pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Coral Classification Using Deep Convolutional Neural Networks
Authors: Mohamed Elawady
Date: 2015-11-29 00:00:00
Tasks: Classification, Color Normalization, General Classification, Image Enhancement

Autonomous repair of deep-sea coral reefs is a recent proposed idea to
support the oceans ecosystem in which is vital for commercial fishing, tourism
and other species. This idea can be operated through using many small
autonomous underwater vehicles (AUVs) and swarm intelligence techniques to
locate and replace chunks of coral which have been broken off, thus enabling
re-growth and maintaining the habitat. The aim of this project is developing
machine vision algorithms to enable an underwater robot to locate a coral reef
and a chunk of coral on the seabed and prompt the robot to pick it up. Although
there is no literature on this particular problem, related work on fish
counting may give some insight into the problem. The technical challenges are
principally due to the potential lack of clarity of the water and platform
stabilization as well as spurious artifacts (rocks, fish, and crabs). We
present an efficient sparse classification for coral species using supervised
deep learning method called Convolutional Neural Networks (CNNs). We compute
Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component
Analysis (ZCA) Whitening to extract shape and texture feature descriptors,
which are employed to be supplementary channels (feature-based maps) besides
basic spatial color channels (spatial-based maps) of coral input image, we also
experiment state-of-art preprocessing underwater algorithms for image
enhancement and color normalization and color conversion adjustment. Our
proposed coral classification method is developed under MATLAB platform, and
evaluated by two different coral datasets (University of California San Diego's
Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea).
Multimodal Skip-gram Using Convolutional Pseudowords
Authors: Zachary Seymour, Yingming Li, Zhongfei Zhang
Date: 2015-11-12 00:00:00
Tasks: Object Recognition, Retrieval, Video Retrieval, Word Similarity

This work studies the representational mapping across multimodal data such
that given a piece of the raw data in one modality the corresponding semantic
description in terms of the raw data in another modality is immediately
obtained. Such a representational mapping can be found in a wide spectrum of
real-world applications including image/video retrieval, object recognition,
action/behavior recognition, and event understanding and prediction. To that
end, we introduce a simplified training objective for learning multimodal
embeddings using the skip-gram architecture by introducing convolutional
"pseudowords:" embeddings composed of the additive combination of distributed
word representations and image features from convolutional neural networks
projected into the multimodal space. We present extensive results of the
representational properties of these embeddings on various word similarity
benchmarks to show the promise of this approach.
Optimization over Sparse Symmetric Sets via a Nonmonotone Projected Gradient Method
Authors: Zhaosong Lu
Date: 2015-09-29 00:00:00

We consider the problem of minimizing a Lipschitz differentiable function
over a class of sparse symmetric sets that has wide applications in engineering
and science. For this problem, it is known that any accumulation point of the
classical projected gradient (PG) method with a constant stepsize $1/L$
satisfies the $L$-stationarity optimality condition that was introduced in [3].
In this paper we introduce a new optimality condition that is stronger than the
$L$-stationarity optimality condition. We also propose a nonmonotone projected
gradient (NPG) method for this problem by incorporating some support-changing
and coordintate-swapping strategies into a projected gradient method with
variable stepsizes. It is shown that any accumulation point of NPG satisfies
the new optimality condition and moreover it is a coordinatewise stationary
point. Under some suitable assumptions, we further show that it is a global or
a local minimizer of the problem. Numerical experiments are conducted to
compare the performance of PG and NPG. The computational results demonstrate
that NPG has substantially better solution quality than PG, and moreover, it is
at least comparable to, but sometimes can be much faster than PG in terms of
speed.
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice
Authors: Frederik Ruelens, Bert Claessens, Salman Quaiyum, Bart De Schutter, Robert Babuska, Ronnie Belmans
Date: 2015-11-29 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Electric water heaters have the ability to store energy in their water buffer
without impacting the comfort of the end user. This feature makes them a prime
candidate for residential demand response. However, the stochastic and
nonlinear dynamics of electric water heaters, makes it challenging to harness
their flexibility. Driven by this challenge, this paper formulates the
underlying sequential decision-making problem as a Markov decision process and
uses techniques from reinforcement learning. Specifically, we apply an
auto-encoder network to find a compact feature representation of the sensor
measurements, which helps to mitigate the curse of dimensionality. A wellknown
batch reinforcement learning technique, fitted Q-iteration, is used to find a
control policy, given this feature representation. In a simulation-based
experiment using an electric water heater with 50 temperature sensors, the
proposed method was able to achieve good policies much faster than when using
the full state information. In a lab experiment, we apply fitted Q-iteration to
an electric water heater with eight temperature sensors. Further reducing the
state vector did not improve the results of fitted Q-iteration. The results of
the lab experiment, spanning 40 days, indicate that compared to a thermostat
controller, the presented approach was able to reduce the total cost of energy
consumption of the electric water heater by 15%.
On-line Recognition of Handwritten Mathematical Symbols
Authors: Martin Thoma
Date: 2015-11-29 00:00:00
Tasks: Data Augmentation

Finding the name of an unknown symbol is often hard, but writing the symbol
is easy. This bachelor's thesis presents multiple systems that use the pen
trajectory to classify handwritten symbols. Five preprocessing steps, one data
augmentation algorithm, five features and five variants for multilayer
Perceptron training were evaluated using 166898 recordings which were collected
with two crowdsourcing projects. The evaluation results of these 21 experiments
were used to create an optimized recognizer which has a TOP1 error of less than
17.5% and a TOP3 error of 4.0%. This is an improvement of 18.5% for the TOP1
error and 29.7% for the TOP3 error.
How do the naive Bayes classifier and the Support Vector Machine compare in their ability to forecast the Stock Exchange of Thailand?
Authors: Napas Udomsak
Date: 2015-11-29 00:00:00

This essay investigates the question of how the naive Bayes classifier and
the support vector machine compare in their ability to forecast the Stock
Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier
is explored. The algorithms are trained using data from the month of January
2010, extracted from the MarketWatch.com website. Input features are selected
based on previous studies of the SET100 Index. The Weka 3 software is used to
create models from the labeled training data. Mean squared error and proportion
of correctly classified instances, and a number of other error measurements are
the used to compare the two algorithms. This essay shows that these two
algorithms are currently not advanced enough to accurately model the stock
exchange. Nevertheless, the naive Bayes is better than the support vector
machine at predicting the Stock Exchange of Thailand.
Solving a Mathematical Problem in Square War: a Go-like Board Game
Authors: Chu Luo
Date: 2015-07-26 00:00:00

In this paper, we present a board game: Square War. The game definition of
Square War is similar to the classic Chinese board game Go. Then we propose a
mathematical problem of the game Square War. Finally, we show that the problem
can be solved by using a method of mixed mathematics and computer science.
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
Authors: Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov
Date: 2015-02-19 00:00:00
Tasks: Question Answering, Reading Comprehension

One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks.
Robotic Search & Rescue via Online Multi-task Reinforcement Learning
Authors: Lisa Lee
Date: 2015-11-29 00:00:00
Tasks: Lifelong learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is a general and well-known method that a robot
can use to learn an optimal control policy to solve a particular task. We would
like to build a versatile robot that can learn multiple tasks, but using RL for
each of them would be prohibitively expensive in terms of both time and
wear-and-tear on the robot. To remedy this problem, we use the Policy Gradient
Efficient Lifelong Learning Algorithm (PG-ELLA), an online multi-task RL
algorithm that enables the robot to efficiently learn multiple consecutive
tasks by sharing knowledge between these tasks to accelerate learning and
improve performance. We implemented and evaluated three RL methods--Q-learning,
policy gradient RL, and PG-ELLA--on a ground robot whose task is to find a
target object in an environment under different surface conditions. In this
paper, we discuss our implementations as well as present an empirical analysis
of their learning performance.
Sparseness helps: Sparsity Augmented Collaborative Representation for Classification
Authors: Naveed Akhtar, Faisal Shafait, Ajmal Mian
Date: 2015-11-29 00:00:00
Tasks: Classification, General Classification

Many classification approaches first represent a test sample using the
training samples of all the classes. This collaborative representation is then
used to label the test sample. It was a common belief that sparseness of the
representation is the key to success for this classification scheme. However,
more recently, it has been claimed that it is the collaboration and not the
sparseness that makes the scheme effective. This claim is attractive as it
allows to relinquish the computationally expensive sparsity constraint over the
representation. In this paper, we first extend the analysis supporting this
claim and then show that sparseness explicitly contributes to improved
classification, hence it should not be completely ignored for computational
gains. Inspired by this result, we augment a dense collaborative representation
with a sparse representation and propose an efficient classification method
that capitalizes on the resulting representation. The augmented representation
and the classification method work together meticulously to achieve higher
accuracy and lower computational time compared to state-of-the-art
collaborative representation based classification approaches. Experiments on
benchmark face, object and action databases show the efficacy of our approach.
k-Nearest Neighbour Classification of Datasets with a Family of Distances
Authors: Stan Hatko
Date: 2015-11-29 00:00:00
Tasks: General Classification, LEMMA

The $k$-nearest neighbour ($k$-NN) classifier is one of the oldest and most
important supervised learning algorithms for classifying datasets.
Traditionally the Euclidean norm is used as the distance for the $k$-NN
classifier. In this thesis we investigate the use of alternative distances for
the $k$-NN classifier.
  We start by introducing some background notions in statistical machine
learning. We define the $k$-NN classifier and discuss Stone's theorem and the
proof that $k$-NN is universally consistent on the normed space $R^d$. We then
prove that $k$-NN is universally consistent if we take a sequence of random
norms (that are independent of the sample and the query) from a family of norms
that satisfies a particular boundedness condition. We extend this result by
replacing norms with distances based on uniformly locally Lipschitz functions
that satisfy certain conditions. We discuss the limitations of Stone's lemma
and Stone's theorem, particularly with respect to quasinorms and adaptively
choosing a distance for $k$-NN based on the labelled sample. We show the
universal consistency of a two stage $k$-NN type classifier where we select the
distance adaptively based on a split labelled sample and the query. We conclude
by giving some examples of improvements of the accuracy of classifying various
datasets using the above techniques.
Bootstrapping Ternary Relation Extractors
Authors: Ndapandula Nakashole
Date: 2015-11-29 00:00:00
Tasks: Binary Relation Extraction, Relation, Relation Extraction, Triplet

Binary relation extraction methods have been widely studied in recent years. However, few methods have been developed for higher n-ary relation extraction. One limiting factor is the effort required to generate training data. For binary relations, one only has to provide a few dozen pairs of entities per relation, as training data. For ternary relations (n=3), each training instance is a triplet of entities, placing a greater cognitive load on people. For example, many people know that Google acquired Youtube but not the dollar amount or the date of the acquisition and many people know that Hillary Clinton is married to Bill Clinton by not the location or date of their wedding. This makes higher n-nary training data generation a time consuming exercise in searching the Web. We present a resource for training ternary relation extractors. This was generated using a minimally supervised yet effective approach. We present statistics on the size and the quality of the dataset.
MidRank: Learning to rank based on subsequences
Authors: Basura Fernando, Efstratios Gavves, Damien Muselet, Tinne Tuytelaars
Date: 2015-11-29 00:00:00
Tasks: Learning-To-Rank

We present a supervised learning to rank algorithm that effectively orders
images by exploiting the structure in image sequences. Most often in the
supervised learning to rank literature, ranking is approached either by
analyzing pairs of images or by optimizing a list-wise surrogate loss function
on full sequences. In this work we propose MidRank, which learns from
moderately sized sub-sequences instead. These sub-sequences contain useful
structural ranking information that leads to better learnability during
training and better generalization during testing. By exploiting sub-sequences,
the proposed MidRank improves ranking accuracy considerably on an extensive
array of image ranking applications and datasets.
Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking
Authors: Qi Guo, Le Dan, Dong Yin, Xiangyang Ji
Date: 2015-11-28 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

Multi-object tracking remains challenging due to frequent occurrence of
occlusions and outliers. In order to handle this problem, we propose an
Approximation-Shrink Scheme for sequential optimization. This scheme is
realized by introducing an Ambiguity-Clearness Graph to avoid conflicts and
maintain sequence independent, as well as a sliding window optimization
framework to constrain the size of state space and guarantee convergence. Based
on this window-wise framework, the states of targets are clustered in a
self-organizing manner. Moreover, we show that the traditional online and batch
tracking methods can be embraced by the window-wise framework. Experiments
indicate that with only a small window, the optimization performance can be
much better than online methods and approach to batch methods.
On the Depth of Deep Neural Networks: A Theoretical View
Authors: Shizhao Sun, Wei Chen, Li-Wei Wang, Xiaoguang Liu, Tie-Yan Liu
Date: 2015-06-17 00:00:00

People believe that depth plays an important role in success of deep neural
networks (DNN). However, this belief lacks solid theoretical justifications as
far as we know. We investigate role of depth from perspective of margin bound.
In margin bound, expected error is upper bounded by empirical margin error plus
Rademacher Average (RA) based capacity term. First, we derive an upper bound
for RA of DNN, and show that it increases with increasing depth. This indicates
negative impact of depth on test performance. Second, we show that deeper
networks tend to have larger representation power (measured by Betti numbers
based complexity) than shallower networks in multi-class setting, and thus can
lead to smaller empirical margin error. This implies positive impact of depth.
The combination of these two results shows that for DNN with restricted number
of hidden units, increasing depth is not always good since there is a tradeoff
between positive and negative impacts. These results inspire us to seek
alternative ways to achieve positive impact of depth, e.g., imposing
margin-based penalty terms to cross entropy loss so as to reduce empirical
margin error without increasing depth. Our experiments show that in this way,
we achieve significantly better test performance.
Applying deep learning to classify pornographic images and videos
Authors: Mohamed Moustafa
Date: 2015-11-28 00:00:00
Tasks: Deep Learning

It is no secret that pornographic material is now a one-click-away from
everyone, including children and minors. General social media networks are
striving to isolate adult images and videos from normal ones. Intelligent image
analysis methods can help to automatically detect and isolate questionable
images in media. Unfortunately, these methods require vast experience to design
the classifier including one or more of the popular computer vision feature
descriptors. We propose to build a classifier based on one of the recently
flourishing deep learning techniques. Convolutional neural networks contain
many layers for both automatic features extraction and classification. The
benefit is an easier system to build (no need for hand-crafting features and
classifiers). Additionally, our experiments show that it is even more accurate
than the state of the art methods on the most recent benchmark dataset.
Newton-Stein Method: An optimization method for GLMs via Stein's Lemma
Authors: Murat A. Erdogdu
Date: 2015-11-28 00:00:00
Tasks: LEMMA, Second-order methods

We consider the problem of efficiently computing the maximum likelihood
estimator in Generalized Linear Models (GLMs) when the number of observations
is much larger than the number of coefficients ($n \gg p \gg 1$). In this
regime, optimization algorithms can immensely benefit from approximate second
order information. We propose an alternative way of constructing the curvature
information by formulating it as an estimation problem and applying a
Stein-type lemma, which allows further improvements through sub-sampling and
eigenvalue thresholding. Our algorithm enjoys fast convergence rates,
resembling that of second order methods, with modest per-iteration cost. We
provide its convergence analysis for the general case where the rows of the
design matrix are samples from a sub-gaussian distribution. We show that the
convergence has two phases, a quadratic phase followed by a linear phase.
Finally, we empirically demonstrate that our algorithm achieves the highest
performance compared to various algorithms on several datasets.
Quantification in-the-wild: data-sets and baselines
Authors: Oscar Beijbom, Judy Hoffman, Evan Yao, Trevor Darrell, Alberto Rodriguez-Ramirez, Manuel Gonzalez-Rivero, Ove Hoegh - Guldberg
Date: 2015-10-16 00:00:00
Tasks: parameter estimation, Time Series, Time Series Analysis

Quantification is the task of estimating the class-distribution of a
data-set. While typically considered as a parameter estimation problem with
strict assumptions on the data-set shift, we consider quantification
in-the-wild, on two large scale data-sets from marine ecology: a survey of
Caribbean coral reefs, and a plankton time series from Martha's Vineyard
Coastal Observatory. We investigate several quantification methods from the
literature and indicate opportunities for future work. In particular, we show
that a deep neural network can be fine-tuned on a very limited amount of data
(25 - 100 samples) to outperform alternative methods.
Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog
Authors: Yingzhen Li, Ye Zhang
Date: 2012-08-21 00:00:00
Tasks: Recommendation Systems

Precise recommendation of followers helps in improving the user experience
and maintaining the prosperity of twitter and microblog platforms. In this
paper, we design a hybrid recommender system of microblog as a solution of KDD
Cup 2012, track 1 task, which requires predicting users a user might follow in
Tencent Microblog. We describe the background of the problem and present the
algorithm consisting of keyword analysis, user taxonomy, (potential)interests
extraction and item recommendation. Experimental result shows the high
performance of our algorithm. Some possible improvements are discussed, which
leads to further study.
Multiagent Cooperation and Competition with Deep Reinforcement Learning
Authors: Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin, Kristjan Korjus, Juhan Aru, Jaan Aru, Raul Vicente
Date: 2015-11-27 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multiagent systems appear in most social, economical, and political
situations. In the present work we extend the Deep Q-Learning Network
architecture proposed by Google DeepMind to multiagent environments and
investigate how two agents controlled by independent Deep Q-Networks interact
in the classic videogame Pong. By manipulating the classical rewarding scheme
of Pong we demonstrate how competitive and collaborative behaviors emerge.
Competitive agents learn to play and score efficiently. Agents trained under
collaborative rewarding schemes find an optimal strategy to keep the ball in
the game as long as possible. We also describe the progression from competitive
to collaborative behavior. The present work demonstrates that Deep Q-Networks
can become a practical tool for studying the decentralized learning of
multiagent systems living in highly complex environments.