pwc-archive/papers-with-abstracts huggingface dataset:
Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016
Authors: Nam Khanh Tran
Date: 2016-12-20 00:00:00
Tasks: Binary Classification, Classification, Ensemble Learning, General Classification, Learning-To-Rank

In this paper, we propose two methods for tackling the problem of
cross-device matching for online advertising at CIKM Cup 2016. The first method
considers the matching problem as a binary classification task and solve it by
utilizing ensemble learning techniques. The second method defines the matching
problem as a ranking task and effectively solve it with using learning-to-rank
algorithms. The results show that the proposed methods obtain promising
results, in which the ranking-based method outperforms the classification-based
method for the task.
Stateology: State-Level Interactive Charting of Language, Feelings, and Values
Authors: Konstantinos Pappas, Steven Wilson, Rada Mihalcea
Date: 2016-12-20 00:00:00

People's personality and motivations are manifest in their everyday language
usage. With the emergence of social media, ample examples of such usage are
procurable. In this paper, we aim to analyze the vocabulary used by close to
200,000 Blogger users in the U.S. with the purpose of geographically portraying
various demographic, linguistic, and psychological dimensions at the state
level. We give a description of a web-based tool for viewing maps that depict
various characteristics of the social media users as derived from this large
blog dataset of over two billion words.
Inferring the location of authors from words in their texts
Authors: Max Berggren, Jussi Karlgren, Robert Östling, Mikael Parkvall
Date: 2016-12-20 00:00:00
Proceeding: WS 2015 5

For the purposes of computational dialectology or other geographically bound
text analysis tasks, texts must be annotated with their or their authors'
location. Many texts are locatable through explicit labels but most have no
explicit annotation of place. This paper describes a series of experiments to
determine how positionally annotated microblog posts can be used to learn
location-indicating words which then can be used to locate blog texts and their
authors. A Gaussian distribution is used to model the locational qualities of
words. We introduce the notion of placeness to describe how locational words
are.
  We find that modelling word distributions to account for several locations
and thus several Gaussian distributions per word, defining a filter which picks
out words with high placeness based on their local distributional context, and
aggregating locational information in a centroid for each text gives the most
useful results. The results are applied to data in the Swedish language.
Enhancing Observability in Distribution Grids using Smart Meter Data
Authors: Siddharth Bhela, Vassilis Kekatos, Sriharsha Veeramachaneni
Date: 2016-12-20 00:00:00
Tasks: State Estimation

Due to limited metering infrastructure, distribution grids are currently
challenged by observability issues. On the other hand, smart meter data,
including local voltage magnitudes and power injections, are communicated to
the utility operator from grid buses with renewable generation and
demand-response programs. This work employs grid data from metered buses
towards inferring the underlying grid state. To this end, a coupled formulation
of the power flow problem (CPF) is put forth. Exploiting the high variability
of injections at metered buses, the controllability of solar inverters, and the
relative time-invariance of conventional loads, the idea is to solve the
non-linear power flow equations jointly over consecutive time instants. An
intuitive and easily verifiable rule pertaining to the locations of metered and
non-metered buses on the physical grid is shown to be a necessary and
sufficient criterion for local observability in radial networks. To account for
noisy smart meter readings, a coupled power system state estimation (CPSSE)
problem is further developed. Both CPF and CPSSE tasks are tackled via
augmented semi-definite program relaxations. The observability criterion along
with the CPF and CPSSE solvers are numerically corroborated using synthetic and
actual solar generation and load data on the IEEE 34-bus benchmark feeder.
Partially blind domain adaptation for age prediction from DNA methylation data
Authors: Lisa Handl, Adrin Jalali, Michael Scherer, Nico Pfeifer
Date: 2016-12-20 00:00:00
Tasks: Domain Adaptation, feature selection

Over the last years, huge resources of biological and medical data have
become available for research. This data offers great chances for machine
learning applications in health care, e.g. for precision medicine, but is also
challenging to analyze. Typical challenges include a large number of possibly
correlated features and heterogeneity in the data. One flourishing field of
biological research in which this is relevant is epigenetics. Here, especially
large amounts of DNA methylation data have emerged. This epigenetic mark has
been used to predict a donor's 'epigenetic age' and increased epigenetic aging
has been linked to lifestyle and disease history. In this paper we propose an
adaptive model which performs feature selection for each test sample
individually based on the distribution of the input data. The method can be
seen as partially blind domain adaptation. We apply the model to the problem of
age prediction based on DNA methylation data from a variety of tissues, and
compare it to a standard model, which does not take heterogeneity into account.
The standard approach has particularly bad performance on one tissue type on
which we show substantial improvement with our new adaptive approach even
though no samples of that tissue were part of the training data.
Supervised Learning for Optimal Power Flow as a Real-Time Proxy
Authors: Raphael Canyasse, Gal Dalal, Shie Mannor
Date: 2016-12-20 00:00:00

In this work we design and compare different supervised learning algorithms
to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The
motivation for quick calculation of OPF cost outcomes stems from the growing
need of algorithmic-based long-term and medium-term planning methodologies in
power networks. Integrated in a multiple time-horizon coordination framework,
we refer to this approximation module as a proxy for predicting short-term
decision outcomes without the need of actual simulation and optimization of
them. Our method enables fast approximate calculation of OPF cost with less
than 1% error on average, achieved in run-times that are several orders of
magnitude lower than of exact computation. Several test-cases such as
IEEE-RTS96 are used to demonstrate the efficiency of our approach.
Deep Motion Features for Visual Tracking
Authors: Susanna Gladh, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
Date: 2016-12-20 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization, Video Classification, Visual Tracking

Robust visual tracking is a challenging computer vision problem, with many
real-world applications. Most existing approaches employ hand-crafted
appearance features, such as HOG or Color Names. Recently, deep RGB features
extracted from convolutional neural networks have been successfully applied for
tracking. Despite their success, these features only capture appearance
information. On the other hand, motion cues provide discriminative and
complementary information that can improve tracking performance. Contrary to
visual tracking, deep motion features have been successfully applied for action
recognition and video classification tasks. Typically, the motion features are
learned by training a CNN on optical flow images extracted from large amounts
of labeled videos.
  This paper presents an investigation of the impact of deep motion features in
a tracking-by-detection framework. We further show that hand-crafted, deep RGB,
and deep motion features contain complementary information. To the best of our
knowledge, we are the first to propose fusing appearance information with deep
motion features for visual tracking. Comprehensive experiments clearly suggest
that our fusion approach with deep motion features outperforms standard methods
relying on appearance information alone.
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory
Authors: Muhammad Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang
Date: 2016-12-20 00:00:00
Tasks: Clustering, Clustering Ensemble, Diversity

The Wisdom of Crowds (WOC), as a theory in the social science, gets a new
paradigm in computer science. The WOC theory explains that the aggregate
decision made by a group is often better than those of its individual members
if specific conditions are satisfied. This paper presents a novel framework for
unsupervised and semi-supervised cluster ensemble by exploiting the WOC theory.
We employ four conditions in the WOC theory, i.e., diversity, independency,
decentralization and aggregation, to guide both the constructing of individual
clustering results and the final combination for clustering ensemble. Firstly,
independency criterion, as a novel mapping system on the raw data set, removes
the correlation between features on our proposed method. Then, decentralization
as a novel mechanism generates high-quality individual clustering results.
Next, uniformity as a new diversity metric evaluates the generated clustering
results. Further, weighted evidence accumulation clustering method is proposed
for the final aggregation without using thresholding procedure. Experimental
study on varied data sets demonstrates that the proposed approach achieves
superior performance to state-of-the-art methods.
A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data
Authors: Naoki Nishimura, Noriyoshi Sukegawa, Yuichi Takano, Jiro Iwanaga
Date: 2016-12-20 00:00:00
Tasks: parameter estimation

This paper analyzes customer product-choice behavior based on the recency and
frequency of each customer's page views on e-commerce sites. Recently, we
devised an optimization model for estimating product-choice probabilities that
satisfy monotonicity, convexity, and concavity constraints with respect to
recency and frequency. This shape-restricted model delivered high predictive
performance even when there were few training samples. However, typical
e-commerce sites deal in many different varieties of products, so the
predictive performance of the model can be further improved by integration of
such product heterogeneity. For this purpose, we develop a novel latent-class
shape-restricted model for estimating product-choice probabilities for each
latent class of products. We also give a tailored expectation-maximization
algorithm for parameter estimation. Computational results demonstrate that
higher predictive performance is achieved with our latent-class model than with
the previous shape-restricted model and common latent-class logistic
regression.
Grammar rules for the isiZulu complex verb
Authors: C. Maria Keet, Langa Khumalo
Date: 2016-12-20 00:00:00
Tasks: Machine Translation, Negation, Translation

The isiZulu verb is known for its morphological complexity, which is a
subject for on-going linguistics research, as well as for prospects of
computational use, such as controlled natural language interfaces, machine
translation, and spellcheckers. To this end, we seek to answer the question as
to what the precise grammar rules for the isiZulu complex verb are (and, by
extension, the Bantu verb morphology). To this end, we iteratively specify the
grammar as a Context Free Grammar, and evaluate it computationally. The grammar
presented in this paper covers the subject and object concords, negation,
present tense, aspect, mood, and the causative, applicative, stative, and the
reciprocal verbal extensions, politeness, the wh-question modifiers, and aspect
doubling, ensuring their correct order as they appear in verbs. The grammar
conforms to specification.
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
Authors: Sebastian Ramos, Stefan Gehrig, Peter Pinggera, Uwe Franke, Carsten Rother
Date: 2016-12-20 00:00:00
Tasks: Self-Driving Cars

The detection of small road hazards, such as lost cargo, is a vital
capability for self-driving cars. We tackle this challenging and rarely
addressed problem with a vision system that leverages appearance, contextual as
well as geometric cues. To utilize the appearance and contextual cues, we
propose a new deep learning-based obstacle detection framework. Here a variant
of a fully convolutional network is used to predict a pixel-wise semantic
labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii)
background. The geometric cues are exploited using a state-of-the-art detection
approach that predicts obstacles from stereo input images via model-based
statistical hypothesis tests. We present a principled Bayesian framework to
fuse the semantic and stereo-based detection results. The mid-level Stixel
representation is used to describe obstacles in a flexible, compact and robust
manner. We evaluate our new obstacle detection system on the Lost and Found
dataset, which includes very challenging scenes with obstacles of only 5 cm
height. Overall, we report a major improvement over the state-of-the-art, with
relative performance gains of up to 50%. In particular, we achieve a detection
rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on
our self-driving platform.
RIDS: Robust Identification of Sparse Gene Regulatory Networks from Perturbation Experiments
Authors: Hoi-To Wai, Anna Scaglione, Uzi Harush, Baruch Barzel, Amir Leshem
Date: 2016-12-20 00:00:00

Reconstructing the causal network in a complex dynamical system plays a
crucial role in many applications, from sub-cellular biology to economic
systems. Here we focus on inferring gene regulation networks (GRNs) from
perturbation or gene deletion experiments. Despite their scientific merit, such
perturbation experiments are not often used for such inference due to their
costly experimental procedure, requiring significant resources to complete the
measurement of every single experiment. To overcome this challenge, we develop
the Robust IDentification of Sparse networks (RIDS) method that reconstructs
the GRN from a small number of perturbation experiments. Our method uses the
gene expression data observed in each experiment and translates that into a
steady state condition of the system's nonlinear interaction dynamics. Applying
a sparse optimization criterion, we are able to extract the parameters of the
underlying weighted network, even from very few experiments. In fact, we
demonstrate analytically that, under certain conditions, the GRN can be
perfectly reconstructed using $K = \Omega (d_{max})$ perturbation experiments,
where $d_{max}$ is the maximum in-degree of the GRN, a small value for
realistic sparse networks, indicating that RIDS can achieve high performance
with a scalable number of experiments. We test our method on both synthetic and
experimental data extracted from the DREAM5 network inference challenge. We
show that the RIDS achieves superior performance compared to the
state-of-the-art methods, while requiring as few as ~60% less experimental
data. Moreover, as opposed to almost all competing methods, RIDS allows us to
infer the directionality of the GRN links, allowing us to infer empirical GRNs,
without relying on the commonly provided list of transcription factors.
End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation
Authors: Heechul Jung, Min-Kook Choi, Kwon Soon, Woo Young Jung
Date: 2016-12-20 00:00:00
Tasks: Semantic Segmentation

Traditional pedestrian collision warning systems sometimes raise alarms even
when there is no danger (e.g., when all pedestrians are walking on the
sidewalk). These false alarms can make it difficult for drivers to concentrate
on their driving. In this paper, we propose a novel framework for an end-to-end
pedestrian collision warning system based on a convolutional neural network.
Semantic segmentation information is used to train the convolutional neural
network and two loss functions, such as cross entropy and Euclidean losses, are
minimized. Finally, we demonstrate the effectiveness of our method in reducing
false alarms and increasing warning accuracy compared to a traditional
histogram of oriented gradients (HoG)-based system.
Wide-Slice Residual Networks for Food Recognition
Authors: Niki Martinel, Gian Luca Foresti, Christian Micheloni
Date: 2016-12-20 00:00:00
Tasks: Image Classification

Food diary applications represent a tantalizing market. Such applications,
based on image food recognition, opened to new challenges for computer vision
and pattern recognition algorithms. Recent works in the field are focusing
either on hand-crafted representations or on learning these by exploiting deep
neural networks. Despite the success of such a last family of works, these
generally exploit off-the shelf deep architectures to classify food dishes.
Thus, the architectures are not cast to the specific problem. We believe that
better results can be obtained if the deep architecture is defined with respect
to an analysis of the food composition. Following such an intuition, this work
introduces a new deep scheme that is designed to handle the food structure.
Specifically, inspired by the recent success of residual deep network, we
exploit such a learning scheme and introduce a slice convolution block to
capture the vertical food layers. Outputs of the deep residual blocks are
combined with the sliced convolution to produce the classification score for
specific food categories. To evaluate our proposed architecture we have
conducted experimental results on three benchmark datasets. Results demonstrate
that our solution shows better performance with respect to existing approaches
(e.g., a top-1 accuracy of 90.27% on the Food-101 challenging dataset).
Data Centroid Based Multi-Level Fuzzy Min-Max Neural Network
Authors: Shraddha Deshmukh, Sagar Gandhi, Pratap Sanap, Vivek Kulkarni
Date: 2016-08-19 00:00:00
Tasks: Classification, General Classification

Recently, a multi-level fuzzy min max neural network (MLF) was proposed,
which improves the classification accuracy by handling an overlapped region
(area of confusion) with the help of a tree structure. In this brief, an
extension of MLF is proposed which defines a new boundary region, where the
previously proposed methods mark decisions with less confidence and hence
misclassification is more frequent. A methodology to classify patterns more
accurately is presented. Our work enhances the testing procedure by means of
data centroids. We exhibit an illustrative example, clearly highlighting the
advantage of our approach. Results on standard datasets are also presented to
evidentially prove a consistent improvement in the classification rate.
Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs
Authors: Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan, Gautam Shroff, Puneet Agarwal, Richa Rawat
Date: 2016-12-20 00:00:00
Tasks: Inductive logic programming

We investigate solving discrete optimisation problems using the estimation of
distribution (EDA) approach via a novel combination of deep belief
networks(DBN) and inductive logic programming (ILP).While DBNs are used to
learn the structure of successively better feasible solutions,ILP enables the
incorporation of domain-based background knowledge related to the goodness of
solutions.Recent work showed that ILP could be an effective way to use domain
knowledge in an EDA scenario.However,in a purely ILP-based EDA,sampling
successive populations is either inefficient or not straightforward.In our
Neuro-symbolic EDA,an ILP engine is used to construct a model for good
solutions using domain-based background knowledge.These rules are introduced as
Boolean features in the last hidden layer of DBNs used for EDA-based
optimization.This incorporation of logical ILP features requires some changes
while training and sampling from DBNs: (a)our DBNs need to be trained with data
for units at the input layer as well as some units in an otherwise hidden
layer, and (b)we would like the samples generated to be drawn from instances
entailed by the logical model.We demonstrate the viability of our approach on
instances of two optimisation problems: predicting optimal depth-of-win for the
KRK endgame,and jobshop scheduling.Our results are promising: (i)On each
iteration of distribution estimation,samples obtained with an ILP-assisted DBN
have a substantially greater proportion of good solutions than samples
generated using a DBN without ILP features, and (ii)On termination of
distribution estimation,samples obtained using an ILP-assisted DBN contain more
near-optimal samples than samples from a DBN without ILP features.These results
suggest that the use of ILP-constructed theories could be useful for
incorporating complex domain-knowledge into deep models for estimation of
distribution based procedures.
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
Authors: Forrest Iandola
Date: 2016-12-20 00:00:00

In recent years, the research community has discovered that deep neural
networks (DNNs) and convolutional neural networks (CNNs) can yield higher
accuracy than all previous solutions to a broad array of machine learning
problems. To our knowledge, there is no single CNN/DNN architecture that solves
all problems optimally. Instead, the "right" CNN/DNN architecture varies
depending on the application at hand. CNN/DNNs comprise an enormous design
space. Quantitatively, we find that a small region of the CNN design space
contains 30 billion different CNN architectures.
  In this dissertation, we develop a methodology that enables systematic
exploration of the design space of CNNs. Our methodology is comprised of the
following four themes.
  1. Judiciously choosing benchmarks and metrics.
  2. Rapidly training CNN models.
  3. Defining and describing the CNN design space.
  4. Exploring the design space of CNN architectures.
  Taken together, these four themes comprise an effective methodology for
discovering the "right" CNN architectures to meet the needs of practical
applications.
Asynchronous Stochastic Gradient Descent with Variance Reduction for Non-Convex Optimization
Authors: Zhouyuan Huo, Heng Huang
Date: 2016-04-12 00:00:00

We provide the first theoretical analysis on the convergence rate of the
asynchronous stochastic variance reduced gradient (SVRG) descent algorithm on
non-convex optimization. Recent studies have shown that the asynchronous
stochastic gradient descent (SGD) based algorithms with variance reduction
converge with a linear convergent rate on convex problems. However, there is no
work to analyze asynchronous SGD with variance reduction technique on
non-convex problem. In this paper, we study two asynchronous parallel
implementations of SVRG: one is on a distributed memory system and the other is
on a shared memory system. We provide the theoretical analysis that both
algorithms can obtain a convergence rate of $O(1/T)$, and linear speed up is
achievable if the number of workers is upper bounded. V1,v2,v3 have been
withdrawn due to reference issue, please refer the newest version v4.