pwc-archive/papers-with-abstracts huggingface dataset:
Localization: A Missing Link in the Pipeline of Object Matching and Registration
Authors: Deepak Mishra, Rajeev Ranjan, Santanu Chaudhury, Mukul Sarkar, Arvinder Singh Soin
Date: 2018-05-01 00:00:00
Tasks: Image Registration, Object, Object Localization

Image registration is a process of aligning two or more images of same
objects using geometric transformation. Most of the existing approaches work on
the assumption of location invariance. These approaches require object-centric
images to perform matching. Further, in absence of intensity level symmetry
between the corresponding points in two images, the learning based registration
approaches rely on synthetic deformations, which often fail in real scenarios.
To address these issues, a combination of convolutional neural networks (CNNs)
to perform the desired registration is developed in this work. The complete
objective is divided into three sub-objectives: object localization,
segmentation and matching transformation. Object localization step establishes
an initial correspondence between the images. A modified version of single shot
multi-box detector is used for this purpose. The detected region is cropped to
make the images object-centric. Subsequently, the objects are segmented and
matched using a spatial transformer network employing thin plate spline
deformation. Initial experiments on MNIST and Caltech-101 datasets show that
the proposed model is able to produce accurate matching. Quantitative
evaluation performed using dice coefficient (DC) and mean intersection over
union (mIoU) show that proposed method results in the values of 79% and 66%,
respectively for MNIST dataset and the values of 94% and 90%, respectively for
Caltech-101 dataset. The proposed framework is extended to the registration of
CT and US images, which is free from any data specific assumptions and has
better generalization capability as compared to the existing
rule-based/classical approaches.
Privately Learning High-Dimensional Distributions
Authors: Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman
Date: 2018-05-01 00:00:00
Tasks: Sensitivity, Vocal Bursts Intensity Prediction

We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and learning a product distribution over the Boolean hypercube in total variation distance. The sample complexity of our algorithms nearly matches the sample complexity of the optimal non-private learners for these tasks in a wide range of parameters, showing that privacy comes essentially for free for these problems. In particular, in contrast to previous approaches, our algorithm for learning Gaussians does not require strong a priori bounds on the range of the parameters. Our algorithms introduce a novel technical approach to reducing the sensitivity of the estimation procedure that we call recursive private preconditioning.
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition
Authors: Jian Liu, Naveed Akhtar, Ajmal Mian
Date: 2017-07-04 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

We propose Human Pose Models that represent RGB and depth images of human
poses independent of clothing textures, backgrounds, lighting conditions, body
shapes and camera viewpoints. Learning such universal models requires training
images where all factors are varied for every human pose. Capturing such data
is prohibitively expensive. Therefore, we develop a framework for synthesizing
the training data. First, we learn representative human poses from a large
corpus of real motion captured human skeleton data. Next, we fit synthetic 3D
humans with different body shapes to each pose and render each from 180 camera
viewpoints while randomly varying the clothing textures, background and
lighting. Generative Adversarial Networks are employed to minimize the gap
between synthetic and real image distributions. CNN models are then learned
that transfer human poses to a shared high-level invariant space. The learned
CNN models are then used as invariant feature extractors from real RGB and
depth frames of human action videos and the temporal variations are modelled by
Fourier Temporal Pyramid. Finally, linear SVM is used for classification.
Experiments on three benchmark cross-view human action datasets show that our
algorithm outperforms existing methods by significant margins for RGB only and
RGB-D action recognition.
Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning
Authors: Takumi Akazaki, Shuang Liu, Yoriyuki Yamagata, Yihai Duan, Jianye Hao
Date: 2018-05-01 00:00:00
Tasks: Deep Reinforcement Learning, Distributed Computing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With the rapid development of software and distributed computing,
Cyber-Physical Systems (CPS) are widely adopted in many application areas,
e.g., smart grid, autonomous automobile. It is difficult to detect defects in
CPS models due to the complexities involved in the software and physical
systems. To find defects in CPS models efficiently, robustness guided
falsification of CPS is introduced. Existing methods use several optimization
techniques to generate counterexamples, which falsify the given properties of a
CPS. However those methods may require a large number of simulation runs to
find the counterexample and is far from practical. In this work, we explore
state-of-the-art Deep Reinforcement Learning (DRL) techniques to reduce the
number of simulation runs required to find such counterexamples. We report our
method and the preliminary evaluation results.
Certifying Some Distributional Robustness with Principled Adversarial Training
Authors: Aman Sinha, Hongseok Namkoong, Riccardo Volpi, John Duchi
Date: 2017-10-29 00:00:00
Proceeding: ICLR 2018 1

Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We address this problem through the principled lens of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangian penalty formulation of perturbing the underlying data distribution in a Wasserstein ball, we provide a training procedure that augments model parameter updates with worst-case perturbations of training data. For smooth losses, our procedure provably achieves moderate levels of robustness with little computational or statistical cost relative to empirical risk minimization. Furthermore, our statistical guarantees allow us to efficiently certify robustness for the population loss. For imperceptible perturbations, our method matches or outperforms heuristic approaches.
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols
Authors: Chaitanya Kulkarni, Wei Xu, Alan Ritter, Raghu Machiraju
Date: 2018-05-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: BIG-bench Machine Learning, Reading Comprehension, Semantic Parsing

We describe an effort to annotate a corpus of natural language instructions
consisting of 622 wet lab protocols to facilitate automatic or semi-automatic
conversion of protocols into a machine-readable format and benefit biological
research. Experimental results demonstrate the utility of our corpus for
developing machine learning approaches to shallow semantic parsing of
instructional texts. We make our annotated Wet Lab Protocol Corpus available to
the research community.
Generalizing Across Domains via Cross-Gradient Training
Authors: Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, Sunita Sarawagi
Date: 2018-04-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation, Domain Adaptation, Domain Generalization

We present CROSSGRAD, a method to use multi-domain training data to learn a
classifier that generalizes to new domains. CROSSGRAD does not need an
adaptation phase via labeled or unlabeled data, or domain features in the new
domain. Most existing domain adaptation methods attempt to erase domain signals
using techniques like domain adversarial training. In contrast, CROSSGRAD is
free to use domain signals for predicting labels, if it can prevent overfitting
on training domains. We conceptualize the task in a Bayesian setting, in which
a sampling step is implemented as data augmentation, based on domain-guided
perturbations of input instances. CROSSGRAD parallelly trains a label and a
domain classifier on examples perturbed by loss gradients of each other's
objectives. This enables us to directly perturb inputs, without separating and
re-mixing domain signals while making various distributional assumptions.
Empirical evaluation on three different applications where this setting is
natural establishes that (1) domain-guided perturbation provides consistently
better generalization to unseen domains, compared to generic instance
perturbation methods, and that (2) data augmentation is a more stable and
accurate method than domain adversarial training.
"I ain't tellin' white folks nuthin": A quantitative exploration of the race-related problem of candour in the WPA slave narratives
Authors: Soumya Kambhampati
Date: 2018-05-01 00:00:00
Tasks: Sentiment Analysis

From 1936-38, the Works Progress Administration interviewed thousands of
former slaves about their life experiences. While these interviews are crucial
to understanding the "peculiar institution" from the standpoint of the slave
himself, issues relating to bias cloud analyses of these interviews. The
problem I investigate is the problem of candour in the WPA slave narratives: it
is widely held in the historical community that the strict racial caste system
of the Deep South compelled black ex-slaves to tell white interviewers what
they thought they wanted to hear, suggesting that there was a significant
difference candour depending on whether their interviewer was white or black.
In this work, I attempt to quantitatively characterise this race-related
problem of candour. Prior work has either been of an impressionistic,
qualitative nature, or utilised exceedingly simple quantitative methodology. In
contrast, I use more sophisticated statistical methods: in particular word
frequency and sentiment analysis and comparative topic modelling with LDA to
try and identify differences in the content and sentiment expressed by
ex-slaves in front of white interviewers versus black interviewers. While my
sentiment analysis methodology was ultimately unsuccessful due to the
complexity of the task, my word frequency analysis and comparative topic
modelling methods both showed strong evidence that the content expressed in
front of white interviewers was different from that of black interviewers. In
particular, I found that the ex-slaves spoke much more about unfavourable
aspects of slavery like whipping and slave patrollers in front of interviewers
of their own race. I hope that my more-sophisticated statistical methodology
helps improve the robustness of the argument for the existence of this problem
of candour in the slave narratives, which some would seek to deny for
revisionist purposes.
Fixation Data Analysis for High Resolution Satellite Images
Authors: Ashu Sharma, Jayanta Kumar Ghosh, Saptrarshi Kolay
Date: 2018-05-01 00:00:00
Tasks: Segmentation, Vocal Bursts Intensity Prediction

The presented study is an eye tracking experiment for high-resolution
satellite (HRS) images. The reported experiment explores the Area Of Interest
(AOI) based analysis of eye fixation data for complex HRS images. The study
reflects the requisite of reference data for bottom-up saliency-based
segmentation and the struggle of eye tracking data analysis for complex
satellite images. The intended fixation data analysis aims towards the
reference data creation for bottom-up saliency-based segmentation of
high-resolution satellite images. The analytical outcome of this experimental
study provides a solution for AOI-based analysis for fixation data in the
complex environment of satellite images and recommendations for reference data
construction which is already an ongoing effort.
Phylotastic: An Experiment in Creating, Manipulating, and Evolving Phylogenetic Biology Workflows Using Logic Programming
Authors: Thanh Hai Nguyen, Enrico Pontelli, Tran Cao Son
Date: 2018-05-01 00:00:00

Evolutionary Biologists have long struggled with the challenge of developing
analysis workflows in a flexible manner, thus facilitating the reuse of
phylogenetic knowledge. An evolutionary biology workflow can be viewed as a
plan which composes web services that can retrieve, manipulate, and produce
phylogenetic trees. The Phylotastic project was launched two years ago as a
collaboration between evolutionary biologists and computer scientists, with the
goal of developing an open architecture to facilitate the creation of such
analysis workflows. While composition of web services is a problem that has
been extensively explored in the literature, including within the logic
programming domain, the incarnation of the problem in Phylotastic provides a
number of additional challenges. Along with the need to integrate preferences
and formal ontologies in the description of the desired workflow, evolutionary
biologists tend to construct workflows in an incremental manner, by
successively refining the workflow, by indicating desired changes (e.g.,
exclusion of certain services, modifications of the desired output). This leads
to the need of successive iterations of incremental replanning, to develop a
new workflow that integrates the requested changes while minimizing the changes
to the original workflow. This paper illustrates how Phylotastic has addressed
the challenges of creating and refining phylogenetic analysis workflows using
logic programming technology and how such solutions have been used within the
general framework of the Phylotastic project. Under consideration in Theory and
Practice of Logic Programming (TPLP).
Compact Factorization of Matrices Using Generalized Round-Rank
Authors: Pouya Pezeshkpour, Carlos Guestrin, Sameer Singh
Date: 2018-05-01 00:00:00

Matrix factorization is a well-studied task in machine learning for compactly
representing large, noisy data. In our approach, instead of using the
traditional concept of matrix rank, we define a new notion of link-rank based
on a non-linear link function used within factorization. In particular, by
applying the round function on a factorization to obtain ordinal-valued
matrices, we introduce generalized round-rank (GRR). We show that not only are
there many full-rank matrices that are low GRR, but further, that these
matrices cannot be approximated well by low-rank linear factorization. We
provide uniqueness conditions of this formulation and provide gradient
descent-based algorithms. Finally, we present experiments on real-world
datasets to demonstrate that the GRR-based factorization is significantly more
accurate than linear factorization, while converging faster and using lower
rank representations.
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation
Authors: Rui Wang, Masao Utiyama, Eiichiro Sumita
Date: 2018-05-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, Sentence, Translation

Traditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch. In reality, some sentences are well-learned during the initial few epochs; however, using this approach, the well-learned sentences would continue to be trained along with those sentences that were not well learned for 10-30 epochs, which results in a wastage of time. Here, we propose an efficient method to dynamically sample the sentences in order to accelerate the NMT training. In this approach, a weight is assigned to each sentence based on the measured difference between the training costs of two iterations. Further, in each epoch, a certain percentage of sentences are dynamically sampled according to their weights. Empirical results based on the NIST Chinese-to-English and the WMT English-to-German tasks depict that the proposed method can significantly accelerate the NMT training and improve the NMT performance.
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
Authors: S. F. B. Pinto, R. C. de Lamare
Date: 2018-05-01 00:00:00

In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.
Convolutional Neural Network Architectures for Signals Supported on Graphs
Authors: Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro
Date: 2018-05-01 00:00:00
Tasks: Authorship Attribution, Graph Neural Network

Two architectures that generalize convolutional neural networks (CNNs) for
the processing of signals supported on graphs are introduced. We start with the
selection graph neural network (GNN), which replaces linear time invariant
filters with linear shift invariant graph filters to generate convolutional
features and reinterprets pooling as a possibly nonlinear subsampling stage
where nearby nodes pool their information in a set of preselected sample nodes.
A key component of the architecture is to remember the position of sampled
nodes to permit computation of convolutional features at deeper layers. The
second architecture, dubbed aggregation GNN, diffuses the signal through the
graph and stores the sequence of diffused components observed by a designated
node. This procedure effectively aggregates all components into a stream of
information having temporal structure to which the convolution and pooling
stages of regular CNNs can be applied. A multinode version of aggregation GNNs
is further introduced for operation in large scale graphs. An important
property of selection and aggregation GNNs is that they reduce to conventional
CNNs when particularized to time signals reinterpreted as graph signals in a
circulant graph. Comparative numerical analyses are performed in a source
localization application over synthetic and real-world networks. Performance is
also evaluated for an authorship attribution problem and text category
classification. Multinode aggregation GNNs are consistently the best performing
GNN architecture.
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Authors: Zheng Zhang, Minlie Huang, Zhongzhou Zhao, Feng Ji, Haiqing Chen, Xiaoyan Zhu
Date: 2018-05-01 00:00:00
Tasks: Dialogue Management, Management, Task-Oriented Dialogue Systems

Dialogue management (DM) decides the next action of a dialogue system
according to the current dialogue state, and thus plays a central role in
task-oriented dialogue systems. Since dialogue management requires to have
access to not only local utterances, but also the global semantics of the
entire dialogue session, modeling the long-range history information is a
critical issue. To this end, we propose a novel Memory-Augmented Dialogue
management model (MAD) which employs a memory controller and two additional
memory structures, i.e., a slot-value memory and an external memory. The
slot-value memory tracks the dialogue state by memorizing and updating the
values of semantic slots (for instance, cuisine, price, and location), and the
external memory augments the representation of hidden states of traditional
recurrent neural networks through storing more context information. To update
the dialogue state efficiently, we also propose slot-level attention on user
utterances to extract specific semantic information for each slot. Experiments
show that our model can obtain state-of-the-art performance and outperforms
existing baselines.
CRAFT: Complementary Recommendations Using Adversarial Feature Transformer
Authors: Cong Phuoc Huynh, Arridhana Ciptadi, Ambrish Tyagi, Amit Agrawal
Date: 2018-04-29 00:00:00

Traditional approaches for complementary product recommendations rely on
behavioral and non-visual data such as customer co-views or co-buys. However,
certain domains such as fashion are primarily visual. We propose a framework
that harnesses visual cues in an unsupervised manner to learn the distribution
of co-occurring complementary items in real world images. Our model learns a
non-linear transformation between the two manifolds of source and target
complementary item categories (e.g., tops and bottoms in outfits). Given a
large dataset of images containing instances of co-occurring object categories,
we train a generative transformer network directly on the feature
representation space by casting it as an adversarial optimization problem. Such
a conditional generative model can produce multiple novel samples of
complementary items (in the feature space) for a given query item. The final
recommendations are selected from the closest real world examples to the
synthesized complementary features. We apply our framework to the task of
recommending complementary tops for a given bottom clothing item. The
recommendations made by our system are diverse, and are favored by human
experts over the baseline approaches.
Homocentric Hypersphere Feature Embedding for Person Re-identification
Authors: Wangmeng Xiang, Jianqiang Huang, Xianbiao Qi, Xian-Sheng Hua, Lei Zhang
Date: 2018-04-24 00:00:00
Tasks: Person Re-Identification, Triplet

Person re-identification (Person ReID) is a challenging task due to the large
variations in camera viewpoint, lighting, resolution, and human pose. Recently,
with the advancement of deep learning technologies, the performance of Person
ReID has been improved swiftly. Feature extraction and feature matching are two
crucial components in the training and deployment stages of Person ReID.
However, many existing Person ReID methods have measure inconsistency between
the training stage and the deployment stage, and they couple magnitude and
orientation information of feature vectors in feature representation.
Meanwhile, traditional triplet loss methods focus on samples within a
mini-batch and lack knowledge of global feature distribution. To address these
issues, we propose a novel homocentric hypersphere embedding scheme to decouple
magnitude and orientation information for both feature and weight vectors, and
reformulate classification loss and triplet loss to their angular versions and
combine them into an angular discriminative loss. We evaluate our proposed
method extensively on the widely used Person ReID benchmarks, including
Market1501, CUHK03 and DukeMTMC-ReID. Our method demonstrates leading
performance on all datasets.