pwc-archive/papers-with-abstracts huggingface dataset:
Anomaly Detection in Clutter using Spectrally Enhanced Ladar
Authors: Puneet S Chhabra, Andrew M. Wallace, James R Hopgood
Date: 2016-02-17 00:00:00
Tasks: Anomaly Detection

Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a
series of echoes that reflect from objects in a scene. These can be first, last
or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure
the intensity of light reflected from objects continuously over a period of
time. In a camouflaged scenario, e.g., objects hidden behind dense foliage, a
FW-Ladar penetrates such foliage and returns a sequence of echoes including
buried faint echoes. The aim of this paper is to learn local-patterns of
co-occurring echoes characterised by their measured spectra. A deviation from
such patterns defines an abnormal event in a forest/tree depth profile. As far
as the authors know, neither DR or FW-Ladar, along with several spectral
measurements, has not been applied to anomaly detection. This work presents an
algorithm that allows detection of spectral and temporal anomalies in FW-Multi
Spectral Ladar (FW-MSL) data samples. An anomaly is defined as a full waveform
temporal and spectral signature that does not conform to a prior expectation,
represented using a learnt subspace (dictionary) and set of coefficients that
capture co-occurring local-patterns using an overlapping temporal window. A
modified optimization scheme is proposed for subspace learning based on
stochastic approximations. The objective function is augmented with a
discriminative term that represents the subspace's separability properties and
supports anomaly characterisation. The algorithm detects several man-made
objects and anomalous spectra hidden in a dense clutter of vegetation and also
allows tree species classification.
2D SEM images turn into 3D object models
Authors: Wichai Shanklin
Date: 2016-02-17 00:00:00
Tasks: Object, Surface Reconstruction

The scanning electron microscopy (SEM) is probably one the most fascinating
examination approach that has been used since more than two decades to detailed
inspection of micro scale objects. Most of the scanning electron microscopes
could only produce 2D images that could not assist operational analysis of
microscopic surface properties. Computer vision algorithms combined with very
advanced geometry and mathematical approaches turn any SEM into a full 3D
measurement device. This work focuses on a methodical literature review for
automatic 3D surface reconstruction of scanning electron microscope images.
Convolutional networks and learning invariant to homogeneous multiplicative scalings
Authors: Mark Tygert, Arthur Szlam, Soumith Chintala, Marc'Aurelio Ranzato, Yuandong Tian, Wojciech Zaremba
Date: 2015-06-26 00:00:00
Tasks: Classification, General Classification, regression

The conventional classification schemes -- notably multinomial logistic
regression -- used in conjunction with convolutional networks (convnets) are
classical in statistics, designed without consideration for the usual coupling
with convnets, stochastic gradient descent, and backpropagation. In the
specific application to supervised learning for convnets, a simple
scale-invariant classification stage turns out to be more robust than
multinomial logistic regression, appears to result in slightly lower errors on
several standard test sets, has similar computational costs, and features
precise control over the actual rate of learning. "Scale-invariant" means that
multiplying the input values by any nonzero scalar leaves the output unchanged.
A Sparse PCA Approach to Clustering
Authors: T. Tony Cai, Linjun Zhang
Date: 2016-02-16 00:00:00
Tasks: Clustering

We discuss a clustering method for Gaussian mixture model based on the sparse
principal component analysis (SPCA) method and compare it with the IF-PCA
method. We also discuss the dependent case where the covariance matrix $\Sigma$
is not necessarily diagonal.
BioSpaun: A large-scale behaving brain model with complex neurons
Authors: Chris Eliasmith, Jan Gosmann, Xuan Choo
Date: 2016-02-16 00:00:00
Tasks: Blocking

We describe a large-scale functional brain model that includes detailed,
conductance-based, compartmental models of individual neurons. We call the
model BioSpaun, to indicate the increased biological plausibility of these
neurons, and because it is a direct extension of the Spaun model
\cite{Eliasmith2012b}. We demonstrate that including these detailed
compartmental models does not adversely affect performance across a variety of
tasks, including digit recognition, serial working memory, and counting. We
then explore the effects of applying TTX, a sodium channel blocking drug, to
the model. We characterize the behavioral changes that result from this
molecular level intervention. We believe this is the first demonstration of a
large-scale brain model that clearly links low-level molecular interventions
and high-level behavior.
Experimental analysis of data-driven control for a building heating system
Authors: Giuseppe Tommaso Costanzo, Sandro Iacovella, Frederik Ruelens, T. Leurs, Bert Claessens
Date: 2015-07-13 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Driven by the opportunity to harvest the flexibility related to building
climate control for demand response applications, this work presents a
data-driven control approach building upon recent advancements in reinforcement
learning. More specifically, model assisted batch reinforcement learning is
applied to the setting of building climate control subjected to a dynamic
pricing. The underlying sequential decision making problem is cast on a markov
decision problem, after which the control algorithm is detailed. In this work,
fitted Q-iteration is used to construct a policy from a batch of experimental
tuples. In those regions of the state space where the experimental sample
density is low, virtual support samples are added using an artificial neural
network. Finally, the resulting policy is shaped using domain knowledge. The
control approach has been evaluated quantitatively using a simulation and
qualitatively in a living lab. From the quantitative analysis it has been found
that the control approach converges in approximately 20 days to obtain a
control policy with a performance within 90% of the mathematical optimum. The
experimental analysis confirms that within 10 to 20 days sensible policies are
obtained that can be used for different outside temperature regimes.
Support Consistency of Direct Sparse-Change Learning in Markov Networks
Authors: Song Liu, Taiji Suzuki, Raissa Relator, Jun Sese, Masashi Sugiyama, Kenji Fukumizu
Date: 2014-07-02 00:00:00
Tasks: Change Detection

We study the problem of learning sparse structure changes between two Markov
networks $P$ and $Q$. Rather than fitting two Markov networks separately to two
sets of data and figuring out their differences, a recent work proposed to
learn changes \emph{directly} via estimating the ratio between two Markov
network models. In this paper, we give sufficient conditions for
\emph{successful change detection} with respect to the sample size $n_p, n_q$,
the dimension of data $m$, and the number of changed edges $d$. When using an
unbounded density ratio model we prove that the true sparse changes can be
consistently identified for $n_p = \Omega(d^2 \log \frac{m^2+m}{2})$ and $n_q =
\Omega({n_p^2})$, with an exponentially decaying upper-bound on learning error.
Such sample complexity can be improved to $\min(n_p, n_q) = \Omega(d^2 \log
\frac{m^2+m}{2})$ when the boundedness of the density ratio model is assumed.
Our theoretical guarantee can be applied to a wide range of discrete/continuous
Markov networks.
An Approach for Noise Removal on Depth Images
Authors: Rashi Chaudhary, Himanshu Dasgupta
Date: 2016-02-16 00:00:00
Tasks: 3D Reconstruction, Object Recognition

Image based rendering is a fundamental problem in computer vision and
graphics. Modern techniques often rely on depth image for the 3D construction.
However for most of the existing depth cameras, the large and unpredictable
noises can be problematic, which can cause noticeable artifacts in the rendered
results. In this paper, we proposed an efficacious method for depth image noise
removal that can be applied for most RGBD systems. The proposed solution will
benefit many subsequent vision problems such as 3D reconstruction, novel view
rendering, object recognition. Our experimental results demonstrate the
efficacy and accuracy.
Fast Learning Requires Good Memory: A Time-Space Lower Bound for Parity Learning
Authors: Ran Raz
Date: 2016-02-16 00:00:00

We prove that any algorithm for learning parities requires either a memory of
quadratic size or an exponential number of samples. This proves a recent
conjecture of Steinhardt, Valiant and Wager and shows that for some learning
problems a large storage space is crucial.
  More formally, in the problem of parity learning, an unknown string $x \in
\{0,1\}^n$ was chosen uniformly at random. A learner tries to learn $x$ from a
stream of samples $(a_1, b_1), (a_2, b_2) \ldots$, where each~$a_t$ is
uniformly distributed over $\{0,1\}^n$ and $b_t$ is the inner product of $a_t$
and $x$, modulo~2. We show that any algorithm for parity learning, that uses
less than $\frac{n^2}{25}$ bits of memory, requires an exponential number of
samples.
  Previously, there was no non-trivial lower bound on the number of samples
needed, for any learning problem, even if the allowed memory size is $O(n)$
(where $n$ is the space needed to store one sample).
  We also give an application of our result in the field of bounded-storage
cryptography. We show an encryption scheme that requires a private key of
length $n$, as well as time complexity of $n$ per encryption/decription of each
bit, and is provenly and unconditionally secure as long as the attacker uses
less than $\frac{n^2}{25}$ memory bits and the scheme is used at most an
exponential number of times. Previous works on bounded-storage cryptography
assumed that the memory size used by the attacker is at most linear in the time
needed for encryption/decription.
Fuzzy Object-Oriented Dynamic Networks. II
Authors: D. A. Terletskyi, A. I. Provotar
Date: 2016-02-04 00:00:00
Tasks: Object

This article generalizes object-oriented dynamic networks to the fuzzy case,
which allows one to represent knowledge on objects and classes of objects that
are fuzzy by nature and also to model their changes in time. Within the
framework of the approach described, a mechanism is proposed that makes it
possible to acquire new knowledge on the basis of basic knowledge and
considerably differs from well-known methods used in existing models of
knowledge representation. The approach is illustrated by an example of
construction of a concrete fuzzy object-oriented dynamic network.
Fuzzy Object-Oriented Dynamic Networks. I
Authors: D. A. Terletskyi, A. I. Provotar
Date: 2016-01-07 00:00:00
Tasks: Object

The concepts of fuzzy objects and their classes are described that make it
possible to structurally represent knowledge about fuzzy and partially-defined
objects and their classes. Operations over such objects and classes are also
proposed that make it possible to obtain sets and new classes of fuzzy objects
and also to model variations in object structures under the influence of
external factors.
A Harmonic Extension Approach for Collaborative Ranking
Authors: Da Kuang, Zuoqiang Shi, Stanley Osher, Andrea Bertozzi
Date: 2016-02-16 00:00:00
Tasks: Collaborative Ranking, Computational Efficiency, Matrix Completion, Recommendation Systems

We present a new perspective on graph-based methods for collaborative ranking
for recommender systems. Unlike user-based or item-based methods that compute a
weighted average of ratings given by the nearest neighbors, or low-rank
approximation methods using convex optimization and the nuclear norm, we
formulate matrix completion as a series of semi-supervised learning problems,
and propagate the known ratings to the missing ones on the user-user or
item-item graph globally. The semi-supervised learning problems are expressed
as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and
can be discretized by a point integral method. We show that our approach does
not impose a low-rank Euclidean subspace on the data points, but instead
minimizes the dimension of the underlying manifold. Our method, named LDM (low
dimensional manifold), turns out to be particularly effective in generating
rankings of items, showing decent computational efficiency and robust ranking
quality compared to state-of-the-art methods.
Practical Introduction to Clustering Data
Authors: Alexander K. Hartmann
Date: 2016-02-16 00:00:00
Tasks: Clustering, General Classification

Data clustering is an approach to seek for structure in sets of complex data,
i.e., sets of "objects". The main objective is to identify groups of objects
which are similar to each other, e.g., for classification. Here, an
introduction to clustering is given and three basic approaches are introduced:
the k-means algorithm, neighbour-based clustering, and an agglomerative
clustering method. For all cases, C source code examples are given, allowing
for an easy implementation.
Convolutional Clustering for Unsupervised Learning
Authors: Aysegul Dundar, Jonghoon Jin, Eugenio Culurciello
Date: 2015-11-19 00:00:00
Tasks: Clustering, Image Classification

The task of labeling data for training deep neural networks is daunting and
tedious, requiring millions of labels to achieve the current state-of-the-art
results. Such reliance on large amounts of labeled data can be relaxed by
exploiting hierarchical features via unsupervised learning techniques. In this
work, we propose to train a deep convolutional network based on an enhanced
version of the k-means clustering algorithm, which reduces the number of
correlated parameters in the form of similar filters, and thus increases test
categorization accuracy. We call our algorithm convolutional k-means
clustering. We further show that learning the connection between the layers of
a deep convolutional neural network improves its ability to be trained on a
smaller amount of labeled data. Our experiments show that the proposed
algorithm outperforms other techniques that learn filters unsupervised.
Specifically, we obtained a test accuracy of 74.1% on STL-10 and a test error
of 0.5% on MNIST.
Union of Low-Rank Subspaces Detector
Authors: Mohsen Joneidi, Parvin Ahmadi, Mostafa Sadeghi, Nazanin Rahnavard
Date: 2013-07-29 00:00:00
Tasks: Action Detection, Activity Detection

The problem of signal detection using a flexible and general model is
considered. Due to applicability and flexibility of sparse signal
representation and approximation, it has attracted a lot of attention in many
signal processing areas. In this paper, we propose a new detection method based
on sparse decomposition in a union of subspaces (UoS) model. Our proposed
detector uses a dictionary that can be interpreted as a bank of matched
subspaces. This improves the performance of signal detection, as it is a
generalization for detectors. Low-rank assumption for the desired signals
implies that the representations of these signals in terms of some proper bases
would be sparse. Our proposed detector exploits sparsity in its decision rule.
We demonstrate the high efficiency of our method in the cases of voice activity
detection in speech processing.
Deep Reinforcement Learning in Parameterized Action Space
Authors: Matthew Hausknecht, Peter Stone
Date: 2015-11-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces. To fill this gap, this paper focuses on learning within the domain of simulated RoboCup soccer, which features a small set of discrete action types, each of which is parameterized with continuous variables. The best learned agent can score goals more reliably than the 2012 RoboCup champion agent. As such, this paper represents a successful extension of deep reinforcement learning to the class of parameterized action space MDPs.
Limiting fitness distributions in evolutionary dynamics
Authors: Matteo Smerlak, Ahmed Youssef
Date: 2015-11-01 00:00:00

Darwinian evolution can be modeled in general terms as a flow in the space of
fitness (i.e. reproductive rate) distributions. In the diffusion approximation,
Tsimring et al. have showed that this flow admits "fitness wave" solutions:
Gaussian-shape fitness distributions moving towards higher fitness values at
constant speed. Here we show more generally that evolving fitness distributions
are attracted to a one-parameter family of distributions with a fixed parabolic
relationship between skewness and kurtosis. Unlike fitness waves, this
statistical pattern encompasses both positive and negative (a.k.a. purifying)
selection and is not restricted to rapidly adapting populations. Moreover we
find that the mean fitness of a population under the selection of pre-existing
variation is a power-law function of time, as observed in microbiological
evolution experiments but at variance with fitness wave theory. At the
conceptual level, our results can be viewed as the resolution of the "dynamic
insufficiency" of Fisher's fundamental theorem of natural selection. Our
predictions are in good agreement with numerical simulations.
Cells in Multidimensional Recurrent Neural Networks
Authors: G. Leifert, T. Strauß, T. Grüning, R. Labahn
Date: 2014-12-08 00:00:00

The transcription of handwritten text on images is one task in machine
learning and one solution to solve it is using multi-dimensional recurrent
neural networks (MDRNN) with connectionist temporal classification (CTC). The
RNNs can contain special units, the long short-term memory (LSTM) cells. They
are able to learn long term dependencies but they get unstable when the
dimension is chosen greater than one. We defined some useful and necessary
properties for the one-dimensional LSTM cell and extend them in the
multi-dimensional case. Thereby we introduce several new cells with better
stability. We present a method to design cells using the theory of linear shift
invariant systems. The new cells are compared to the LSTM cell on the IFN/ENIT
and Rimes database, where we can improve the recognition rate compared to the
LSTM cell. So each application where the LSTM cells in MDRNNs are used could be
improved by substituting them by the new developed cells.
Contextual Media Retrieval Using Natural Language Queries
Authors: Sreyasi Nag Chowdhury, Mateusz Malinowski, Andreas Bulling, Mario Fritz
Date: 2016-02-16 00:00:00
Tasks: Natural Language Queries, Retrieval

The widespread integration of cameras in hand-held and head-worn devices as
well as the ability to share content online enables a large and diverse visual
capture of the world that millions of users build up collectively every day. We
envision these images as well as associated meta information, such as GPS
coordinates and timestamps, to form a collective visual memory that can be
queried while automatically taking the ever-changing context of mobile users
into account. As a first step towards this vision, in this work we present
Xplore-M-Ego: a novel media retrieval system that allows users to query a
dynamic database of images and videos using spatio-temporal natural language
queries. We evaluate our system using a new dataset of real user queries as
well as through a usability study. One key finding is that there is a
considerable amount of inter-user variability, for example in the resolution of
spatial relations in natural language utterances. We show that our retrieval
system can cope with this variability using personalisation through an online
learning-based retrieval formulation.
Optimizing Gaze Direction in a Visual Navigation Task
Authors: Tuomas Välimäki, Risto Ritala
Date: 2016-02-16 00:00:00
Tasks: Visual Navigation

Navigation in an unknown environment consists of multiple separable subtasks,
such as collecting information about the surroundings and navigating to the
current goal. In the case of pure visual navigation, all these subtasks need to
utilize the same vision system, and therefore a way to optimally control the
direction of focus is needed. We present a case study, where we model the
active sensing problem of directing the gaze of a mobile robot with three
machine vision cameras as a partially observable Markov decision process
(POMDP) using a mutual information (MI) based reward function. The key aspect
of the solution is that the cameras are dynamically used either in monocular or
stereo configuration. The benefits of using the proposed active sensing
implementation are demonstrated with simulations and experiments on a real
robot.
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining
Authors: Emile Contal, Nicolas Vayatis
Date: 2016-02-16 00:00:00
Tasks: Gaussian Processes, global-optimization

The paper considers the problem of global optimization in the setup of
stochastic process bandits. We introduce an UCB algorithm which builds a
cascade of discretization trees based on generic chaining in order to render
possible his operability over a continuous domain. The theoretical framework
applies to functions under weak probabilistic smoothness assumptions and also
extends significantly the spectrum of application of UCB strategies. Moreover
generic regret bounds are derived which are then specialized to Gaussian
processes indexed on infinite-dimensional spaces as well as to quadratic forms
of Gaussian processes. Lower bounds are also proved in the case of Gaussian
processes to assess the optimality of the proposed algorithm.
Deep Learning-Based Image Kernel for Inductive Transfer
Authors: Neeraj Kumar, Animesh Karmakar, Ranti Dev Sharma, Abhinav Mittal, Amit Sethi
Date: 2015-12-13 00:00:00
Tasks: Deep Learning, Transfer Learning

We propose a method to classify images from target classes with a small
number of training examples based on transfer learning from non-target classes.
Without using any more information than class labels for samples from
non-target classes, we train a Siamese net to estimate the probability of two
images to belong to the same class. With some post-processing, output of the
Siamese net can be used to form a gram matrix of a Mercer kernel. Coupled with
a support vector machine (SVM), such a kernel gave reasonable classification
accuracy on target classes without any fine-tuning. When the Siamese net was
only partially fine-tuned using a small number of samples from the target
classes, the resulting classifier outperformed the state-of-the-art and other
alternatives. We share class separation capabilities and insights into the
learning process of such a kernel on MNIST, Dogs vs. Cats, and CIFAR-10
datasets.
Reinforcement Learning approach for Real Time Strategy Games Battle city and S3
Authors: Harshit Sethy, Amit Patel
Date: 2016-02-16 00:00:00
Tasks: Q-Learning, Real-Time Strategy Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we proposed reinforcement learning algorithms with the
generalized reward function. In our proposed method we use Q-learning and SARSA
algorithms with generalised reward function to train the reinforcement learning
agent. We evaluated the performance of our proposed algorithms on two real-time
strategy games called BattleCity and S3. There are two main advantages of
having such an approach as compared to other works in RTS. (1) We can ignore
the concept of a simulator which is often game specific and is usually hard
coded in any type of RTS games (2) our system can learn from interaction with
any opponents and quickly change the strategy according to the opponents and do
not need any human traces as used in previous works. Keywords : Reinforcement
learning, Machine learning, Real time strategy, Artificial intelligence.