pwc-archive/papers-with-abstracts huggingface dataset:
Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids
Authors: Deepjyoti Deka, Michael Chertkov, Scott Backhaus
Date: 2018-03-17 00:00:00

Distribution grid is the medium and low voltage part of a large power system.
Structurally, the majority of distribution networks operate radially, such that
energized lines form a collection of trees, i.e. forest, with a substation
being at the root of any tree. The operational topology/forest may change from
time to time, however tracking these changes, even though important for the
distribution grid operation and control, is hindered by limited real-time
monitoring. This paper develops a learning framework to reconstruct radial
operational structure of the distribution grid from synchronized voltage
measurements in the grid subject to the exogenous fluctuations in nodal power
consumption. To detect operational lines our learning algorithm uses
conditional independence tests for continuous random variables that is
applicable to a wide class of probability distributions of the nodal
consumption and Gaussian injections in particular. Moreover, our algorithm
applies to the practical case of unbalanced three-phase power flow. Algorithm
performance is validated on AC power flow simulations over IEEE distribution
grid test cases.
Beyond the Low-Degree Algorithm: Mixtures of Subcubes and Their Applications
Authors: Sitan Chen, Ankur Moitra
Date: 2018-03-17 00:00:00
Tasks: Learning Theory

We introduce the problem of learning mixtures of $k$ subcubes over
$\{0,1\}^n$, which contains many classic learning theory problems as a special
case (and is itself a special case of others). We give a surprising $n^{O(\log
k)}$-time learning algorithm based on higher-order multilinear moments. It is
not possible to learn the parameters because the same distribution can be
represented by quite different models. Instead, we develop a framework for
reasoning about how multilinear moments can pinpoint essential features of the
mixture, like the number of components.
  We also give applications of our algorithm to learning decision trees with
stochastic transitions (which also capture interesting scenarios where the
transitions are deterministic but there are latent variables). Using our
algorithm for learning mixtures of subcubes, we can approximate the Bayes
optimal classifier within additive error $\epsilon$ on $k$-leaf decision trees
with at most $s$ stochastic transitions on any root-to-leaf path in $n^{O(s +
\log k)}\cdot\text{poly}(1/\epsilon)$ time. In this stochastic setting, the
classic Occam algorithms for learning decision trees with zero stochastic
transitions break down, while the low-degree algorithm of Linial et al.
inherently has a quasipolynomial dependence on $1/\epsilon$.
  In contrast, as we will show, mixtures of $k$ subcubes are uniquely
determined by their degree $2 \log k$ moments and hence provide a useful
abstraction for simultaneously achieving the polynomial dependence on
$1/\epsilon$ of the classic Occam algorithms for decision trees and the
flexibility of the low-degree algorithm in being able to accommodate stochastic
transitions. Using our multilinear moment techniques, we also give the first
improved upper and lower bounds since the work of Feldman et al. for the
related but harder problem of learning mixtures of binary product
distributions.
Provable Convex Co-clustering of Tensors
Authors: Eric C. Chi, Brian R. Gaines, Will Wei Sun, Hua Zhou, Jian Yang
Date: 2018-03-17 00:00:00
Tasks: Clustering, Computational Efficiency

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising "blessing of dimensionality" phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness.
Using 3D Hahn Moments as A Computational Representation of ATS Drugs Molecular Structure
Authors: Satrya Fajri Pratama, Azah Kamilah Muda, Yun-Huoy Choo, Ramon Carbó-Dorca, Ajith Abraham
Date: 2018-02-18 00:00:00

The campaign against drug abuse is fought by all countries, most notably on
ATS drugs. The technical limitations of the current test kits to detect new
brand of ATS drugs present a challenge to law enforcement authorities and
forensic laboratories. Meanwhile, new molecular imaging devices which allowed
mankind to characterize the physical 3D molecular structure have been recently
introduced, and it can be used to remedy the limitations of existing drug test
kits. Thus, a new type of 3D molecular structure representation technique
should be developed to cater the 3D molecular structure acquired physically
using these molecular imaging devices. One of the applications of image
processing methods to represent a 3D image is 3D moments, and this study
formulates a new 3D moments technique, namely 3D Hahn moments, to represent the
3D molecular structure of ATS drugs. The performance of the proposed technique
was analysed using drug chemical structures obtained from UNODC for the ATS
drugs, while non-ATS drugs are obtained randomly from ChemSpider database. The
evaluation shows the technique is qualified to be further explored in the
future works to be fully compatible with ATS drug identification domain.
Hidden Integrality and Semi-random Robustness of SDP Relaxation for Sub-Gaussian Mixture Model
Authors: Yingjie Fei, Yudong Chen
Date: 2018-03-17 00:00:00
Tasks: Clustering

We consider the problem of estimating the discrete clustering structures under the Sub-Gaussian Mixture Model. Our main results establish a hidden integrality property of a semidefinite programming (SDP) relaxation for this problem: while the optimal solution to the SDP is not integer-valued in general, its estimation error can be upper bounded by that of an idealized integer program. The error of the integer program, and hence that of the SDP, are further shown to decay exponentially in the signal-to-noise ratio. In addition, we show that the SDP relaxation is robust under the semi-random setting in which an adversary can modify the data generated from the mixture model. In particular, we generalize the hidden integrality property to the semi-random model and thereby show that SDP achieves the optimal error bound in this setting. These results together highlight the "global-to-local" mechanism that drives the performance of the SDP relaxation. To the best of our knowledge, our result is the first exponentially decaying error bound for convex relaxations of mixture models. A corollary of our results shows that in certain regimes the SDP solutions are in fact integral and exact. More generally, our results establish sufficient conditions for the SDP to correctly recover the cluster memberships of $(1-\delta)$ fraction of the points for any $\delta\in(0,1)$. As a special case, we show that under the $d$-dimensional Stochastic Ball Model, SDP achieves non-trivial (sometimes exact) recovery when the center separation is as small as $\sqrt{1/d}$, which improves upon previous exact recovery results that require constant separation.
MergeNet: A Deep Net Architecture for Small Obstacle Discovery
Authors: Krishnam Gupta, Syed Ashar Javed, Vineet Gandhi, K. Madhava Krishna
Date: 2018-03-17 00:00:00
Tasks: Autonomous Driving

We present here, a novel network architecture called MergeNet for discovering
small obstacles for on-road scenes in the context of autonomous driving. The
basis of the architecture rests on the central consideration of training with
less amount of data since the physical setup and the annotation process for
small obstacles is hard to scale. For making effective use of the limited data,
we propose a multi-stage training procedure involving weight-sharing, separate
learning of low and high level features from the RGBD input and a refining
stage which learns to fuse the obtained complementary features. The model is
trained and evaluated on the Lost and Found dataset and is able to achieve
state-of-art results with just 135 images in comparison to the 1000 images used
by the previous benchmark. Additionally, we also compare our results with
recent methods trained on 6000 images and show that our method achieves
comparable performance with only 1000 training samples.
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision
Authors: Syed Ashar Javed, Shreyas Saxena, Vineet Gandhi
Date: 2018-03-17 00:00:00
Tasks: Visual Grounding

Localizing natural language phrases in images is a challenging problem that
requires joint understanding of both the textual and visual modalities. In the
unsupervised setting, lack of supervisory signals exacerbate this difficulty.
In this paper, we propose a novel framework for unsupervised visual grounding
which uses concept learning as a proxy task to obtain self-supervision. The
simple intuition behind this idea is to encourage the model to localize to
regions which can explain some semantic property in the data, in our case, the
property being the presence of a concept in a set of images. We present
thorough quantitative and qualitative experiments to demonstrate the efficacy
of our approach and show a 5.6% improvement over the current state of the art
on Visual Genome dataset, a 5.8% improvement on the ReferItGame dataset and
comparable to state-of-art performance on the Flickr30k dataset.
Weakly Supervised Salient Object Detection Using Image Labels
Authors: Guanbin Li, Yuan Xie, Liang Lin
Date: 2018-03-17 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Unsupervised Saliency Detection

Deep learning based salient object detection has recently achieved great
success with its performance greatly outperforms any other unsupervised
methods. However, annotating per-pixel saliency masks is a tedious and
inefficient procedure. In this paper, we note that superior salient object
detection can be obtained by iteratively mining and correcting the labeling
ambiguity on saliency maps from traditional unsupervised methods. We propose to
use the combination of a coarse salient object activation map from the
classification network and saliency maps generated from unsupervised methods as
pixel-level annotation, and develop a simple yet very effective algorithm to
train fully convolutional networks for salient object detection supervised by
these noisy annotations. Our algorithm is based on alternately exploiting a
graphical model and training a fully convolutional network for model updating.
The graphical model corrects the internal labeling ambiguity through spatial
consistency and structure preserving while the fully convolutional network
helps to correct the cross-image semantic ambiguity and simultaneously update
the coarse activation map for next iteration. Experimental results demonstrate
that our proposed method greatly outperforms all state-of-the-art unsupervised
saliency detection methods and can be comparable to the current best
strongly-supervised methods training with thousands of pixel-level saliency map
annotations on all public benchmarks.
Argumentation theory for mathematical argument
Authors: Joseph Corneli, Ursula Martin, Dave Murray-Rust, Gabriela Rino Nesin, Alison Pease
Date: 2018-03-17 00:00:00
Tasks: Sentence

To adequately model mathematical arguments the analyst must be able to
represent the mathematical objects under discussion and the relationships
between them, as well as inferences drawn about these objects and relationships
as the discourse unfolds. We introduce a framework with these properties, which
has been used to analyse mathematical dialogues and expository texts. The
framework can recover salient elements of discourse at, and within, the
sentence level, as well as the way mathematical content connects to form larger
argumentative structures. We show how the framework might be used to support
computational reasoning, and argue that it provides a more natural way to
examine the process of proving theorems than do Lamport's structured proofs.
PAC-Reasoning in Relational Domains
Authors: Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert
Date: 2018-03-15 00:00:00

We consider the problem of predicting plausible missing facts in relational
data, given a set of imperfect logical rules. In particular, our aim is to
provide bounds on the (expected) number of incorrect inferences that are made
in this way. Since for classical inference it is in general impossible to bound
this number in a non-trivial way, we consider two inference relations that
weaken, but remain close in spirit to classical inference.
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification
Authors: Bin Wang, Yanan sun, Bing Xue, Mengjie Zhang
Date: 2018-03-17 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional neural networks (CNNs) are one of the most effective deep
learning methods to solve image classification problems, but the best
architecture of a CNN to solve a specific problem can be extremely complicated
and hard to design. This paper focuses on utilising Particle Swarm Optimisation
(PSO) to automatically search for the optimal architecture of CNNs without any
manual work involved. In order to achieve the goal, three improvements are made
based on traditional PSO. First, a novel encoding strategy inspired by computer
networks which empowers particle vectors to easily encode CNN layers is
proposed; Second, in order to allow the proposed method to learn
variable-length CNN architectures, a Disabled layer is designed to hide some
dimensions of the particle vector to achieve variable-length particles; Third,
since the learning process on large data is slow, partial datasets are randomly
picked for the evaluation to dramatically speed it up. The proposed algorithm
is examined and compared with 12 existing algorithms including the state-of-art
methods on three widely used image classification benchmark datasets. The
experimental results show that the proposed algorithm is a strong competitor to
the state-of-art algorithms in terms of classification error. This is the first
work using PSO for automatically evolving the architectures of CNNs.
Robust event-stream pattern tracking based on correlative filter
Authors: Hongmin Li, Luping Shi
Date: 2018-03-17 00:00:00
Tasks: Event-based vision, Object, Object Tracking

Object tracking based on retina-inspired and event-based dynamic vision
sensor (DVS) is challenging for the noise events, rapid change of event-stream
shape, chaos of complex background textures, and occlusion. To address these
challenges, this paper presents a robust event-stream pattern tracking method
based on correlative filter mechanism. In the proposed method, rate coding is
used to encode the event-stream object in each segment. Feature representations
from hierarchical convolutional layers of a deep convolutional neural network
(CNN) are used to represent the appearance of the rate encoded event-stream
object. The results prove that our method not only achieves good tracking
performance in many complicated scenes with noise events, complex background
textures, occlusion, and intersected trajectories, but also is robust to
variable scale, variable pose, and non-rigid deformations. In addition, this
correlative filter based event-stream tracking has the advantage of high speed.
The proposed approach will promote the potential applications of these
event-based vision sensors in self-driving, robots and many other high-speed
scenes.
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer
Authors: Hoang Tran Vu, Ching-Chun Huang
Date: 2017-12-25 00:00:00
Tasks: Domain Adaptation, Representation Learning, Style Transfer, Transfer Learning

Many methods have been proposed to solve the domain adaptation problem
recently. However, the success of them implicitly funds on the assumption that
the information of domains are fully transferrable. If the assumption is not
satisfied, the effect of negative transfer may degrade domain adaptation. In
this paper, a better learning network has been proposed by considering three
tasks - domain adaptation, disentangled representation, and style transfer
simultaneously. Firstly, the learned features are disentangled into common
parts and specific parts. The common parts represent the transferrable
features, which are suitable for domain adaptation with less negative transfer.
Conversely, the specific parts characterize the unique style of each individual
domain. Based on this, the new concept of feature exchange across domains,
which can not only enhance the transferability of common features but also be
useful for image style transfer, is introduced. These designs allow us to
introduce five types of training objectives to realize the three challenging
tasks at the same time. The experimental results show that our architecture can
be adaptive well to full transfer learning and partial transfer learning upon a
well-learned disentangled representation. Besides, the trained network also
demonstrates high potential to generate style-transferred images.
Weight Initialization of Deep Neural Networks(DNNs) using Data Statistics
Authors: Saiprasad Koturwar, Shabbir Merchant
Date: 2017-10-29 00:00:00

Deep neural networks (DNNs) form the backbone of almost every
state-of-the-art technique in the fields such as computer vision, speech
processing, and text analysis. The recent advances in computational technology
have made the use of DNNs more practical. Despite the overwhelming performances
by DNN and the advances in computational technology, it is seen that very few
researchers try to train their models from the scratch. Training of DNNs still
remains a difficult and tedious job. The main challenges that researchers face
during training of DNNs are the vanishing/exploding gradient problem and the
highly non-convex nature of the objective function which has up to million
variables. The approaches suggested in He and Xavier solve the vanishing
gradient problem by providing a sophisticated initialization technique. These
approaches have been quite effective and have achieved good results on standard
datasets, but these same approaches do not work very well on more practical
datasets. We think the reason for this is not making use of data statistics for
initializing the network weights. Optimizing such a high dimensional loss
function requires careful initialization of network weights. In this work, we
propose a data dependent initialization and analyze its performance against the
standard initialization techniques such as He and Xavier. We performed our
experiments on some practical datasets and the results show our algorithm's
superior classification accuracy.
Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model
Authors: Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy
Date: 2018-03-17 00:00:00
Tasks: Optical Flow Estimation, Scheduling

Accurate prediction of traffic signal duration for roadway junction is a
challenging problem due to the dynamic nature of traffic flows. Though
supervised learning can be used, parameters may vary across roadway junctions.
In this paper, we present a computer vision guided expert system that can learn
the departure rate of a given traffic junction modeled using traditional
queuing theory. First, we temporally group the optical flow of the moving
vehicles using Dirichlet Process Mixture Model (DPMM). These groups are
referred to as tracklets or temporal clusters. Tracklet features are then used
to learn the dynamic behavior of a traffic junction, especially during on/off
cycles of a signal. The proposed queuing theory based approach can predict the
signal open duration for the next cycle with higher accuracy when compared with
other popular features used for tracking. The hypothesis has been verified on
two publicly available video datasets. The results reveal that the DPMM based
features are better than existing tracking frameworks to estimate $\mu$. Thus,
signal duration prediction is more accurate when tested on these datasets.The
method can be used for designing intelligent operator-independent traffic
control systems for roadway junctions at cities and highways.
Cascade Adversarial Machine Learning Regularized with a Unified Embedding
Authors: Taesik Na, Jong Hwan Ko, Saibal Mukhopadhyay
Date: 2017-08-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning

Injecting adversarial examples during training, known as adversarial
training, can improve robustness against one-step attacks, but not for unknown
iterative attacks. To address this challenge, we first show iteratively
generated adversarial images easily transfer between networks trained with the
same strategy. Inspired by this observation, we propose cascade adversarial
training, which transfers the knowledge of the end results of adversarial
training. We train a network from scratch by injecting iteratively generated
adversarial images crafted from already defended networks in addition to
one-step adversarial images from the network being trained. We also propose to
utilize embedding space for both classification and low-level (pixel-level)
similarity learning to ignore unknown pixel level perturbation. During
training, we inject adversarial images without replacing their corresponding
clean images and penalize the distance between the two embeddings (clean and
adversarial). Experimental results show that cascade adversarial training
together with our proposed low-level similarity learning efficiently enhances
the robustness against iterative attacks, but at the expense of decreased
robustness against one-step attacks. We show that combining those two
techniques can also improve robustness under the worst case black box attack
scenario.
Sufficient Markov Decision Processes with Alternating Deep Neural Networks
Authors: Longshaokan Wang, Eric B. Laber, Katie Witkiewitz
Date: 2017-04-25 00:00:00

Advances in mobile computing technologies have made it possible to monitor
and apply data-driven interventions across complex systems in real time. Markov
decision processes (MDPs) are the primary model for sequential decision
problems with a large or indefinite time horizon. Choosing a representation of
the underlying decision process that is both Markov and low-dimensional is
non-trivial. We propose a method for constructing a low-dimensional
representation of the original decision process for which: 1. the MDP model
holds; 2. a decision strategy that maximizes mean utility when applied to the
low-dimensional representation also maximizes mean utility when applied to the
original process. We use a deep neural network to define a class of potential
process representations and estimate the process of lowest dimension within
this class. The method is illustrated using data from a mobile study on heavy
drinking and smoking among college students.