pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network
Authors: Raehyun Kim, Hyunjae Kim, Janghyuk Lee, Jaewoo Kang
Date: 2019-03-25 00:00:00
Tasks: Attribute, Decision Making, Multi-Task Learning

Most companies utilize demographic information to develop their strategy in a
market. However, such information is not available to most retail companies.
Several studies have been conducted to predict the demographic attributes of
users from their transaction histories, but they have some limitations. First,
they focused on parameter sharing to predict all attributes but capturing
task-specific features is also important in multi-task learning. Second, they
assumed that all transactions are equally important in predicting demographic
attributes. However, some transactions are more useful than others for
predicting a certain attribute. Furthermore, decision making process of models
cannot be interpreted as they work in a black-box manner. To address the
limitations, we propose an Embedding Transformation Network with Attention
(ETNA) model which shares representations at the bottom of the model structure
and transforms them to task-specific representations using a simple linear
transformation method. In addition, we can obtain more informative transactions
for predicting certain attributes using the attention mechanism. The
experimental results show that our model outperforms the previous models on all
tasks. In our qualitative analysis, we show the visualization of attention
weights, which provides business managers with some useful insights.
Unconstrained Facial Action Unit Detection via Latent Feature Domain
Authors: Zhiwen Shao, Jianfei Cai, Tat-Jen Cham, Xuequan Lu, Lizhuang Ma
Date: 2019-03-25 00:00:00
Tasks: Action Unit Detection, Domain Adaptation, Facial Action Unit Detection, Image Generation

Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate annotations. Most existing methods depend on either impractical labor-intensive labeling or inaccurate pseudo labels. In this paper, we propose an end-to-end unconstrained facial AU detection framework based on domain adaptation, which transfers accurate AU labels from a constrained source domain to an unconstrained target domain by exploiting labels of AU-related facial landmarks. Specifically, we map a source image with label and a target image without label into a latent feature domain by combining source landmark-related feature with target landmark-free feature. Due to the combination of source AU-related information and target AU-free information, the latent feature domain with transferred source label can be learned by maximizing the target-domain AU detection performance. Moreover, we introduce a novel landmark adversarial loss to disentangle the landmark-free feature from the landmark-related feature by treating the adversarial learning as a multi-player minimax game. Our framework can also be naturally extended for use with target-domain pseudo AU labels. Extensive experiments show that our method soundly outperforms lower-bounds and upper-bounds of the basic model, as well as state-of-the-art approaches on the challenging in-the-wild benchmarks. The code is available at https://github.com/ZhiwenShao/ADLD.
Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment
Authors: Chunyang Feng, Yufeng Sun, Xin Li
Date: 2019-03-25 00:00:00
Tasks: Iris Segmentation, Region Proposal, Segmentation

Despite the significant advances in iris segmentation, accomplishing accurate
iris segmentation in non-cooperative environment remains a grand challenge. In
this paper, we present a deep learning framework, referred to as Iris R-CNN, to
offer superior accuracy for iris segmentation. The proposed framework is
derived from Mask R-CNN, and several novel techniques are proposed to carefully
explore the unique characteristics of iris. First, we propose two novel
networks: (i) Double-Circle Region Proposal Network (DC-RPN), and (ii)
Double-Circle Classification and Regression Network (DC-CRN) to take into
account the iris and pupil circles to maximize the accuracy for iris
segmentation. Second, we propose a novel normalization scheme for Regions of
Interest (RoIs) to facilitate a radically new pooling operation over a
double-circle region. Experimental results on two challenging iris databases,
UBIRIS.v2 and MICHE, demonstrate the superior accuracy of the proposed approach
over other state-of-the-art methods.
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information
Authors: Dwarikanath Mahapatra, ZongYuan Ge
Date: 2019-03-25 00:00:00
Tasks: Image Registration, Medical Image Analysis, Transfer Learning

Registration is an important task in automated medical image analysis.
Although deep learning (DL) based image registration methods out perform time
consuming conventional approaches, they are heavily dependent on training data
and do not generalize well for new images types. We present a DL based approach
that can register an image pair which is different from the training images.
This is achieved by training generative adversarial networks (GANs) in
combination with segmentation information and transfer learning. Experiments on
chest Xray and brain MR images show that our method gives better registration
performance over conventional methods.
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
Authors: Yongqin Xian, Saurabh Sharma, Bernt Schiele, Zeynep Akata
Date: 2019-03-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Data Augmentation, Few-Shot Learning, Generalized Zero-Shot Learning, Transductive Learning, Zero-Shot Learning

When labeled training data is scarce, a promising data augmentation approach
is to generate visual features of unknown classes using their attributes. To
learn the class conditional distribution of CNN features, these models rely on
pairs of image features and class attributes. Hence, they can not make use of
the abundance of unlabeled data samples. In this paper, we tackle any-shot
learning problems i.e. zero-shot and few-shot, in a unified feature generating
framework that operates in both inductive and transductive learning settings.
We develop a conditional generative model that combines the strength of VAE and
GANs and in addition, via an unconditional discriminator, learns the marginal
feature distribution of unlabeled images. We empirically show that our model
learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA
and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e.
inductive and transductive (generalized) zero- and few-shot learning settings.
We also demonstrate that our learned features are interpretable: we visualize
them by inverting them back to the pixel space and we explain them by
generating textual arguments of why they are associated with a certain label.
Recurrent Back-Projection Network for Video Super-Resolution
Authors: Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita
Date: 2019-03-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Image Super-Resolution, Super-Resolution, Video Super-Resolution

We proposed a novel architecture for the problem of video super-resolution.
We integrate spatial and temporal contexts from continuous video frames using a
recurrent encoder-decoder module, that fuses multi-frame information with the
more traditional, single frame super-resolution path for the target frame. In
contrast to most prior work where frames are pooled together by stacking or
warping, our model, the Recurrent Back-Projection Network (RBPN) treats each
context frame as a separate source of information. These sources are combined
in an iterative refinement framework inspired by the idea of back-projection in
multiple-image super-resolution. This is aided by explicitly representing
estimated inter-frame motion with respect to the target, rather than explicitly
aligning frames. We propose a new video super-resolution benchmark, allowing
evaluation at a larger scale and considering videos in different motion
regimes. Experimental results demonstrate that our RBPN is superior to existing
methods on several datasets.
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction
Authors: Peng Xu, Denilson Barbosa
Date: 2019-03-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Link Prediction, Relation, Relation Extraction

Knowledge Bases (KBs) require constant up-dating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Authors: Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing
Date: 2019-03-25 00:00:00
Tasks: Graph Learning, Knowledge Graphs, Language Modeling, Language Modelling, Medical Report Generation, Retrieval

Generating long and semantic-coherent reports to describe medical images
poses great challenges towards bridging visual and linguistic modalities,
incorporating medical domain knowledge, and generating realistic and accurate
descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase
(KERP) approach which reconciles traditional knowledge- and retrieval-based
methods with modern learning-based methods for accurate and robust medical
report generation. Specifically, KERP decomposes medical report generation into
explicit medical abnormality graph learning and subsequent natural language
modeling. KERP first employs an Encode module that transforms visual features
into a structured abnormality graph by incorporating prior medical knowledge;
then a Retrieve module that retrieves text templates based on the detected
abnormalities; and lastly, a Paraphrase module that rewrites the templates
according to specific cases. The core of KERP is a proposed generic
implementation unit---Graph Transformer (GTR) that dynamically transforms
high-level semantics between graph-structured data of multiple domains such as
knowledge graphs, images and sequences. Experiments show that the proposed
approach generates structured and robust reports supported with accurate
abnormality description and explainable attentive regions, achieving the
state-of-the-art results on two medical report benchmarks, with the best
medical abnormality and disease classification accuracy and improved human
evaluation performance.
End-to-End Learning Using Cycle Consistency for Image-to-Caption Transformations
Authors: Keisuke Hagiwara, Yusuke Mukuta, Tatsuya Harada
Date: 2019-03-25 00:00:00

So far, research to generate captions from images has been carried out from
the viewpoint that a caption holds sufficient information for an image. If it
is possible to generate an image that is close to the input image from a
generated caption, i.e., if it is possible to generate a natural language
caption containing sufficient information to reproduce the image, then the
caption is considered to be faithful to the image. To make such regeneration
possible, learning using the cycle-consistency loss is effective. In this
study, we propose a method of generating captions by learning end-to-end mutual
transformations between images and texts. To evaluate our method, we perform
comparative experiments with and without the cycle consistency. The results are
evaluated by an automatic evaluation and crowdsourcing, demonstrating that our
proposed method is effective.
Fiducia: A Personalized Food Recommender System for Zomato
Authors: Mansi Goel, Ayush Agarwal, Deepak Thukral, Tanmoy Chakraborty
Date: 2019-03-25 00:00:00
Tasks: Recommendation Systems

This paper presents Fiducia, a food review system involving a pipeline which
processes restaurant-related reviews obtained from Zomato (India's largest
restaurant search and discovery service). Fiducia is specific to popular cafe
food items and manages to identify relevant information pertaining to each item
separately in the reviews. It uses a sentiment check on these pieces of text
and accordingly suggests an appropriate restaurant for the particular item
depending on user-item and item-item similarity. Experimental results show that
the sentiment analyzer module of Fiducia achieves an accuracy of over 85% and
our final recommender system achieves an RMSE of about 1.01 beating other
baselines.
Argument Mining for Understanding Peer Reviews
Authors: Xinyu Hua, Mitko Nikolov, Nikhil Badugu, Lu Wang
Date: 2019-03-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Argument Mining

Peer-review plays a critical role in the scientific writing and publication
ecosystem. To assess the efficiency and efficacy of the reviewing process, one
essential element is to understand and evaluate the reviews themselves. In this
work, we study the content and structure of peer reviews under the argument
mining framework, through automatically detecting (1) argumentative
propositions put forward by reviewers, and (2) their types (e.g., evaluating
the work or making suggestions for improvement). We first collect 14.2K reviews
from major machine learning and natural language processing venues. 400 reviews
are annotated with 10,386 propositions and corresponding types of Evaluation,
Request, Fact, Reference, or Quote. We then train state-of-the-art proposition
segmentation and classification models on the data to evaluate their utilities
and identify new challenges for this new domain, motivating future directions
for argument mining. Further experiments show that proposition usage varies
across venues in amount, type, and topic.
Functional Generative Design of Mechanisms with Recurrent Neural Networks and Novelty Search
Authors: Cameron R. Wolfe, Cem C. Tutum, Risto Miikkulainen
Date: 2019-03-25 00:00:00

Consumer-grade 3D printers have made it easier to fabricate aesthetic objects
and static assemblies, opening the door to automated design of such objects.
However, while static designs are easily produced with 3D printing, functional
designs with moving parts are more difficult to generate: The search space is
too high-dimensional, the resolution of the 3D-printed parts is not adequate,
and it is difficult to predict the physical behavior of imperfect 3D-printed
mechanisms. An example challenge is to produce a diverse set of reliable and
effective gear mechanisms that could be used after production without extensive
post-processing. To meet this challenge, an indirect encoding based on a
Recurrent Neural Network (RNN) is created and evolved using novelty search. The
elite solutions of each generation are 3D printed to evaluate their functional
performance on a physical test platform. The system is able to discover
sequential design rules that are difficult to discover with other methods.
Compared to direct encoding evolved with Genetic Algorithms (GAs), its designs
are geometrically more diverse and functionally more effective. It therefore
forms a promising foundation for the generative design of 3D-printed,
functional mechanisms.
A Higher-Order Kolmogorov-Smirnov Test
Authors: Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani
Date: 2019-03-24 00:00:00

We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which
can be more sensitive to differences in the tails. Our test statistic is an
integral probability metric (IPM) defined over a higher-order total variation
ball, recovering the original KS test as its simplest case. We give an exact
representer result for our IPM, which generalizes the fact that the original KS
test statistic can be expressed in equivalent variational and CDF forms. For
small enough orders ($k \leq 5$), we develop a linear-time algorithm for
computing our higher-order KS test statistic; for all others ($k \geq 6$), we
give a nearly linear-time approximation. We derive the asymptotic null
distribution for our test, and show that our nearly linear-time approximation
shares the same asymptotic null. Lastly, we complement our theory with
numerical studies.
Residual Non-local Attention Networks for Image Restoration
Authors: Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, Yun Fu
Date: 2019-03-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Demosaicking, Denoising, Image Denoising, Image Restoration, Super-Resolution

In this paper, we propose a residual non-local attention network for
high-quality image restoration. Without considering the uneven distribution of
information in the corrupted images, previous methods are restricted by local
convolutional operation and equal treatment of spatial- and channel-wise
features. To address this issue, we design local and non-local attention blocks
to extract features that capture the long-range dependencies between pixels and
pay more attention to the challenging parts. Specifically, we design trunk
branch and (non-)local mask branch in each (non-)local attention block. The
trunk branch is used to extract hierarchical features. Local and non-local mask
branches aim to adaptively rescale these hierarchical features with mixed
attentions. The local mask branch concentrates on more local structures with
convolutional operations, while non-local attention considers more about
long-range dependencies in the whole feature map. Furthermore, we propose
residual local and non-local attention learning to train the very deep network,
which further enhance the representation ability of the network. Our proposed
method can be generalized for various image restoration applications, such as
image denoising, demosaicing, compression artifacts reduction, and
super-resolution. Experiments demonstrate that our method obtains comparable or
better results compared with recently leading methods quantitatively and
visually.
Truly Batch Apprenticeship Learning with Deep Successor Features
Authors: Dong-hun Lee, Srivatsan Srinivasan, Finale Doshi-Velez
Date: 2019-03-24 00:00:00
Tasks: Management

We introduce a novel apprenticeship learning algorithm to learn an expert's
underlying reward structure in off-policy model-free \emph{batch} settings.
Unlike existing methods that require a dynamics model or additional data
acquisition for on-policy evaluation, our algorithm requires only the batch
data of observed expert behavior. Such settings are common in real-world
tasks---health care, finance or industrial processes ---where accurate
simulators do not exist or data acquisition is costly. To address challenges in
batch settings, we introduce Deep Successor Feature Networks(DSFN) that
estimate feature expectations in an off-policy setting and a
transition-regularized imitation network that produces a near-expert initial
policy and an efficient feature representation. Our algorithm achieves superior
results in batch settings on both control benchmarks and a vital clinical task
of sepsis management in the Intensive Care Unit.
Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks
Authors: Kenta Oono, Taiji Suzuki
Date: 2019-03-24 00:00:00
Tasks: Vocal Bursts Type Prediction

Convolutional neural networks (CNNs) have been shown to achieve optimal approximation and estimation error rates (in minimax sense) in several function classes. However, previous analyzed optimal CNNs are unrealistically wide and difficult to obtain via optimization due to sparse constraints in important function classes, including the H\"older class. We show a ResNet-type CNN can attain the minimax optimal error rates in these classes in more plausible situations -- it can be dense, and its width, channel size, and filter size are constant with respect to sample size. The key idea is that we can replicate the learning ability of Fully-connected neural networks (FNNs) by tailored CNNs, as long as the FNNs have \textit{block-sparse} structures. Our theory is general in a sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs. As an application, we derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and H\"older classes with the same strategy.
A Strongly Consistent Sparse $k$-means Clustering with Direct $l_1$ Penalization on Variable Weights
Authors: Saptarshi Chakraborty, Swagatam Das
Date: 2019-03-24 00:00:00
Tasks: Clustering, feature selection

We propose the Lasso Weighted $k$-means ($LW$-$k$-means) algorithm as a
simple yet efficient sparse clustering procedure for high-dimensional data
where the number of features ($p$) can be much larger compared to the number of
observations ($n$). In the $LW$-$k$-means algorithm, we introduce a lasso-based
penalty term, directly on the feature weights to incorporate feature selection
in the framework of sparse clustering. $LW$-$k$-means does not make any
distributional assumption of the given dataset and thus, induces a
non-parametric method for feature selection. We also analytically investigate
the convergence of the underlying optimization procedure in $LW$-$k$-means and
establish the strong consistency of our algorithm. $LW$-$k$-means is tested on
several real-life and synthetic datasets and through detailed experimental
analysis, we find that the performance of the method is highly competitive
against some state-of-the-art procedures for clustering and feature selection,
not only in terms of clustering accuracy but also with respect to computational
time.
Automated Classification of Histopathology Images Using Transfer Learning
Authors: Muhammed Talo
Date: 2019-03-24 00:00:00
Tasks: Classification, Diagnostic, General Classification, Image Classification, Transfer Learning, whole slide images

There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided diagnostic systems are a critical step in the early diagnosis and treatment of diseases. Once a pathology image is scanned by a microscope and loaded onto a computer, it can be used for automated detection and classification of diseases. In this study, the DenseNet-161 and ResNet-50 pre-trained CNN models have been used to classify digital histopathology patches into the corresponding whole slide images via transfer learning technique. The proposed pre-trained models were tested on grayscale and color histopathology images. The DenseNet-161 pre-trained model achieved a classification accuracy of 97.89% using grayscale images and the ResNet-50 model obtained the accuracy of 98.87% for color images. The proposed pre-trained models outperform state-of-the-art methods in all performance metrics to classify digital pathology patches into 24 categories.
A Formalization of Robustness for Deep Neural Networks
Authors: Tommaso Dreossi, Shromona Ghosh, Alberto Sangiovanni-Vincentelli, Sanjit A. Seshia
Date: 2019-03-24 00:00:00
Tasks: Adversarial Attack

Deep neural networks have been shown to lack robustness to small input
perturbations. The process of generating the perturbations that expose the lack
of robustness of neural networks is known as adversarial input generation. This
process depends on the goals and capabilities of the adversary, In this paper,
we propose a unifying formalization of the adversarial input generation process
from a formal methods perspective. We provide a definition of robustness that
is general enough to capture different formulations. The expressiveness of our
formalization is shown by modeling and comparing a variety of adversarial
attack techniques.