pwc-archive/papers-with-abstracts huggingface dataset:
Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem
Authors: Patrick Kenekayoro, Godswill Zipamone
Date: 2016-02-16 00:00:00

Timetabling is a problem faced in all higher education institutions. The
International Timetabling Competition (ITC) has published a dataset that can be
used to test the quality of methods used to solve this problem. A number of
meta-heuristic approaches have obtained good results when tested on the ITC
dataset, however few have used the ant colony optimization technique,
particularly on the ITC 2007 curriculum based university course timetabling
problem. This study describes an ant system that solves the curriculum based
university course timetabling problem and the quality of the algorithm is
tested on the ITC 2007 dataset. The ant system was able to find feasible
solutions in all instances of the dataset and close to optimal solutions in
some instances. The ant system performs better than some published approaches,
however results obtained are not as good as those obtained by the best
published approaches. This study may be used as a benchmark for ant based
algorithms that solve the curriculum based university course timetabling
problem.
Loop-corrected belief propagation for lattice spin models
Authors: Hai-Jun Zhou, Wei-Mou Zheng
Date: 2015-05-13 00:00:00

Belief propagation (BP) is a message-passing method for solving probabilistic
graphical models. It is very successful in treating disordered models (such as
spin glasses) on random graphs. On the other hand, finite-dimensional lattice
models have an abundant number of short loops, and the BP method is still far
from being satisfactory in treating the complicated loop-induced correlations
in these systems. Here we propose a loop-corrected BP method to take into
account the effect of short loops in lattice spin models. We demonstrate,
through an application to the square-lattice Ising model, that loop-corrected
BP improves over the naive BP method significantly. We also implement
loop-corrected BP at the coarse-grained region graph level to further boost its
performance.
Generalized minimum dominating set and application in automatic text summarization
Authors: Yi-Zhi Xu, Hai-Jun Zhou
Date: 2016-02-16 00:00:00
Tasks: Text Summarization

For a graph formed by vertices and weighted edges, a generalized minimum
dominating set (MDS) is a vertex set of smallest cardinality such that the
summed weight of edges from each outside vertex to vertices in this set is
equal to or larger than certain threshold value. This generalized MDS problem
reduces to the conventional MDS problem in the limiting case of all the edge
weights being equal to the threshold value. We treat the generalized MDS
problem in the present paper by a replica-symmetric spin glass theory and
derive a set of belief-propagation equations. As a practical application we
consider the problem of extracting a set of sentences that best summarize a
given input text document. We carry out a preliminary test of the statistical
physics-inspired method to this automatic text summarization problem.
Efficient inference in occlusion-aware generative models of images
Authors: Jonathan Huang, Kevin Murphy
Date: 2015-11-19 00:00:00
Tasks: Object

We present a generative model of images based on layering, in which image
layers are individually generated, then composited from front to back. We are
thus able to factor the appearance of an image into the appearance of
individual objects within the image --- and additionally for each individual
object, we can factor content from pose. Unlike prior work on layered models,
we learn a shape prior for each object/layer, allowing the model to tease out
which object is in front by looking for a consistent shape, without needing
access to motion cues or any labeled data. We show that ordinary stochastic
gradient variational bayes (SGVB), which optimizes our fully differentiable
lower-bound on the log-likelihood, is sufficient to learn an interpretable
representation of images. Finally we present experiments demonstrating the
effectiveness of the model for inferring foreground and background objects in
images.
Personalized Federated Search at LinkedIn
Authors: Dhruv Arya, Viet Ha-Thuc, Shakti Sinha
Date: 2016-02-16 00:00:00

LinkedIn has grown to become a platform hosting diverse sources of
information ranging from member profiles, jobs, professional groups, slideshows
etc. Given the existence of multiple sources, when a member issues a query like
"software engineer", the member could look for software engineer profiles, jobs
or professional groups. To tackle this problem, we exploit a data-driven
approach that extracts searcher intents from their profile data and recent
activities at a large scale. The intents such as job seeking, hiring, content
consuming are used to construct features to personalize federated search
experience. We tested the approach on the LinkedIn homepage and A/B tests show
significant improvements in member engagement. As of writing this paper, the
approach powers all of federated search on LinkedIn homepage.
Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
Authors: Tianhao Zhang, Gregory Kahn, Sergey Levine, Pieter Abbeel
Date: 2015-09-22 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), State Estimation

Model predictive control (MPC) is an effective method for controlling robotic
systems, particularly autonomous aerial vehicles such as quadcopters. However,
application of MPC can be computationally demanding, and typically requires
estimating the state of the system, which can be challenging in complex,
unstructured environments. Reinforcement learning can in principle forego the
need for explicit state estimation and acquire a policy that directly maps
sensor readings to actions, but is difficult to apply to unstable systems that
are liable to fail catastrophically during training before an effective policy
has been found. We propose to combine MPC with reinforcement learning in the
framework of guided policy search, where MPC is used to generate data at
training time, under full state observations provided by an instrumented
training environment. This data is used to train a deep neural network policy,
which is allowed to access only the raw observations from the vehicle's onboard
sensors. After training, the neural network policy can successfully control the
robot without knowledge of the full state, and at a fraction of the
computational cost of MPC. We evaluate our method by learning obstacle
avoidance policies for a simulated quadrotor, using simulated onboard sensors
and no explicit state estimation at test time.
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
Authors: Weiyao Lin, Yang Mi, Weiyue Wang, Jianxin Wu, Jingdong Wang, Tao Mei
Date: 2016-02-16 00:00:00
Tasks: Clustering, Optical Flow Estimation, Scene Understanding

This paper addresses the problem of detecting coherent motions in crowd
scenes and presents its two applications in crowd scene understanding: semantic
region detection and recurrent activity mining. It processes input motion
fields (e.g., optical flow fields) and produces a coherent motion filed, named
as thermal energy field. The thermal energy field is able to capture both
motion correlation among particles and the motion trends of individual
particles which are helpful to discover coherency among them. We further
introduce a two-step clustering process to construct stable semantic regions
from the extracted time-varying coherent motions. These semantic regions can be
used to recognize pre-defined activities in crowd scenes. Finally, we introduce
a cluster-and-merge process which automatically discovers recurrent activities
in crowd scenes by clustering and merging the extracted coherent motions.
Experiments on various videos demonstrate the effectiveness of our approach.
Bayesian generalized fused lasso modeling via NEG distribution
Authors: Kaito Shimamura, Masao Ueki, Shuichi. Kawano, Sadanori Konishi
Date: 2016-02-16 00:00:00
Tasks: regression

The fused lasso penalizes a loss function by the $L_1$ norm for both the
regression coefficients and their successive differences to encourage sparsity
of both. In this paper, we propose a Bayesian generalized fused lasso modeling
based on a normal-exponential-gamma (NEG) prior distribution. The NEG prior is
assumed into the difference of successive regression coefficients. The proposed
method enables us to construct a more versatile sparse model than the ordinary
fused lasso by using a flexible regularization term. We also propose a sparse
fused algorithm to produce exact sparse solutions. Simulation studies and real
data analyses show that the proposed method has superior performance to the
ordinary fused lasso.
Segmentation Rectification for Video Cutout via One-Class Structured Learning
Authors: Junyan Wang, Sai-Kit Yeung, Jue Wang, Kun Zhou
Date: 2016-02-16 00:00:00
Tasks: Segmentation

Recent works on interactive video object cutout mainly focus on designing
dynamic foreground-background (FB) classifiers for segmentation propagation.
However, the research on optimally removing errors from the FB classification
is sparse, and the errors often accumulate rapidly, causing significant errors
in the propagated frames. In this work, we take the initial steps to addressing
this problem, and we call this new task \emph{segmentation rectification}. Our
key observation is that the possibly asymmetrically distributed false positive
and false negative errors were handled equally in the conventional methods. We,
alternatively, propose to optimally remove these two types of errors. To this
effect, we propose a novel bilayer Markov Random Field (MRF) model for this new
task. We also adopt the well-established structured learning framework to learn
the optimal model from data. Additionally, we propose a novel one-class
structured SVM (OSSVM) which greatly speeds up the structured learning process.
Our method naturally extends to RGB-D videos as well. Comprehensive experiments
on both RGB and RGB-D data demonstrate that our simple and effective method
significantly outperforms the segmentation propagation methods adopted in the
state-of-the-art video cutout systems, and the results also suggest the
potential usefulness of our method in image cutout system.
NED: An Inter-Graph Node Metric Based On Edit Distance
Authors: Haohan Zhu, Xianrui Meng, George Kollios
Date: 2016-02-07 00:00:00
Tasks: Transfer Learning

Node similarity is a fundamental problem in graph analytics. However, node
similarity between nodes in different graphs (inter-graph nodes) has not
received a lot of attention yet. The inter-graph node similarity is important
in learning a new graph based on the knowledge of an existing graph (transfer
learning on graphs) and has applications in biological, communication, and
social networks. In this paper, we propose a novel distance function for
measuring inter-graph node similarity with edit distance, called NED. In NED,
two nodes are compared according to their local neighborhood structures which
are represented as unordered k-adjacent trees, without relying on labels or
other assumptions. Since the computation problem of tree edit distance on
unordered trees is NP-Complete, we propose a modified tree edit distance,
called TED*, for comparing neighborhood trees. TED* is a metric distance, as
the original tree edit distance, but more importantly, TED* is polynomially
computable. As a metric distance, NED admits efficient indexing, provides
interpretable results, and shows to perform better than existing approaches on
a number of data analysis tasks, including graph de-anonymization. Finally, the
efficiency and effectiveness of NED are empirically demonstrated using
real-world graphs.
Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling
Authors: Yogesh Girdhar, Walter Cho, Matthew Campbell, Jesus Pineda, Elizabeth Clarke, Hanumant Singh
Date: 2015-09-26 00:00:00
Tasks: Anomaly Detection

This paper explores the use of a Bayesian non-parametric topic modeling
technique for the purpose of anomaly detection in video data. We present
results from two experiments. The first experiment shows that the proposed
technique is automatically able characterize the underlying terrain, and detect
anomalous flora in image data collected by an underwater robot. The second
experiment shows that the same technique can be used on images from a static
camera in a dynamic unstructured environment. In the second dataset, consisting
of video data from a static seafloor camera capturing images of a busy coral
reef, the proposed technique was able to detect all three instances of an
underwater vehicle passing in front of the camera, amongst many other
observations of fishes, debris, lighting changes due to surface waves, and
benthic flora.
Fast, Robust, Continuous Monocular Egomotion Computation
Authors: Andrew Jaegle, Stephen Phillips, Kostas Daniilidis
Date: 2016-02-16 00:00:00
Tasks: counterfactual, Motion Estimation, Optical Flow Estimation, Translation

We propose robust methods for estimating camera egomotion in noisy,
real-world monocular image sequences in the general case of unknown observer
rotation and translation with two views and a small baseline. This is a
difficult problem because of the nonconvex cost function of the perspective
camera motion equation and because of non-Gaussian noise arising from noisy
optical flow estimates and scene non-rigidity. To address this problem, we
introduce the expected residual likelihood method (ERL), which estimates
confidence weights for noisy optical flow data using likelihood distributions
of the residuals of the flow field under a range of counterfactual model
parameters. We show that ERL is effective at identifying outliers and
recovering appropriate confidence weights in many settings. We compare ERL to a
novel formulation of the perspective camera motion equation using a lifted
kernel, a recently proposed optimization framework for joint parameter and
confidence weight estimation with good empirical properties. We incorporate
these strategies into a motion estimation pipeline that avoids falling into
local minima. We find that ERL outperforms the lifted kernel method and
baseline monocular egomotion estimation strategies on the challenging KITTI
dataset, while adding almost no runtime cost over baseline egomotion methods.
Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation
Authors: Yushi Yao, Zheng Huang
Date: 2016-02-16 00:00:00
Tasks: Articles, Chinese Word Segmentation, Segmentation

Recurrent neural network(RNN) has been broadly applied to natural language
processing(NLP) problems. This kind of neural network is designed for modeling
sequential data and has been testified to be quite efficient in sequential
tagging tasks. In this paper, we propose to use bi-directional RNN with long
short-term memory(LSTM) units for Chinese word segmentation, which is a crucial
preprocess task for modeling Chinese sentences and articles. Classical methods
focus on designing and combining hand-craft features from context, whereas
bi-directional LSTM network(BLSTM) does not need any prior knowledge or
pre-designing, and it is expert in keeping the contextual information in both
directions. Experiment result shows that our approach gets state-of-the-art
performance in word segmentation on both traditional Chinese datasets and
simplified Chinese datasets.
Deep Feature-based Face Detection on Mobile Devices
Authors: Sayantan Sarkar, Vishal M. Patel, Rama Chellappa
Date: 2016-02-16 00:00:00
Tasks: Face Detection

We propose a deep feature-based face detector for mobile devices to detect
user's face acquired by the front facing camera. The proposed method is able to
detect faces in images containing extreme pose and illumination variations as
well as partial faces. The main challenge in developing deep feature-based
algorithms for mobile devices is the constrained nature of the mobile platform
and the non-availability of CUDA enabled GPUs on such devices. Our
implementation takes into account the special nature of the images captured by
the front-facing camera of mobile devices and exploits the GPUs present in
mobile devices without CUDA-based frameorks, to meet these challenges.
Graphlet Decomposition: Framework, Algorithms, and Applications
Authors: Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield, Theodore L. Willke
Date: 2015-06-13 00:00:00

From social science to biology, numerous applications often rely on graphlets
for intuitive and meaningful characterization of networks at both the global
macro-level as well as the local micro-level. While graphlets have witnessed a
tremendous success and impact in a variety of domains, there has yet to be a
fast and efficient approach for computing the frequencies of these subgraph
patterns. However, existing methods are not scalable to large networks with
millions of nodes and edges, which impedes the application of graphlets to new
problems that require large-scale network analysis. To address these problems,
we propose a fast, efficient, and parallel algorithm for counting graphlets of
size k={3,4}-nodes that take only a fraction of the time to compute when
compared with the current methods used. The proposed graphlet counting
algorithms leverages a number of proven combinatorial arguments for different
graphlets. For each edge, we count a few graphlets, and with these counts along
with the combinatorial arguments, we obtain the exact counts of others in
constant time. On a large collection of 300+ networks from a variety of
domains, our graphlet counting strategies are on average 460x faster than
current methods. This brings new opportunities to investigate the use of
graphlets on much larger networks and newer applications as we show in the
experiments. To the best of our knowledge, this paper provides the largest
graphlet computations to date as well as the largest systematic investigation
on over 300+ networks from a variety of domains.
Black-box optimization with a politician
Authors: Sébastien Bubeck, Yin-Tat Lee
Date: 2016-02-15 00:00:00

We propose a new framework for black-box convex optimization which is
well-suited for situations where gradient computations are expensive. We derive
a new method for this framework which leverages several concepts from convex
optimization, from standard first-order methods (e.g. gradient descent or
quasi-Newton methods) to analytical centers (i.e. minimizers of self-concordant
barriers). We demonstrate empirically that our new technique compares favorably
with state of the art algorithms (such as BFGS).
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Authors: Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Date: 2016-02-15 00:00:00
Tasks: regression

Performing exact posterior inference in complex generative models is often
difficult or impossible due to an expensive to evaluate or intractable
likelihood function. Approximate Bayesian computation (ABC) is an inference
framework that constructs an approximation to the true likelihood based on the
similarity between the observed and simulated data as measured by a predefined
set of summary statistics. Although the choice of appropriate problem-specific
summary statistics crucially influences the quality of the likelihood
approximation and hence also the quality of the posterior sample in ABC, there
are only few principled general-purpose approaches to the selection or
construction of such summary statistics. In this paper, we develop a novel
framework for this task using kernel-based distribution regression. We model
the functional relationship between data distributions and the optimal choice
(with respect to a loss function) of summary statistics using kernel-based
distribution regression. We show that our approach can be implemented in a
computationally and statistically efficient way using the random Fourier
features framework for large-scale kernel learning. In addition to that, our
framework shows superior performance when compared to related methods on toy
and real-world problems.
Quantum Perceptron Models
Authors: Nathan Wiebe, Ashish Kapoor, Krysta M. Svore
Date: 2016-02-15 00:00:00
Proceeding: NeurIPS 2016 12

We demonstrate how quantum computation can provide non-trivial improvements
in the computational and statistical complexity of the perceptron model. We
develop two quantum algorithms for perceptron learning. The first algorithm
exploits quantum information processing to determine a separating hyperplane
using a number of steps sublinear in the number of data points $N$, namely
$O(\sqrt{N})$. The second algorithm illustrates how the classical mistake bound
of $O(\frac{1}{\gamma^2})$ can be further improved to
$O(\frac{1}{\sqrt{\gamma}})$ through quantum means, where $\gamma$ denotes the
margin. Such improvements are achieved through the application of quantum
amplitude amplification to the version space interpretation of the perceptron
model.
Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes
Authors: Richard J. Preen, Larry Bull
Date: 2012-04-18 00:00:00
Tasks: 3D Shape Representation, Evolutionary Algorithms

We have recently presented an initial study of evolutionary algorithms used
to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are
evaluated under approximated wind tunnel conditions after being physically
instantiated by a 3D printer. That is, unlike other approaches such as
computational fluid dynamics simulations, no mathematical formulations are used
and no model assumptions are made. However, the representation used
significantly restricted the range of morphologies explored. In this paper, we
present initial explorations into the use of a simple generative encoding,
known as Gielis superformula, that produces a highly flexible 3D shape
representation to design VAWT. First, the target-based evolution of 3D
artefacts is investigated and subsequently initial design experiments are
performed wherein each VAWT candidate is physically instantiated and evaluated
under approximated wind tunnel conditions. It is shown possible to produce very
closely matching designs of a number of 3D objects through the evolution of
supershapes produced by Gielis superformula. Moreover, it is shown possible to
use artificial physical evolution to identify novel and increasingly efficient
supershape VAWT designs.
Discriminative Regularization for Generative Models
Authors: Alex Lamb, Vincent Dumoulin, Aaron Courville
Date: 2016-02-09 00:00:00

We explore the question of whether the representations learned by classifiers
can be used to enhance the quality of generative models. Our conjecture is that
labels correspond to characteristics of natural data which are most salient to
humans: identity in faces, objects in images, and utterances in speech. We
propose to take advantage of this by using the representations from
discriminative classifiers to augment the objective function corresponding to a
generative model. In particular we enhance the objective function of the
variational autoencoder, a popular generative model, with a discriminative
regularization term. We show that enhancing the objective function in this way
leads to samples that are clearer and have higher visual quality than the
samples from the standard variational autoencoders.