pwc-archive/papers-with-abstracts huggingface dataset:
Explaining Deep Learning Models using Causal Inference
Authors: Tanmayee Narendra, Anush Sankaran, Deepak Vijaykeerthy, Senthil Mani
Date: 2018-11-11 00:00:00
Tasks: Causal Inference, counterfactual, Deep Learning

Although deep learning models have been successfully applied to a variety of
tasks, due to the millions of parameters, they are becoming increasingly opaque
and complex. In order to establish trust for their widespread commercial use,
it is important to formalize a principled framework to reason over these
models. In this work, we use ideas from causal inference to describe a general
framework to reason over CNN models. Specifically, we build a Structural Causal
Model (SCM) as an abstraction over a specific aspect of the CNN. We also
formulate a method to quantitatively rank the filters of a convolution layer
according to their counterfactual importance. We illustrate our approach with
popular CNN architectures such as LeNet5, VGG19, and ResNet32.
Fashion and Apparel Classification using Convolutional Neural Networks
Authors: Alexander Schindler, Thomas Lidy, Stephan Karner, Matthias Hecker
Date: 2018-11-11 00:00:00
Tasks: Classification, Gender Classification, General Classification, Human Detection, image-classification, Image Classification

We present an empirical study of applying deep Convolutional Neural Networks
(CNN) to the task of fashion and apparel image classification to improve
meta-data enrichment of e-commerce applications. Five different CNN
architectures were analyzed using clean and pre-trained models. The models were
evaluated in three different tasks person detection, product and gender
classification, on two small and large scale datasets.
Improved Visual Relocalization by Discovering Anchor Points
Authors: Soham Saha, Girish Varma, C. V. Jawahar
Date: 2018-11-11 00:00:00
Tasks: Outdoor Localization

We address the visual relocalization problem of predicting the location and
camera orientation or pose (6DOF) of the given input scene. We propose a method
based on how humans determine their location using the visible landmarks. We
define anchor points uniformly across the route map and propose a deep learning
architecture which predicts the most relevant anchor point present in the scene
as well as the relative offsets with respect to it. The relevant anchor point
need not be the nearest anchor point to the ground truth location, as it might
not be visible due to the pose. Hence we propose a multi task loss function,
which discovers the relevant anchor point, without needing the ground truth for
it. We validate the effectiveness of our approach by experimenting on
CambridgeLandmarks (large scale outdoor scenes) as well as 7 Scenes (indoor
scenes) using variousCNN feature extractors. Our method improves the median
error in indoor as well as outdoor localization datasets compared to the
previous best deep learning model known as PoseNet (with geometric
re-projection loss) using the same feature extractor. We improve the median
error in localization in the specific case of Street scene, by over 8m.
User Modeling for Task Oriented Dialogues
Authors: Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur, Pararth Shah
Date: 2018-11-11 00:00:00
Tasks: Dialogue State Tracking, Diversity, Task-Oriented Dialogue Systems, User Simulation

We introduce end-to-end neural network based models for simulating users of
task-oriented dialogue systems. User simulation in dialogue systems is crucial
from two different perspectives: (i) automatic evaluation of different dialogue
models, and (ii) training task-oriented dialogue systems. We design a
hierarchical sequence-to-sequence model that first encodes the initial user
goal and system turns into fixed length representations using Recurrent Neural
Networks (RNN). It then encodes the dialogue history using another RNN layer.
At each turn, user responses are decoded from the hidden representations of the
dialogue level RNN. This hierarchical user simulator (HUS) approach allows the
model to capture undiscovered parts of the user goal without the need of an
explicit dialogue state tracking. We further develop several variants by
utilizing a latent variable model to inject random variations into user
responses to promote diversity in simulated user responses and a novel goal
regularization mechanism to penalize divergence of user responses from the
initial user goal. We evaluate the proposed models on movie ticket booking
domain by systematically interacting each user simulator with various dialogue
system policies trained with different objectives and users.
Survey of state-of-the-art mixed data clustering algorithms
Authors: Amir Ahmad, Shehroz S. Khan
Date: 2018-11-11 00:00:00
Tasks: Clustering, Marketing, Survey

Mixed data comprises both numeric and categorical features, and mixed
datasets occur frequently in many domains, such as health, finance, and
marketing. Clustering is often applied to mixed datasets to find structures and
to group similar objects for further analysis. However, clustering mixed data
is challenging because it is difficult to directly apply mathematical
operations, such as summation or averaging, to the feature values of these
datasets. In this paper, we present a taxonomy for the study of mixed data
clustering algorithms by identifying five major research themes. We then
present a state-of-the-art review of the research works within each research
theme. We analyze the strengths and weaknesses of these methods with pointers
for future research directions. Lastly, we present an in-depth analysis of the
overall challenges in this field, highlight open research questions and discuss
guidelines to make progress in the field.
Neural Generative Models for 3D Faces with Application in 3D Texture Free Face Recognition
Authors: Ahmed ElSayed, Elif Kongar, Ausif Mahmood, Tarek Sobh, Terrance Boult
Date: 2018-11-11 00:00:00
Tasks: Face Recognition, Face Verification

Using heterogeneous depth cameras and 3D scanners in 3D face verification
causes variations in the resolution of the 3D point clouds. To solve this
issue, previous studies use 3D registration techniques. Out of these proposed
techniques, detecting points of correspondence is proven to be an efficient
method given that the data belongs to the same individual. However, if the data
belongs to different persons, the registration algorithms can convert the 3D
point cloud of one person to another, destroying the distinguishing features
between the two point clouds. Another issue regarding the storage size of the
point clouds. That is, if the captured depth image contains around 50 thousand
points in the cloud for a single pose for one individual, then the storage size
of the entire dataset will be in order of giga if not tera bytes. With these
motivations, this work introduces a new technique for 3D point clouds
generation using a neural modeling system to handle the differences caused by
heterogeneous depth cameras, and to generate a new face canonical compact
representation. The proposed system reduces the stored 3D dataset size, and if
required, provides an accurate dataset regeneration. Furthermore, the system
generates neural models for all gallery point clouds and stores these models to
represent the faces in the recognition or verification processes. For the probe
cloud to be verified, a new model is generated specifically for that particular
cloud and is matched against pre-stored gallery model presentations to identify
the query cloud. This work also introduces the utilization of Siamese deep
neural network in 3D face verification using generated model representations as
raw data for the deep network, and shows that the accuracy of the trained
network is comparable all published results on Bosphorus dataset.
Multi-labeled Relation Extraction with Attentive Capsule Network
Authors: Xinsong Zhang, Pengshuai Li, Weijia Jia, Hai Zhao
Date: 2018-11-11 00:00:00
Tasks: Multi-Labeled Relation Extraction, Relation, Relation Extraction, Sentence

To disclose overlapped multiple relations from a sentence still keeps
challenging. Most current works in terms of neural models inconveniently
assuming that each sentence is explicitly mapped to a relation label, cannot
handle multiple relations properly as the overlapped features of the relations
are either ignored or very difficult to identify. To tackle with the new issue,
we propose a novel approach for multi-labeled relation extraction with capsule
network which acts considerably better than current convolutional or recurrent
net in identifying the highly overlapped relations within an individual
sentence. To better cluster the features and precisely extract the relations,
we further devise attention-based routing algorithm and sliding-margin loss
function, and embed them into our capsule network. The experimental results
show that the proposed approach can indeed extract the highly overlapped
features and achieve significant performance improvement for relation
extraction comparing to the state-of-the-art works.
Open Vocabulary Learning for Neural Chinese Pinyin IME
Authors: Zhuosheng Zhang, Yafang Huang, Hai Zhao
Date: 2018-11-11 00:00:00
Proceeding: ACL 2019 7

Pinyin-to-character (P2C) conversion is the core component of pinyin-based Chinese input method engine (IME). However, the conversion is seriously compromised by the ambiguities of Chinese characters corresponding to pinyin as well as the predefined fixed vocabularies. To alleviate such inconveniences, we propose a neural P2C conversion model augmented by an online updated vocabulary with a sampling mechanism to support open vocabulary learning during IME working. Our experiments show that the proposed method outperforms commercial IMEs and state-of-the-art traditional models on standard corpus and true inputting history dataset in terms of multiple metrics and thus the online updated vocabulary indeed helps our IME effectively follows user inputting behavior.
Generalization Bounds for Vicinal Risk Minimization Principle
Authors: Chao Zhang, Min-Hsiu Hsieh, DaCheng Tao
Date: 2018-11-11 00:00:00
Tasks: Generalization Bounds

The vicinal risk minimization (VRM) principle, first proposed by
\citet{vapnik1999nature}, is an empirical risk minimization (ERM) variant that
replaces Dirac masses with vicinal functions. Although there is strong
numerical evidence showing that VRM outperforms ERM if appropriate vicinal
functions are chosen, a comprehensive theoretical understanding of VRM is still
lacking. In this paper, we study the generalization bounds for VRM. Our results
support Vapnik's original arguments and additionally provide deeper insights
into VRM. First, we prove that the complexity of function classes convolving
with vicinal functions can be controlled by that of the original function
classes under the assumption that the function class is composed of
Lipschitz-continuous functions. Then, the resulting generalization bounds for
VRM suggest that the generalization performance of VRM is also effected by the
choice of vicinity function and the quality of function classes. These findings
can be used to examine whether the choice of vicinal function is appropriate
for the VRM-based learning setting. Finally, we provide a theoretical
explanation for existing VRM models, e.g., uniform distribution-based models,
Gaussian distribution-based models, and mixup models.
Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for Deep Transparent Reinforcement Learning
Authors: John Yang, Gyujeong Lee, Minsung Hyun, Simyung Chang, Nojun Kwak
Date: 2018-11-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We tackle the blackbox issue of deep neural networks in the settings of
reinforcement learning (RL) where neural agents learn towards maximizing reward
gains in an uncontrollable way. Such learning approach is risky when the
interacting environment includes an expanse of state space because it is then
almost impossible to foresee all unwanted outcomes and penalize them with
negative rewards beforehand. Unlike reverse analysis of learned neural features
from previous works, our proposed method \nj{tackles the blackbox issue by
encouraging} an RL policy network to learn interpretable latent features
through an implementation of a disentangled representation learning method.
Toward this end, our method allows an RL agent to understand self-efficacy by
distinguishing its influences from uncontrollable environmental factors, which
closely resembles the way humans understand their scenes. Our experimental
results show that the learned latent factors not only are interpretable, but
also enable modeling the distribution of entire visited state space with a
specific action condition. We have experimented that this characteristic of the
proposed structure can lead to ex post facto governance for desired behaviors
of RL agents.
Deep Face Quality Assessment
Authors: Vishal Agarwal
Date: 2018-11-11 00:00:00
Tasks: Face Image Quality, Face Image Quality Assessment, Image Quality Assessment

Face image quality is an important factor in facial recognition systems as
its verification and recognition accuracy is highly dependent on the quality of
image presented. Rejecting low quality images can significantly increase the
accuracy of any facial recognition system. In this project, a simple approach
is presented to train a deep convolutional neural network to perform end-to-end
face image quality assessment. The work is done in 2 stages : First, generation
of quality score label and secondly, training a deep convolutional neural
network in a supervised manner to predict quality score between 0 and 1. The
generation of quality labels is done by comparing the face image with a
template of best quality images and then evaluating the normalized score based
on the similarity.
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
Authors: Ishan Jindal, Zhiwei Qin, Xue-wen Chen, Matthew Nokleby, Jieping Ye
Date: 2018-11-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Travel Time Estimation

In this paper, we develop a reinforcement learning (RL) based system to learn
an effective policy for carpooling that maximizes transportation efficiency so
that fewer cars are required to fulfill the given amount of trip demand. For
this purpose, first, we develop a deep neural network model, called ST-NN
(Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS
trip data. Secondly, we develop a carpooling simulation environment for RL
training, with the output of ST-NN and using the NYC taxi trip dataset. In
order to maximize transportation efficiency and minimize traffic congestion, we
choose the effective distance covered by the driver on a carpool trip as the
reward. Therefore, the more effective distance a driver achieves over a trip
(i.e. to satisfy more trip demand) the higher the efficiency and the less will
be the traffic congestion. We compared the performance of RL learned policy to
a fixed policy (which always accepts carpool) as a baseline and obtained
promising results that are interpretable and demonstrate the advantage of our
RL approach. We also compare the performance of ST-NN to that of
state-of-the-art travel time estimation methods and observe that ST-NN
significantly improves the prediction performance and is more robust to
outliers.
Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders
Authors: Jen J. Gong, Abigail Z. Jacobs, Toby E. Stuart, Mathijs de Vaan
Date: 2018-11-11 00:00:00
Tasks: Medical Diagnosis

The opioid epidemic in the United States claims over 40,000 lives per year,
and it is estimated that well over two million Americans have an opioid use
disorder. Over-prescription and misuse of prescription opioids play an
important role in the epidemic. Individuals who are prescribed opioids, and who
are diagnosed with opioid use disorder, have diverse underlying health states.
Policy interventions targeting prescription opioid use, opioid use disorder,
and overdose often fail to account for this variation. To identify latent
health states, or phenotypes, pertinent to opioid use and opioid use disorders,
we use probabilistic topic modeling with medical diagnosis histories from a
statewide population of individuals who were prescribed opioids. We demonstrate
that our learned phenotypes are predictive of future opioid use-related
outcomes. In addition, we show how the learned phenotypes can provide important
context for variability in opioid prescriptions. Understanding the
heterogeneity in individual health states and in prescription opioid use can
help identify policy interventions to address this public health crisis.
Langevin-gradient parallel tempering for Bayesian neural learning
Authors: Rohitash Chandra, Konark Jain, Ratneel V. Deo, Sally Cripps
Date: 2018-11-11 00:00:00
Tasks: Decision Making, Time Series, Time Series Analysis, Time Series Prediction, Uncertainty Quantification

Bayesian neural learning feature a rigorous approach to estimation and
uncertainty quantification via the posterior distribution of weights that
represent knowledge of the neural network. This not only provides point
estimates of optimal set of weights but also the ability to quantify
uncertainty in decision making using the posterior distribution. Markov chain
Monte Carlo (MCMC) techniques are typically used to obtain sample-based
estimates of the posterior distribution. However, these techniques face
challenges in convergence and scalability, particularly in settings with large
datasets and network architectures. This paper address these challenges in two
ways. First, parallel tempering is used used to explore multiple modes of the
posterior distribution and implemented in multi-core computing architecture.
Second, we make within-chain sampling schemes more efficient by using Langevin
gradient information in forming Metropolis-Hastings proposal distributions. We
demonstrate the techniques using time series prediction and pattern
classification applications. The results show that the method not only improves
the computational time, but provides better prediction or decision making
capabilities when compared to related methods.
Diversity-Driven Extensible Hierarchical Reinforcement Learning
Authors: Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Mai Xu
Date: 2018-11-10 00:00:00
Tasks: Diversity, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical reinforcement learning (HRL) has recently shown promising
advances on speeding up learning, improving the exploration, and discovering
intertask transferable skills. Most recent works focus on HRL with two levels,
i.e., a master policy manipulates subpolicies, which in turn manipulate
primitive actions. However, HRL with multiple levels is usually needed in many
real-world scenarios, whose ultimate goals are highly abstract, while their
actions are very primitive. Therefore, in this paper, we propose a
diversity-driven extensible HRL (DEHRL), where an extensible and scalable
framework is built and learned levelwise to realize HRL with multiple levels.
DEHRL follows a popular assumption: diverse subpolicies are useful, i.e.,
subpolicies are believed to be more useful if they are more diverse. However,
existing implementations of this diversity assumption usually have their own
drawbacks, which makes them inapplicable to HRL with multiple levels.
Consequently, we further propose a novel diversity-driven solution to achieve
this assumption in DEHRL. Experimental studies evaluate DEHRL with five
baselines from four perspectives in two domains; the results show that DEHRL
outperforms the state-of-the-art baselines in all four aspects.
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing
Authors: Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki
Date: 2018-11-10 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Local Color Enhancement, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep RL for image processing are still limited. Therefore, we
extend deep RL to pixelRL for various image processing applications. In
pixelRL, each pixel has an agent, and the agent changes the pixel value by
taking an action. We also propose an effective learning method for pixelRL that
significantly improves the performance by considering not only the future
states of the own pixel but also those of the neighbor pixels. The proposed
method can be applied to some image processing tasks that require pixel-wise
manipulations, where deep RL has never been applied. We apply the proposed
method to three image processing tasks: image denoising, image restoration, and
local color enhancement. Our experimental results demonstrate that the proposed
method achieves comparable or better performance, compared with the
state-of-the-art methods based on supervised learning.
Playing by the Book: An Interactive Game Approach for Action Graph Extraction from Text
Authors: Ronen Tamari, Hiroyuki Shindo, Dafna Shahaf, Yuji Matsumoto
Date: 2018-11-10 00:00:00
Proceeding: WS 2019 6

Understanding procedural text requires tracking entities, actions and effects
as the narrative unfolds. We focus on the challenging real-world problem of
action-graph extraction from material science papers, where language is highly
specialized and data annotation is expensive and scarce. We propose a novel
approach, Text2Quest, where procedural text is interpreted as instructions for
an interactive game. A learning agent completes the game by executing the
procedure correctly in a text-based simulated lab environment. The framework
can complement existing approaches and enables richer forms of learning
compared to static texts. We discuss potential limitations and advantages of
the approach, and release a prototype proof-of-concept, hoping to encourage
research in this direction.
Automatic Brain Structures Segmentation Using Deep Residual Dilated U-Net
Authors: Hongwei Li, Andrii Zhygallo, Bjoern Menze
Date: 2018-11-10 00:00:00
Tasks: Brain Image Segmentation, Brain Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Brain image segmentation is used for visualizing and quantifying anatomical
structures of the brain. We present an automated ap-proach using 2D deep
residual dilated networks which captures rich context information of different
tissues for the segmentation of eight brain structures. The proposed system was
evaluated in the MICCAI Brain Segmentation Challenge and ranked 9th out of 22
teams. We further compared the method with traditional U-Net using
leave-one-subject-out cross-validation setting on the public dataset.
Experimental results shows that the proposed method outperforms traditional
U-Net (i.e. 80.9% vs 78.3% in averaged Dice score, 4.35mm vs 11.59mm in
averaged robust Hausdorff distance) and is computationally efficient.
Multi-label Object Attribute Classification using a Convolutional Neural Network
Authors: Soubarna Banik, Mikko Lauri, Simone Frintrop
Date: 2018-11-10 00:00:00
Tasks: Attribute, General Classification, Object, Object Recognition

Objects of different classes can be described using a limited number of
attributes such as color, shape, pattern, and texture. Learning to detect
object attributes instead of only detecting objects can be helpful in dealing
with a priori unknown objects. With this inspiration, a deep convolutional
neural network for low-level object attribute classification, called the Deep
Attribute Network (DAN), is proposed. Since object features are implicitly
learned by object recognition networks, one such existing network is modified
and fine-tuned for developing DAN. The performance of DAN is evaluated on the
ImageNet Attribute and a-Pascal datasets. Experiments show that in comparison
with state-of-the-art methods, the proposed model achieves better results.