pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of the sample covariance matrix from compressive measurements
Authors: Farhad Pourkamali-Anaraki
Date: 2015-12-30 00:00:00

This paper focuses on the estimation of the sample covariance matrix from
low-dimensional random projections of data known as compressive measurements.
In particular, we present an unbiased estimator to extract the covariance
structure from compressive measurements obtained by a general class of random
projection matrices consisting of i.i.d. zero-mean entries and finite first
four moments. In contrast to previous works, we make no structural assumptions
about the underlying covariance matrix such as being low-rank. In fact, our
analysis is based on a non-Bayesian data setting which requires no
distributional assumptions on the set of data samples. Furthermore, inspired by
the generality of the projection matrices, we propose an approach to covariance
estimation that utilizes sparse Rademacher matrices. Therefore, our algorithm
can be used to estimate the covariance matrix in applications with limited
memory and computation power at the acquisition devices. Experimental results
demonstrate that our approach allows for accurate estimation of the sample
covariance matrix on several real-world data sets, including video data.
Partial Sum Minimization of Singular Values Representation on Grassmann Manifolds
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Date: 2016-01-21 00:00:00
Tasks: Clustering

As a significant subspace clustering method, low rank representation (LRR)
has attracted great attention in recent years. To further improve the
performance of LRR and extend its applications, there are several issues to be
resolved. The nuclear norm in LRR does not sufficiently use the prior knowledge
of the rank which is known in many practical problems. The LRR is designed for
vectorial data from linear spaces, thus not suitable for high dimensional data
with intrinsic non-linear manifold structure. This paper proposes an extended
LRR model for manifold-valued Grassmann data which incorporates prior knowledge
by minimizing partial sum of singular values instead of the nuclear norm,
namely Partial Sum minimization of Singular Values Representation (GPSSVR). The
new model not only enforces the global structure of data in low rank, but also
retains important information by minimizing only smaller singular values. To
further maintain the local structures among Grassmann points, we also integrate
the Laplacian penalty with GPSSVR. An effective algorithm is proposed to solve
the optimization problem based on the GPSSVR model. The proposed model and
algorithms are assessed on some widely used human action video datasets and a
real scenery dataset. The experimental results show that the proposed methods
obviously outperform other state-of-the-art methods.
DeepArchitect: Automatically Designing and Training Deep Architectures
Authors: Renato Negrinho, Geoff Gordon
Date: 2017-04-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Hyperparameter Optimization, Model Discovery

In deep learning, performance is strongly affected by the choice of
architecture and hyperparameters. While there has been extensive work on
automatic hyperparameter optimization for simple spaces, complex spaces such as
the space of deep architectures remain largely unexplored. As a result, the
choice of architecture is done manually by the human expert through a slow
trial and error process guided mainly by intuition. In this paper we describe a
framework for automatically designing and training deep models. We propose an
extensible and modular language that allows the human expert to compactly
represent complex search spaces over architectures and their hyperparameters.
The resulting search spaces are tree-structured and therefore easy to traverse.
Models can be automatically compiled to computational graphs once values for
all hyperparameters have been chosen. We can leverage the structure of the
search space to introduce different model search algorithms, such as random
search, Monte Carlo tree search (MCTS), and sequential model-based optimization
(SMBO). We present experiments comparing the different algorithms on CIFAR-10
and show that MCTS and SMBO outperform random search. In addition, these
experiments show that our framework can be used effectively for model
discovery, as it is possible to describe expressive search spaces and discover
competitive models without much effort from the human expert. Code for our
framework and experiments has been made publicly available.
Matrix Completion via Max-Norm Constrained Optimization
Authors: T. Tony Cai, Wen-Xin Zhou
Date: 2013-03-02 00:00:00
Tasks: Matrix Completion

Matrix completion has been well studied under the uniform sampling model and
the trace-norm regularized methods perform well both theoretically and
numerically in such a setting. However, the uniform sampling model is
unrealistic for a range of applications and the standard trace-norm relaxation
can behave very poorly when the underlying sampling scheme is non-uniform.
  In this paper we propose and analyze a max-norm constrained empirical risk
minimization method for noisy matrix completion under a general sampling model.
The optimal rate of convergence is established under the Frobenius norm loss in
the context of approximately low-rank matrix reconstruction. It is shown that
the max-norm constrained method is minimax rate-optimal and yields a unified
and robust approximate recovery guarantee, with respect to the sampling
distributions. The computational effectiveness of this method is also
discussed, based on first-order algorithms for solving convex optimizations
involving max-norm regularization.
Towards Monetary Incentives in Social Q&A Services
Authors: Steve T. K. Jan, Chun Wang, Qing Zhang, Gang Wang
Date: 2017-03-03 00:00:00
Tasks: Question Answering

Community-based question answering (CQA) services are facing key challenges
to motivate domain experts to provide timely answers. Recently, CQA services
are exploring new incentive models to engage experts and celebrities by
allowing them to set a price on their answers. In this paper, we perform a
data-driven analysis on two emerging payment-based CQA systems: Fenda (China)
and Whale (US). By analyzing a large dataset of 220K questions (worth 1 million
USD collectively), we examine how monetary incentives affect different players
in the system. We find that, while monetary incentive enables quick answers
from experts, it also drives certain users to aggressively game the system for
profits. In addition, in this supplier-driven marketplace, users need to
proactively adjust their price to make profits. Famous people are unwilling to
lower their price, which in turn hurts their income and engagement over time.
Finally, we discuss the key implications to future CQA design.
Learning Quadratic Variance Function (QVF) DAG models via OverDispersion Scoring (ODS)
Authors: Gunwoong Park, Garvesh Raskutti
Date: 2017-04-28 00:00:00
Tasks: Causal Inference

Learning DAG or Bayesian network models is an important problem in
multi-variate causal inference. However, a number of challenges arises in
learning large-scale DAG models including model identifiability and
computational complexity since the space of directed graphs is huge. In this
paper, we address these issues in a number of steps for a broad class of DAG
models where the noise or variance is signal-dependent. Firstly we introduce a
new class of identifiable DAG models, where each node has a distribution where
the variance is a quadratic function of the mean (QVF DAG models). Our QVF DAG
models include many interesting classes of distributions such as Poisson,
Binomial, Geometric, Exponential, Gamma and many other distributions in which
the noise variance depends on the mean. We prove that this class of QVF DAG
models is identifiable, and introduce a new algorithm, the OverDispersion
Scoring (ODS) algorithm, for learning large-scale QVF DAG models. Our algorithm
is based on firstly learning the moralized or undirected graphical model
representation of the DAG to reduce the DAG search-space, and then exploiting
the quadratic variance property to learn the causal ordering. We show through
theoretical results and simulations that our algorithm is statistically
consistent in the high-dimensional p>n setting provided that the degree of the
moralized graph is bounded and performs well compared to state-of-the-art
DAG-learning algorithms.
Duluth at Semeval-2017 Task 7 : Puns upon a midnight dreary, Lexical Semantics for the weak and weary
Authors: Ted Pedersen
Date: 2017-04-27 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Word Sense Disambiguation

This paper describes the Duluth systems that participated in SemEval-2017
Task 7 : Detection and Interpretation of English Puns. The Duluth systems
participated in all three subtasks, and relied on methods that included word
sense disambiguation and measures of semantic relatedness.
Machine Vision System for 3D Plant Phenotyping
Authors: Ayan Chaudhury, Christopher Ward, Ali Talasaz, Alexander G. Ivanov, Mark Brophy, Bernard Grodzinski, Norman P. A. Huner, Rajni V. Patel, John L. Barron
Date: 2017-04-28 00:00:00
Tasks: 3D Reconstruction, Plant Phenotyping

Machine vision for plant phenotyping is an emerging research area for
producing high throughput in agriculture and crop science applications. Since
2D based approaches have their inherent limitations, 3D plant analysis is
becoming state of the art for current phenotyping technologies. We present an
automated system for analyzing plant growth in indoor conditions. A gantry
robot system is used to perform scanning tasks in an automated manner
throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's
payload captures the surface point cloud data of the plant from multiple views.
The plant is monitored from the vegetative to reproductive stages in light/dark
cycles inside a controllable growth chamber. An efficient 3D reconstruction
algorithm is used, by which multiple scans are aligned together to obtain a 3D
mesh of the plant, followed by surface area and volume computations. The whole
system, including the programmable growth chamber, robot, scanner, data
transfer and analysis is fully automated in such a way that a naive user can,
in theory, start the system with a mouse click and get back the growth analysis
results at the end of the lifetime of the plant with no intermediate
intervention. As evidence of its functionality, we show and analyze
quantitative results of the rhythmic growth patterns of the dicot Arabidopsis
thaliana(L.), and the monocot barley (Hordeum vulgare L.) plants under their
diurnal light/dark cycles.
Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design
Authors: Ruijin Cang, Yaopengxiao Xu, Shaohua Chen, Yongming Liu, Yang Jiao, Max Yi Ren
Date: 2016-12-22 00:00:00
Tasks: Dimensionality Reduction

Integrated Computational Materials Engineering (ICME) aims to accelerate
optimal design of complex material systems by integrating material science and
design automation. For tractable ICME, it is required that (1) a structural
feature space be identified to allow reconstruction of new designs, and (2) the
reconstruction process be property-preserving. The majority of existing
structural presentation schemes rely on the designer's understanding of
specific material systems to identify geometric and statistical features, which
could be biased and insufficient for reconstructing physically meaningful
microstructures of complex material systems. In this paper, we develop a
feature learning mechanism based on convolutional deep belief network to
automate a two-way conversion between microstructures and their
lower-dimensional feature representations, and to achieves a 1000-fold
dimension reduction from the microstructure space. The proposed model is
applied to a wide spectrum of heterogeneous material systems with distinct
microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy,
Fontainebleau sandstone, and Spherical colloids, to produce material
reconstructions that are close to the original samples with respect to 2-point
correlation functions and mean critical fracture strength. This capability is
not achieved by existing synthesis methods that rely on the Markovian
assumption of material microstructures.
Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms
Authors: Thomas Gabor, Lenz Belzner
Date: 2017-04-27 00:00:00
Tasks: Diversity, Evolutionary Algorithms

The evolutionary edit distance between two individuals in a population, i.e.,
the amount of applications of any genetic operator it would take the
evolutionary process to generate one individual starting from the other, seems
like a promising estimate for the diversity between said individuals. We
introduce genealogical diversity, i.e., estimating two individuals' degree of
relatedness by analyzing large, unused parts of their genome, as a
computationally efficient method to approximate that measure for diversity.
Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique
Authors: Miyeon Jung, You-Bin Lee, Sang-Man Jin, Sung-Min Park
Date: 2017-04-27 00:00:00
Tasks: General Classification, Prediction

Daytime hypoglycemia should be accurately predicted to achieve normoglycemia
and to avoid disastrous situations. Hypoglycemia, an abnormally low blood
glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia.
Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In
this paper, we propose new predictor variables to predict daytime hypoglycemia
using continuous glucose monitoring (CGM) data. We apply classification and
regression tree (CART) as a prediction method. The independent variables of our
prediction model are the rate of decrease from a peak and absolute level of the
BG at the decision point. The evaluation results showed that our model was able
to detect almost 80% of hypoglycemic events 15 min in advance, which was higher
than the existing methods with similar conditions. The proposed method might
achieve a real-time prediction as well as can be embedded into BG monitoring
device.
GazeDirector: Fully Articulated Eye Gaze Redirection in Video
Authors: Erroll Wood, Tadas Baltrusaitis, Louis-Philippe Morency, Peter Robinson, Andreas Bulling
Date: 2017-04-27 00:00:00
Tasks: Gaze Estimation, gaze redirection

We present GazeDirector, a new approach for eye gaze redirection that uses
model-fitting. Our method first tracks the eyes by fitting a multi-part eye
region model to video frames using analysis-by-synthesis, thereby recovering
eye region shape, texture, pose, and gaze simultaneously. It then redirects
gaze by 1) warping the eyelids from the original image using a model-derived
flow field, and 2) rendering and compositing synthesized 3D eyeballs onto the
output image in a photorealistic manner. GazeDirector allows us to change where
people are looking without person-specific training data, and with full
articulation, i.e. we can precisely specify new gaze directions in 3D.
Quantitatively, we evaluate both model-fitting and gaze synthesis, with
experiments for gaze estimation and redirection on the Columbia gaze dataset.
Qualitatively, we compare GazeDirector against recent work on gaze redirection,
showing better results especially for large redirection angles. Finally, we
demonstrate gaze redirection on YouTube videos by introducing new 3D gaze
targets and by manipulating visual behavior.
Learning a Neural Semantic Parser from User Feedback
Authors: Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, Luke Zettlemoyer
Date: 2017-04-27 00:00:00
Proceeding: ACL 2017 7
Tasks: SQL Parsing

We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypassing any intermediate meaning representations. These models
are immediately deployed online to solicit feedback from real users to flag
incorrect queries. Finally, the popularity of SQL facilitates gathering
annotations for incorrect predictions using the crowd, which is directly used
to improve our models. This complete feedback loop, without intermediate
representations or database specific engineering, opens up new ways of building
high quality semantic parsers. Experiments suggest that this approach can be
deployed quickly for any new target domain, as we show by learning a semantic
parser for an online academic database from scratch.
Obstacle Avoidance through Deep Networks based Intermediate Perception
Authors: Shichao Yang, Sandeep Konam, Chen Ma, Stephanie Rosenthal, Manuela Veloso, Sebastian Scherer
Date: 2017-04-27 00:00:00

Obstacle avoidance from monocular images is a challenging problem for robots.
Though multi-view structure-from-motion could build 3D maps, it is not robust
in textureless environments. Some learning based methods exploit human
demonstration to predict a steering command directly from a single image.
However, this method is usually biased towards certain tasks or demonstration
scenarios and also biased by human understanding. In this paper, we propose a
new method to predict a trajectory from images. We train our system on more
diverse NYUv2 dataset. The ground truth trajectory is computed from the
designed cost functions automatically. The Convolutional Neural Network
perception is divided into two stages: first, predict depth map and surface
normal from RGB images, which are two important geometric properties related to
3D obstacle representation. Second, predict the trajectory from the depth and
normal. Results show that our intermediate perception increases the accuracy by
20% than the direct prediction. Our model generalizes well to other public
indoor datasets and is also demonstrated for robot flights in simulation and
experiments.
A Network Perspective on Stratification of Multi-Label Data
Authors: Piotr Szymański, Tomasz Kajdanowicz
Date: 2017-04-27 00:00:00
Tasks: Classification, Community Detection, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In the recent years, we have witnessed the development of multi-label
classification methods which utilize the structure of the label space in a
divide and conquer approach to improve classification performance and allow
large data sets to be classified efficiently. Yet most of the available data
sets have been provided in train/test splits that did not account for
maintaining a distribution of higher-order relationships between labels among
splits or folds. We present a new approach to stratifying multi-label data for
classification purposes based on the iterative stratification approach proposed
by Sechidis et. al. in an ECML PKDD 2011 paper. Our method extends the
iterative approach to take into account second-order relationships between
labels. Obtained results are evaluated using statistical properties of obtained
strata as presented by Sechidis. We also propose new statistical measures
relevant to second-order quality: label pairs distribution, the percentage of
label pairs without positive evidence in folds and label pair - fold pairs that
have no positive evidence for the label pair. We verify the impact of new
methods on classification performance of Binary Relevance, Label Powerset and a
fast greedy community detection based label space partitioning classifier.
Random Forests serve as base classifiers. We check the variation of the number
of communities obtained per fold, and the stability of their modularity score.
Second-Order Iterative Stratification is compared to standard k-fold, label
set, and iterative stratification. The proposed approach lowers the variance of
classification quality, improves label pair oriented measures and example
distribution while maintaining a competitive quality in label-oriented
measures. We also witness an increase in stability of network characteristics.
An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design
Authors: Fabio D'Andreagiovanni, Fabian Mett, Jonad Pulaj
Date: 2015-12-31 00:00:00

We investigate the 3-architecture Connected Facility Location Problem arising
in the design of urban telecommunication access networks. We propose an
original optimization model for the problem that includes additional variables
and constraints to take into account wireless signal coverage. Since the
problem can prove challenging even for modern state-of-the art optimization
solvers, we propose to solve it by an original primal heuristic which combines
a probabilistic fixing procedure, guided by peculiar Linear Programming
relaxations, with an exact MIP heuristic, based on a very large neighborhood
search. Computational experiments on a set of realistic instances show that our
heuristic can find solutions associated with much lower optimality gaps than a
state-of-the-art solver.
A Fast Hybrid Primal Heuristic for Multiband Robust Capacitated Network Design with Multiple Time Periods
Authors: Fabio D'Andreagiovanni, Jonatan Krolikowski, Jonad Pulaj
Date: 2014-10-21 00:00:00

We investigate the Robust Multiperiod Network Design Problem, a
generalization of the Capacitated Network Design Problem (CNDP) that, besides
establishing flow routing and network capacity installation as in a canonical
CNDP, also considers a planning horizon made up of multiple time periods and
protection against fluctuations in traffic volumes. As a remedy against traffic
volume uncertainty, we propose a Robust Optimization model based on Multiband
Robustness (B\"using and D'Andreagiovanni, 2012), a refinement of classical
Gamma-Robustness by Bertsimas and Sim that uses a system of multiple deviation
bands. Since the resulting optimization problem may prove very challenging even
for instances of moderate size solved by a state-of-the-art optimization
solver, we propose a hybrid primal heuristic that combines a randomized fixing
strategy inspired by ant colony optimization, which exploits information coming
from linear relaxations of the problem, and an exact large neighbourhood
search. Computational experiments on a set of realistic instances from the
SNDlib show that our original heuristic can run fast and produce solutions of
extremely high quality associated with low optimality gaps.
Improving Facial Attribute Prediction using Semantic Segmentation
Authors: Mahdi M. Kalayeh, Boqing Gong, Mubarak Shah
Date: 2017-04-27 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, Face Parsing, Facial Attribute Classification, Prediction, Segmentation, Semantic Segmentation, Zero-Shot Learning

Attributes are semantically meaningful characteristics whose applicability
widely crosses category boundaries. They are particularly important in
describing and recognizing concepts where no explicit training example is
given, \textit{e.g., zero-shot learning}. Additionally, since attributes are
human describable, they can be used for efficient human-computer interaction.
In this paper, we propose to employ semantic segmentation to improve facial
attribute prediction. The core idea lies in the fact that many facial
attributes describe local properties. In other words, the probability of an
attribute to appear in a face image is far from being uniform in the spatial
domain. We build our facial attribute prediction model jointly with a deep
semantic segmentation network. This harnesses the localization cues learned by
the semantic segmentation to guide the attention of the attribute prediction to
the regions where different attributes naturally show up. As a result of this
approach, in addition to recognition, we are able to localize the attributes,
despite merely having access to image level labels (weak supervision) during
training. We evaluate our proposed method on CelebA and LFWA datasets and
achieve superior results to the prior arts. Furthermore, we show that in the
reverse problem, semantic face parsing improves when facial attributes are
available. That reaffirms the need to jointly model these two interconnected
tasks.