pwc-archive/papers-with-abstracts huggingface dataset:
Maximizing Invariant Data Perturbation with Stochastic Optimization
Authors: Kouichi Ikeno, Satoshi Hara
Date: 2018-07-12 00:00:00
Tasks: General Classification, image-classification, Image Classification, Stochastic Optimization

Feature attribution methods, or saliency maps, are one of the most popular
approaches for explaining the decisions of complex machine learning models such
as deep neural networks. In this study, we propose a stochastic optimization
approach for the perturbation-based feature attribution method. While the
original optimization problem of the perturbation-based feature attribution is
difficult to solve because of the complex constraints, we propose to
reformulate the problem as the maximization of a differentiable function, which
can be solved using gradient-based algorithms. In particular, stochastic
optimization is well-suited for the proposed reformulation, and we can solve
the problem using popular algorithms such as SGD, RMSProp, and Adam. The
experiment on the image classification with VGG16 shows that the proposed
method could identify relevant parts of the images effectively.
Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention
Authors: Xingyu Liao, Lingxiao He, Zhouwang Yang, Chi Zhang
Date: 2018-07-12 00:00:00
Tasks: Action Recognition, Person Re-Identification, Temporal Action Localization, Video-Based Person Re-Identification

Video-based person re-identification (ReID) is a challenging problem, where
some video tracks of people across non-overlapping cameras are available for
matching. Feature aggregation from a video track is a key step for video-based
person ReID. Many existing methods tackle this problem by average/maximum
temporal pooling or RNNs with attention. However, these methods cannot deal
with temporal dependency and spatial misalignment problems at the same time. We
are inspired by video action recognition that involves the identification of
different actions from video tracks. Firstly, we use 3D convolutions on video
volume, instead of using 2D convolutions across frames, to extract spatial and
temporal features simultaneously. Secondly, we use a non-local block to tackle
the misalignment problem and capture spatial-temporal long-range dependencies.
As a result, the network can learn useful spatial-temporal information as a
weighted sum of the features in all space and temporal positions in the input
feature map. Experimental results on three datasets show that our framework
outperforms state-of-the-art approaches by a large margin on multiple metrics.
Online Heart Rate Prediction using Acceleration from a Wrist Worn Wearable
Authors: Ryan McConville, Gareth Archer, Ian Craddock, Herman ter Horst, Robert Piechocki, James Pope, Raul Santos-Rodriguez
Date: 2018-06-25 00:00:00
Tasks: Photoplethysmography (PPG)

In this paper we study the prediction of heart rate from acceleration using a
wrist worn wearable. Although existing photoplethysmography (PPG) heart rate
sensors provide reliable measurements, they use considerably more energy than
accelerometers and have a major impact on battery life of wearable devices. By
using energy-efficient accelerometers to predict heart rate, significant energy
savings can be made. Further, we are interested in understanding patient
recovery after a heart rate intervention, where we expect a variation in heart
rate over time. Therefore, we propose an online approach to tackle the concept
as time passes. We evaluate the methods on approximately 4 weeks of free living
data from three patients over a number of months. We show that our approach can
achieve good predictive performance (e.g., 2.89 Mean Absolute Error) while
using the PPG heart rate sensor infrequently (e.g., 20.25% of the samples).
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection
Authors: Amir Soleimani, Nasser M. Nasrabadi
Date: 2018-07-16 00:00:00
Tasks: Action Detection, Object, Pedestrian Detection, Question Answering

The low resolution of objects of interest in aerial images makes pedestrian
detection and action detection extremely challenging tasks. Furthermore, using
deep convolutional neural networks to process large images can be demanding in
terms of computational requirements. In order to alleviate these challenges, we
propose a two-step, yes and no question answering framework to find specific
individuals doing one or multiple specific actions in aerial images. First, a
deep object detector, Single Shot Multibox Detector (SSD), is used to generate
object proposals from small aerial images. Second, another deep network, is
used to learn a latent common sub-space which associates the high resolution
aerial imagery and the pedestrian action labels that are provided by the
human-based sources
Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging
Authors: Zhongliu Xie
Date: 2018-07-16 00:00:00
Tasks: Transfer Learning

Detection of pulmonary nodules in chest CT imaging plays a crucial role in
early diagnosis of lung cancer. Manual examination is highly time-consuming and
error prone, calling for computer-aided detection, both to improve efficiency
and reduce misdiagnosis. Over the years, a range of systems have been proposed,
mostly following a two-phase paradigm with: 1) candidate detection, 2) false
positive reduction. Recently, deep learning has become a dominant force in
algorithm development. As for candidate detection, prior art was mainly based
on the two-stage Faster R-CNN framework, which starts with an initial sub-net
to generate a set of class-agnostic region proposals, followed by a second
sub-net to perform classification and bounding-box regression. In contrast, we
abandon the conventional two-phase paradigm and two-stage framework altogether
and propose to train a single network for end-to-end nodule detection instead,
without transfer learning or further post-processing. Our feature learning
model is a modification of the ResNet and feature pyramid network combined,
powered by RReLU activation. The major challenge is the condition of extreme
inter-class and intra-class sample imbalance, where the positives are
overwhelmed by a large negative pool, which is mostly composed of easy and a
handful of hard negatives. Direct training on all samples can seriously
undermine training efficacy. We propose a patch-based sampling strategy over a
set of regularly updating anchors, which narrows sampling scope to all
positives and only hard negatives, effectively addressing this issue. As a
result, our approach substantially outperforms prior art in terms of both
accuracy and speed. Finally, the prevailing FROC evaluation over [1/8, 1/4,
1/2, 1, 2, 4, 8] false positives per scan, is far from ideal in real clinical
environments. We suggest FROC over [1, 2, 4] false positives as a better
metric.
Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings
Authors: Debanjan Mahata, John Kuriakose, Rajiv Ratn Shah, Roger Zimmermann, John R. Talburt
Date: 2018-07-16 00:00:00
Tasks: Keyword Extraction

Keyword extraction is a fundamental task in natural language processing that
facilitates mapping of documents to a concise set of representative single and
multi-word phrases. Keywords from text documents are primarily extracted using
supervised and unsupervised approaches. In this paper, we present an
unsupervised technique that uses a combination of theme-weighted personalized
PageRank algorithm and neural phrase embeddings for extracting and ranking
keywords. We also introduce an efficient way of processing text documents and
training phrase embeddings using existing techniques. We share an evaluation
dataset derived from an existing dataset that is used for choosing the
underlying embedding model. The evaluations for ranked keyword extraction are
performed on two benchmark datasets comprising of short abstracts (Inspec), and
long scientific papers (SemEval 2010), and is shown to produce results better
than the state-of-the-art systems.
Meta-Learning with Latent Embedding Optimization
Authors: Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell
Date: 2018-07-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: Few-Shot Learning, Meta-Learning

Gradient-based meta-learning techniques are both widely applicable and
proficient at solving challenging few-shot learning and fast adaptation
problems. However, they have practical difficulties when operating on
high-dimensional parameter spaces in extreme low-data regimes. We show that it
is possible to bypass these limitations by learning a data-dependent latent
generative representation of model parameters, and performing gradient-based
meta-learning in this low-dimensional latent space. The resulting approach,
latent embedding optimization (LEO), decouples the gradient-based adaptation
procedure from the underlying high-dimensional space of model parameters. Our
evaluation shows that LEO can achieve state-of-the-art performance on the
competitive miniImageNet and tieredImageNet few-shot classification tasks.
Further analysis indicates LEO is able to capture uncertainty in the data, and
can perform adaptation more effectively by optimizing in latent space.
A Multimodal Approach to Predict Social Media Popularity
Authors: Mayank Meghawat, Satyendra Yadav, Debanjan Mahata, Yifang Yin, Rajiv Ratn Shah, Roger Zimmermann
Date: 2018-07-16 00:00:00
Tasks: Prediction

Multiple modalities represent different aspects by which information is
conveyed by a data source. Modern day social media platforms are one of the
primary sources of multimodal data, where users use different modes of
expression by posting textual as well as multimedia content such as images and
videos for sharing information. Multimodal information embedded in such posts
could be useful in predicting their popularity. To the best of our knowledge,
no such multimodal dataset exists for the prediction of social media photos. In
this work, we propose a multimodal dataset consisiting of content, context, and
social information for popularity prediction. Specifically, we augment the
SMPT1 dataset for social media prediction in ACM Multimedia grand challenge
2017 with image content, titles, descriptions, and tags. Next, in this paper,
we propose a multimodal approach which exploits visual features (i.e., content
information), textual features (i.e., contextual information), and social
features (e.g., average views and group counts) to predict popularity of social
media photos in terms of view counts. Experimental results confirm that despite
our multimodal approach uses the half of the training dataset from SMP-T1, it
achieves comparable performance with that of state-of-the-art.
Evolving Differentiable Gene Regulatory Networks
Authors: Dennis G Wilson, Kyle Harrington, Sylvain Cussat-Blanc, Hervé Luga
Date: 2018-07-16 00:00:00
Tasks: Artificial Life, BIG-bench Machine Learning, GPU

Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have
shown their capacity to solve real-world problems in various domains such as
agent control, signal processing and artificial life experiments. They have
also benefited from new evolutionary approaches and improvements to dynamic
which have increased their optimization efficiency. In this paper, we present
an additional step toward their usability in machine learning applications. We
detail an GPU-based implementation of differentiable GRNs, allowing for local
optimization of GRN architectures with stochastic gradient descent (SGD). Using
a standard machine learning dataset, we evaluate the ways in which evolution
and SGD can be combined to further GRN optimization. We compare these
approaches with neural network models trained by SGD and with support vector
machines.
Variational Inference: A Unified Framework of Generative Models and Some Revelations
Authors: Jianlin Su
Date: 2018-07-16 00:00:00
Tasks: Variational Inference

We reinterpreting the variational inference in a new perspective. Via this
way, we can easily prove that EM algorithm, VAE, GAN, AAE, ALI(BiGAN) are all
special cases of variational inference. The proof also reveals the loss of
standard GAN is incomplete and it explains why we need to train GAN cautiously.
From that, we find out a regularization term to improve stability of GAN
training.
Siamese Survival Analysis with Competing Risks
Authors: Anton Nemchenko, Trent Kyono, Mihaela van der Schaar
Date: 2018-07-16 00:00:00
Proceeding: ICLR 2018 1
Tasks: Prognosis, Survival Analysis

Survival analysis in the presence of multiple possible adverse events, i.e.,
competing risks, is a pervasive problem in many industries (healthcare,
finance, etc.). Since only one event is typically observed, the incidence of an
event of interest is often obscured by other related competing events. This
nonidentifiability, or inability to estimate true cause-specific survival
curves from empirical data, further complicates competing risk survival
analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep
learning architecture for estimating personalized risk scores in the presence
of competing risks. SSPN circumvents the nonidentifiability problem by avoiding
the estimation of cause-specific survival curves and instead determines
pairwise concordant time-dependent risks, where longer event times are assigned
lower risks. Furthermore, SSPN is able to directly optimize an approximation to
the C-discrimination index, rather than relying on well-known metrics which are
unable to capture the unique requirements of survival analysis with competing
risks.
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning
Authors: Paul Gay, Stuart James, Alessio Del Bue
Date: 2018-07-16 00:00:00
Tasks: 3d scene graph generation, Graph Generation, Scene Graph Generation, Scene Understanding

Recent approaches on visual scene understanding attempt to build a scene
graph -- a computational representation of objects and their pairwise
relationships. Such rich semantic representation is very appealing, yet
difficult to obtain from a single image, especially when considering complex
spatial arrangements in the scene. Differently, an image sequence conveys
useful information using the multi-view geometric relations arising from camera
motion. Indeed, in such cases, object relationships are naturally related to
the 3D scene structure. To this end, this paper proposes a system that first
computes the geometrical location of objects in a generic scene and then
efficiently constructs scene graphs from video by embedding such geometrical
reasoning. Such compelling representation is obtained using a new model where
geometric and visual features are merged using an RNN framework. We report
results on a dataset we created for the task of 3D scene graph generation in
multiple views.
Computationally Efficient Approaches for Image Style Transfer
Authors: Ram Krishna Pandey, Samarjit Karmakar, A. G. Ramakrishnan
Date: 2018-07-16 00:00:00
Tasks: Style Transfer

In this work, we have investigated various style transfer approaches and (i)
examined how the stylized reconstruction changes with the change of loss
function and (ii) provided a computationally efficient solution for the same.
We have used elegant techniques like depth-wise separable convolution in place
of convolution and nearest neighbor interpolation in place of transposed
convolution. Further, we have also added multiple interpolations in place of
transposed convolution. The results obtained are perceptually similar in
quality, while being computationally very efficient. The decrease in the
computational complexity of our architecture is validated by the decrease in
the testing time by 26.1%, 39.1%, and 57.1%, respectively.
Novel Feature-Based Clustering of Micro-Panel Data (CluMP)
Authors: Lukas Sobisek, Maria Stachova, Jan Fojtik
Date: 2018-07-16 00:00:00
Tasks: Clustering

Micro-panel data are collected and analysed in many research and industry
areas. Cluster analysis of micro-panel data is an unsupervised learning
exploratory method identifying subgroup clusters in a data set which include
homogeneous objects in terms of the development dynamics of monitored
variables. The supply of clustering methods tailored to micro-panel data is
limited. The present paper focuses on a feature-based clustering method,
introducing a novel two-step characteristic-based approach designed for this
type of data. The proposed CluMP method aims to identify clusters that are at
least as internally homogeneous and externally heterogeneous as those obtained
by alternative methods already implemented in the statistical system R. We
compare the clustering performance of the devised algorithm with two extant
methods using simulated micro-panel data sets. Our approach has yielded similar
or better outcomes than the other methods, the advantage of the proposed
algorithm being time efficiency which makes it applicable for large data sets.
Bipedal Walking Robot using Deep Deterministic Policy Gradient
Authors: Arun Kumar, Navneet Paul, S. N. Omkar
Date: 2018-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Machine learning algorithms have found several applications in the field of
robotics and control systems. The control systems community has started to show
interest towards several machine learning algorithms from the sub-domains such
as supervised learning, imitation learning and reinforcement learning to
achieve autonomous control and intelligent decision making. Amongst many
complex control problems, stable bipedal walking has been the most challenging
problem. In this paper, we present an architecture to design and simulate a
planar bipedal walking robot(BWR) using a realistic robotics simulator, Gazebo.
The robot demonstrates successful walking behaviour by learning through several
of its trial and errors, without any prior knowledge of itself or the world
dynamics. The autonomous walking of the BWR is achieved using reinforcement
learning algorithm called Deep Deterministic Policy Gradient(DDPG). DDPG is one
of the algorithms for learning controls in continuous action spaces. After
training the model in simulation, it was observed that, with a proper shaped
reward function, the robot achieved faster walking or even rendered a running
gait with an average speed of 0.83 m/s. The gait pattern of the bipedal walker
was compared with the actual human walking pattern. The results show that the
bipedal walking pattern had similar characteristics to that of a human walking
pattern. The video presenting our experiment is available at
https://goo.gl/NHXKqR.
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
Authors: Guiliang Liu, Oliver Schulte, Wang Zhu, Qingcan Li
Date: 2018-07-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (DRL) has achieved impressive success in many
applications. A key component of many DRL models is a neural network
representing a Q function, to estimate the expected cumulative reward following
a state-action pair. The Q function neural network contains a lot of implicit
knowledge about the RL problems, but often remains unexamined and
uninterpreted. To our knowledge, this work develops the first mimic learning
framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to
approximate neural network predictions. An LMUT is learned using a novel
on-line algorithm that is well-suited for an active play setting, where the
mimic learner observes an ongoing interaction between the neural net and the
environment. Empirical evaluation shows that an LMUT mimics a Q function
substantially better than five baseline methods. The transparent tree structure
of an LMUT facilitates understanding the network's learned knowledge by
analyzing feature influence, extracting rules, and highlighting the
super-pixels in image inputs.
Object Relation Detection Based on One-shot Learning
Authors: Li Zhou, Jian Zhao, Jianshu Li, Li Yuan, Jiashi Feng
Date: 2018-07-16 00:00:00
Tasks: Object, One-Shot Learning, Relation

Detecting the relations among objects, such as "cat on sofa" and "person ride
horse", is a crucial task in image understanding, and beneficial to bridging
the semantic gap between images and natural language. Despite the remarkable
progress of deep learning in detection and recognition of individual objects,
it is still a challenging task to localize and recognize the relations between
objects due to the complex combinatorial nature of various kinds of object
relations. Inspired by the recent advances in one-shot learning, we propose a
simple yet effective Semantics Induced Learner (SIL) model for solving this
challenging task. Learning in one-shot manner can enable a detection model to
adapt to a huge number of object relations with diverse appearance effectively
and robustly. In addition, the SIL combines bottom-up and top-down attention
mech- anisms, therefore enabling attention at the level of vision and semantics
favorably. Within our proposed model, the bottom-up mechanism, which is based
on Faster R-CNN, proposes objects regions, and the top-down mechanism selects
and integrates visual features according to semantic information. Experiments
demonstrate the effectiveness of our framework over other state-of-the-art
methods on two large-scale data sets for object relation detection.
Assessment of electrical and infrastructure recovery in Puerto Rico following hurricane Maria using a multisource time series of satellite imagery
Authors: Jacob Shermeyer
Date: 2018-07-16 00:00:00
Tasks: Change Detection, Time Series, Time Series Analysis

Puerto Rico suffered severe damage from the category 5 hurricane (Maria) in
September 2017. Total monetary damages are estimated to be ~92 billion USD, the
third most costly tropical cyclone in US history. The response to this damage
has been tempered and slow moving, with recent estimates placing 45% of the
population without power three months after the storm. Consequently, we
developed a unique data-fusion mapping approach called the Urban Development
Index (UDI) and new open source tool, Comet Time Series (CometTS), to analyze
the recovery of electricity and infrastructure in Puerto Rico. Our approach
incorporates a combination of time series visualizations and change detection
mapping to create depictions of power or infrastructure loss. It also provides
a unique independent assessment of areas that are still struggling to recover.
For this workflow, our time series approach combines nighttime imagery from the
Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer
Suite (NPP VIIRS), multispectral imagery from two Landsat satellites, US Census
data, and crowd-sourced building footprint labels. Based upon our approach we
can identify and evaluate: 1) the recovery of electrical power compared to
pre-storm levels, 2) the location of potentially damaged infrastructure that
has yet to recover from the storm, and 3) the number of persons without power
over time. As of May 31, 2018, declined levels of observed brightness across
the island indicate that 13.9% +/- ~5.6% of persons still lack power and/or
that 13.2% +/- ~5.3% of infrastructure has been lost. In comparison, the Puerto
Rico Electric Power Authority states that less than 1% of their customers still
are without power.