Xiang Zeng
Date: 2018-07-02 00:00:00
Tasks: Reinforcement Learning

In a large E-commerce platform, all the participants compete for impressions
under the allocation mechanism of the platform. Existing methods mainly focus
on the short-term return based on the current observations instead of the
long-term return. In this paper, we formally establish the lifecycle model for
products, by defining the introduction, growth, maturity and decline stages and
their transitions throughout the whole life period. Based on such model, we
further propose a reinforcement learning based mechanism design framework for
impression allocation, which incorporates the first principal component based
permutation and the novel experiences generation method, to maximize short-term
as well as long-term return of the platform. With the power of trial-and-error,
it is possible to optimize impression allocation strategies globally which is
contribute to the healthy development of participants and the platform itself.
We evaluate our algorithm on a simulated environment built based on one of the
largest E-commerce platforms, and a significant improvement has been achieved
in comparison with the baseline solutions.
Policy Optimization With Penalized Point Probability Distance: An Alternative To Proximal Policy Optimization
Authors: Xiangxiang Chu
Date: 2018-07-02 00:00:00
Tasks: Atari Games, Reinforcement Learning

As the most successful variant and improvement for Trust Region Policy
Optimization (TRPO), proximal policy optimization (PPO) has been widely applied
across various domains with several advantages: efficient data utilization,
easy implementation, and good parallelism. In this paper, a first-order
gradient reinforcement learning algorithm called Policy Optimization with
Penalized Point Probability Distance (POP3D), which is a lower bound to the
square of total variance divergence is proposed as another powerful variant.
Firstly, we talk about the shortcomings of several commonly used algorithms, by
which our method is partly motivated. Secondly, we address to overcome these
shortcomings by applying POP3D. Thirdly, we dive into its mechanism from the
perspective of solution manifold. Finally, we make quantitative comparisons
among several state-of-the-art algorithms based on common benchmarks.
Simulation results show that POP3D is highly competitive compared with PPO.
Besides, our code is released in https://github.com/paperwithcode/pop3d.
Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)
Authors: Anvar Bahrampour, Omid Mohamad Nezami
Date: 2018-07-02 00:00:00
Tasks: Diversity

Premature convergence in particle swarm optimization (PSO) algorithm usually
leads to gaining local optimum and preventing from surveying those regions of
solution space which have optimal points in. In this paper, by applying special
mechanisms, suitable regions were detected and then swarm was guided to them by
dispersing part of particles on proper times. This process is called dynamic
swarm dispersion in PSO (DSDPSO) algorithm. In order to specify the proper
times and to rein the evolutionary process alternating between exploring and
exploiting behaviors, we used a diversity measuring approach and implemented
the dispersion mechanism. To promote the performance of DSDPSO algorithm, three
different policies including particle relocation, velocity settings of
dispersed particles and parameters setting were applied. We compared the
promoted algorithm with similar new approaches and according to the numerical
results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO,
CLPSO and APSO in most of the 12 standard benchmark problems with different
properties taken in this study.
Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector
Authors: Sang-gil Lee, Jae Seok Bae, Hyunjae Kim, Jung Hoon Kim, Sungroh Yoon
Date: 2018-07-02 00:00:00
Tasks: Computed Tomography (CT), Lesion Detection, Lesion Segmentation, Medical Object Detection, Object Detection

We present a focal liver lesion detection model leveraged by custom-designed
multi-phase computed tomography (CT) volumes, which reflects real-world
clinical lesion detection practice using a Single Shot MultiBox Detector (SSD).
We show that grouped convolutions effectively harness richer information of the
multi-phase data for the object detection model, while a naive application of
SSD suffers from a generalization gap. We trained and evaluated the modified
SSD model and recently proposed variants with our CT dataset of 64 subjects by
five-fold cross validation. Our model achieved a 53.3% average precision score
and ran in under three seconds per volume, outperforming the original model and
state-of-the-art variants. Results show that the one-stage object detection
model is a practical solution, which runs in near real-time and can learn an
unbiased feature representation from a large-volume real-world detection
dataset, which requires less tedious and time consuming construction of the
weak phase-level bounding box labels.
Confounding variables can degrade generalization performance of radiological deep learning models
Authors: John R. Zech, Marcus A. Badgeley, Manway Liu, Anthony B. Costa, Joseph J. Titano, Eric K. Oermann
Date: 2018-07-02 00:00:00
Tasks: Deep Learning

Early results in using convolutional neural networks (CNNs) on x-rays to
diagnose disease have been promising, but it has not yet been shown that models
trained on x-rays from one hospital or one group of hospitals will work equally
well at different hospitals. Before these tools are used for computer-aided
diagnosis in real-world clinical settings, we must verify their ability to
generalize across a variety of hospital systems. A cross-sectional design was
used to train and evaluate pneumonia screening CNNs on 158,323 chest x-rays
from NIH (n=112,120 from 30,805 patients), Mount Sinai (42,396 from 12,904
patients), and Indiana (n=3,807 from 3,683 patients). In 3 / 5 natural
comparisons, performance on chest x-rays from outside hospitals was
significantly lower than on held-out x-rays from the original hospital systems.
CNNs were able to detect where an x-ray was acquired (hospital system, hospital
department) with extremely high accuracy and calibrate predictions accordingly.
The performance of CNNs in diagnosing diseases on x-rays may reflect not only
their ability to identify disease-specific imaging findings on x-rays, but also
their ability to exploit confounding information. Estimates of CNN performance
based on test data from hospital systems used for model training may overstate
their likely real-world performance.
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks
Authors: Mark Harmon, Diego Klabjan
Date: 2018-07-02 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Recurrent neural networks and sequence to sequence models require a predetermined length for prediction output length. Our model addresses this by allowing the network to predict a variable length output in inference. A new loss function with a tailored gradient computation is developed that trades off prediction accuracy and output length. The model utilizes a function to determine whether a particular output at a time should be evaluated or not given a predetermined threshold. We evaluate the model on the problem of predicting the prices of securities. We find that the model makes longer predictions for more stable securities and it naturally balances prediction accuracy and length.
Optimization of neural networks via finite-value quantum fluctuations
Authors: Masayuki Ohzeki, Shuntaro Okada, Masayoshi Terabe, Shinichiro Taguchi
Date: 2018-07-01 00:00:00

We numerically test an optimization method for deep neural networks (DNNs)
using quantum fluctuations inspired by quantum annealing. For efficient
optimization, our method utilizes the quantum tunneling effect beyond the
potential barriers. The path integral formulation of the DNN optimization
generates an attracting force to simulate the quantum tunneling effect. In the
standard quantum annealing method, the quantum fluctuations will vanish at the
last stage of optimization. In this study, we propose a learning protocol that
utilizes a finite value for quantum fluctuations strength to obtain higher
generalization performance, which is a type of robustness. We demonstrate the
performance of our method using two well-known open datasets: the MNIST dataset
and the Olivetti face dataset. Although computational costs prevent us from
testing our method on large datasets with high-dimensional data, results show
that our method can enhance generalization performance by induction of the
finite value for quantum fluctuations.
Learning to Drive in a Day
Authors: Alex Kendall, Jeffrey Hawke, David Janz, Przemyslaw Mazur, Daniele Reda, John-Mark Allen, Vinh-Dieu Lam, Alex Bewley, Amar Shah
Date: 2018-07-01 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We demonstrate the first application of deep reinforcement learning to
autonomous driving. From randomly initialised parameters, our model is able to
learn a policy for lane following in a handful of training episodes using a
single monocular image as input. We provide a general and easy to obtain
reward: the distance travelled by the vehicle without the safety driver taking
control. We use a continuous, model-free deep reinforcement learning algorithm,
with all exploration and optimisation performed on-vehicle. This demonstrates a
new framework for autonomous driving which moves away from reliance on defined
logical rules, mapping, and direct supervision. We discuss the challenges and
opportunities to scale this approach to a broader range of autonomous driving
tasks.
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Authors: Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh
Date: 2018-07-01 00:00:00
Tasks: Deep Reinforcement Learning, Program Repair, Program Synthesis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has led to several recent breakthroughs, though
the learned policies are often based on black-box neural networks. This makes
them difficult to interpret and to impose desired specification constraints
during learning. We present an iterative framework, MORL, for improving the
learned policies using program synthesis. Concretely, we propose to use
synthesis techniques to obtain a symbolic representation of the learned policy,
which can then be debugged manually or automatically using program repair.
After the repair step, we use behavior cloning to obtain the policy
corresponding to the repaired program, which is then further improved using
gradient descent. This process continues until the learned policy satisfies
desired constraints. We instantiate MORL for the simple CartPole problem and
show that the programmatic representation allows for high-level modifications
that in turn lead to improved learning of the policies.
Machine learning 2.0 : Engineering Data Driven AI Products
Authors: James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni
Date: 2018-07-01 00:00:00
Tasks: BIG-bench Machine Learning

ML 2.0: In this paper, we propose a paradigm shift from the current practice
of creating machine learning models - which requires months-long discovery,
exploration and "feasibility report" generation, followed by re-engineering for
deployment - in favor of a rapid, 8-week process of development, understanding,
validation and deployment that can executed by developers or subject matter
experts (non-ML experts) using reusable APIs. This accomplishes what we call a
"minimum viable data-driven model," delivering a ready-to-use machine learning
model for problems that haven't been solved before using machine learning. We
provide provisions for the refinement and adaptation of the "model," with
strict enforcement and adherence to both the scaffolding/abstractions and the
process. We imagine that this will bring forth the second phase in machine
learning, in which discovery is subsumed by more targeted goals of delivery and
impact.