pwc-archive/papers-with-abstracts huggingface dataset:
Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository
Authors: Mohammad Mahmudur Rahman Khan, Rezoana Bente Arif, Md. Abu Bakr Siddique, Mahjabin Rahman Oishe
Date: 2018-09-17 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning qualifies computers to assimilate with data, without being
solely programmed [1, 2]. Machine learning can be classified as supervised and
unsupervised learning. In supervised learning, computers learn an objective
that portrays an input to an output hinged on training input-output pairs [3].
Most efficient and widely used supervised learning algorithms are K-Nearest
Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
paper is to implement these elegant learning algorithms on eleven different
datasets from the UCI machine learning repository to observe the variation of
accuracies for each of the algorithms on all datasets. Analyzing the accuracy
of the algorithms will give us a brief idea about the relationship of the
machine learning algorithms and the data dimensionality. All the algorithms are
developed in Matlab. Upon such accuracy observation, the comparison can be
built among KNN, SVM, LMNN, and ENN regarding their performances on each
dataset.
Dual Encoding for Zero-Example Video Retrieval
Authors: Jianfeng Dong, Xirong Li, Chaoxi Xu, Shouling Ji, Yuan He, Gang Yang, Xun Wang
Date: 2018-09-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Ad-hoc video search, Retrieval, Video Retrieval

This paper attacks the challenging problem of zero-example video retrieval.
In such a retrieval paradigm, an end user searches for unlabeled videos by
ad-hoc queries described in natural language text with no visual example
provided. Given videos as sequences of frames and queries as sequences of
words, an effective sequence-to-sequence cross-modal matching is required. The
majority of existing methods are concept based, extracting relevant concepts
from queries and videos and accordingly establishing associations between the
two modalities. In contrast, this paper takes a concept-free approach,
proposing a dual deep encoding network that encodes videos and queries into
powerful dense representations of their own. Dual encoding is conceptually
simple, practically effective and end-to-end. As experiments on three
benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the
proposed solution establishes a new state-of-the-art for zero-example video
retrieval.
Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach
Authors: Riccardo Zese, Giuseppe Cota, Evelina Lamma, Elena Bellodi, Fabrizio Riguzzi
Date: 2018-09-17 00:00:00

When modeling real world domains we have to deal with information that is
incomplete or that comes from sources with different trust levels. This
motivates the need for managing uncertainty in the Semantic Web. To this
purpose, we introduced a probabilistic semantics, named DISPONTE, in order to
combine description logics with probability theory. The probability of a query
can be then computed from the set of its explanations by building a Binary
Decision Diagram (BDD). The set of explanations can be found using the tableau
algorithm, which has to handle non-determinism. Prolog, with its efficient
handling of non-determinism, is suitable for implementing the tableau
algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the
tableau algorithm. TRILLP builds a pinpointing formula, that compactly
represents the set of explanations and can be directly translated into a BDD.
Both reasoners were shown to outperform state-of-the-art DL reasoners. In this
paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is
directly built during the construction of the tableau, further speeding up the
overall inference process. An experimental comparison shows the effectiveness
of TORNADO. All systems can be tried online in the TRILL on SWISH web
application at http://trill.ml.unife.it/.
Learning of Multi-Context Models for Autonomous Underwater Vehicles
Authors: Bilal Wehbe, Octavio Arriaga, Mario Michael Krell, Frank Kirchner
Date: 2018-09-17 00:00:00
Tasks: General Classification

Multi-context model learning is crucial for marine robotics where several
factors can cause disturbances to the system's dynamics. This work addresses
the problem of identifying multiple contexts of an AUV model. We build a
simulation model of the robot from experimental data, and use it to fill in the
missing data and generate different model contexts. We implement an
architecture based on long-short-term-memory (LSTM) networks to learn the
different contexts directly from the data. We show that the LSTM network can
achieve high classification accuracy compared to baseline methods, showing
robustness against noise and scaling efficiently on large datasets.
The Importance of Being Earnest: Performance of Modulation Classification for Real RF Signals
Authors: Colin de Vrieze, Ljiljana Simić, Petri Mähönen
Date: 2018-09-17 00:00:00
Tasks: General Classification

Digital modulation classification (DMC) can be highly valuable for equipping
radios with increased spectrum awareness in complex emerging wireless networks.
However, as the existing literature is overwhelmingly based on theoretical or
simulation results, it is unclear how well DMC performs in practice. In this
paper we study the performance of DMC in real-world wireless networks, using an
extensive RF signal dataset of 250,000 over-the-air transmissions with
heterogeneous transceiver hardware and co-channel interference. Our results
show that DMC can achieve a high classification accuracy even under the
challenging real-world conditions of modulated co-channel interference and
low-grade hardware. However, this only holds if the training dataset fully
captures the variety of interference and hardware types in the real radio
environment; otherwise, the DMC performance deteriorates significantly. Our
work has two important engineering implications. First, it shows that it is not
straightforward to exchange learned classifier models among dissimilar radio
environments and devices in practice. Second, our analysis suggests that the
key missing link for real-world deployment of DMC is designing signal features
that generalize well to diverse wireless network scenarios. We are making our
RF signal dataset publicly available as a step towards a unified framework for
realistic DMC evaluation.
Graph Neural Networks for IceCube Signal Classification
Authors: Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna
Date: 2018-09-17 00:00:00
Tasks: Classification, General Classification

Tasks involving the analysis of geometric (graph- and manifold-structured)
data have recently gained prominence in the machine learning community, giving
birth to a rapidly developing field of geometric deep learning. In this work,
we leverage graph neural networks to improve signal detection in the IceCube
neutrino observatory. The IceCube detector array is modeled as a graph, where
vertices are sensors and edges are a learned function of the sensors' spatial
coordinates. As only a subset of IceCube's sensors is active during a given
observation, we note the adaptive nature of our GNN, wherein computation is
restricted to the input signal support. We demonstrate the effectiveness of our
GNN architecture on a task classifying IceCube events, where it outperforms
both a traditional physics-based method as well as classical 3D convolution
neural networks.
Periocular Recognition Using CNN Features Off-the-Shelf
Authors: Kevin Hernandez-Diaz, Fernando Alonso-Fernandez, Josef Bigun
Date: 2018-09-17 00:00:00
Tasks: Object Recognition

Periocular refers to the region around the eye, including sclera, eyelids,
lashes, brows and skin. With a surprisingly high discrimination ability, it is
the ocular modality requiring the least constrained acquisition. Here, we apply
existing pre-trained architectures, proposed in the context of the ImageNet
Large Scale Visual Recognition Challenge, to the task of periocular
recognition. These have proven to be very successful for many other computer
vision tasks apart from the detection and classification tasks for which they
were designed. Experiments are done with a database of periocular images
captured with a digital camera. We demonstrate that these off-the-shelf CNN
features can effectively recognize individuals based on periocular images,
despite being trained to classify generic objects. Compared against reference
periocular features, they show an EER reduction of up to ~40%, with the fusion
of CNN and traditional features providing additional improvements.
Categorizing Comparative Sentences
Authors: Alexander Panchenko, Alexander Bondarenko, Mirco Franzek, Matthias Hagen, Chris Biemann
Date: 2018-09-17 00:00:00
Proceeding: WS 2019 8
Tasks: Argument Mining, Sentence, Sentence Embeddings

We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., "Python has better NLP libraries than MATLAB" => (Python, better, MATLAB). To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27% of the sentences contain an oriented comparison in the sense of "better" or "worse"). A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85% in our experimental evaluation. The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.
Dynamics Estimation Using Recurrent Neural Network
Authors: Astha Sharma
Date: 2018-09-17 00:00:00

There is a plenty of research going on in field of robotics. One of the most
important task is dynamic estimation of response during motion. One of the main
applications of this research topics is the task of pouring, which is performed
daily and is commonly used while cooking. We present an approach to estimate
response to a sequence of manipulation actions. We are experimenting with
pouring motion and the response is the change of the amount of water in the
pouring cup. The pouring motion is represented by rotation angle and the amount
of water is represented by its weight. We are using recurrent neural networks
for building the neural network model to train on sequences which represents
1307 trails of pouring. The model gives great results on unseen test data which
does not too different with training data in terms of dimensions of the cup
used for pouring and receiving. The loss obtained with this test data is
4.5920. The model does not give good results on generalization experiments when
we provide a test set which has dimensions of the cup very different from those
in training data.
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation
Authors: Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro
Date: 2018-09-17 00:00:00
Tasks: Image Generation

This paper deals with a method for generating realistic labeled masses.
Recently, there have been many attempts to apply deep learning to various
bio-image computing fields including computer-aided detection and diagnosis. In
order to learn deep network model to be well-behaved in bio-image computing
fields, a lot of labeled data is required. However, in many bioimaging fields,
the large-size of labeled dataset is scarcely available. Although a few
researches have been dedicated to solving this problem through generative
model, there are some problems as follows: 1) The generated bio-image does not
seem realistic; 2) the variation of generated bio-image is limited; and 3)
additional label annotation task is needed. In this study, we propose a
realistic labeled bio-image generation method through visual feature processing
in latent space. Experimental results have shown that mass images generated by
the proposed method were realistic and had wide expression range of targeted
mass characteristics.
Curriculum goal masking for continuous deep reinforcement learning
Authors: Manfred Eppe, Sven Magg, Stefan Wermter
Date: 2018-09-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has recently gained a focus on problems where
policy or value functions are independent of goals. Evidence exists that the
sampling of goals has a strong effect on the learning performance, but there is
a lack of general mechanisms that focus on optimizing the goal sampling
process. In this work, we present a simple and general goal masking method that
also allows us to estimate a goal's difficulty level and thus realize a
curriculum learning approach for deep RL. Our results indicate that focusing on
goals with a medium difficulty level is appropriate for deep deterministic
policy gradient (DDPG) methods, while an "aim for the stars and reach the
moon-strategy", where hard goals are sampled much more often than simple goals,
leads to the best learning performance in cases where DDPG is combined with for
hindsight experience replay (HER). We demonstrate that the approach
significantly outperforms standard goal sampling for different robotic object
manipulation problems.
Open Subtitles Paraphrase Corpus for Six Languages
Authors: Mathias Creutz
Date: 2018-09-17 00:00:00
Proceeding: LREC 2018 5
Tasks: Sentence

This paper accompanies the release of Opusparcus, a new paraphrase corpus for
six European languages: German, English, Finnish, French, Russian, and Swedish.
The corpus consists of paraphrases, that is, pairs of sentences in the same
language that mean approximately the same thing. The paraphrases are extracted
from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV
shows. The informal and colloquial genre that occurs in subtitles makes such
data a very interesting language resource, for instance, from the perspective
of computer assisted language learning. For each target language, the
Opusparcus data have been partitioned into three types of data sets: training,
development and test sets. The training sets are large, consisting of millions
of sentence pairs, and have been compiled automatically, with the help of
probabilistic ranking functions. The development and test sets consist of
sentence pairs that have been checked manually; each set contains approximately
1000 sentence pairs that have been verified to be acceptable paraphrases by two
annotators.
Automatic Electrodes Detection during simultaneous EEG/fMRI acquisition
Authors: Mathis Fleury, Pierre Maurel, Marsel Mano, Elise Bannier, Christian Barillot
Date: 2018-09-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Position, Retrieval

Simultaneous EEG/fMRI acquisition allows to measure brain activity at high
spatial-temporal resolution. The localisation of EEG sources depends on several
parameters including the position of the electrodes on the scalp. The position
of the MR electrodes during its acquisitions is obtained with the use of the
UTE sequence allowing their visualisation. The retrieval of the electrodes
consists in obtaining the volume where the electrodes are located by applying a
sphere detection algorithm. We detect around 90% of electrodes for each
subject, and our UTE-based electrode detection showed an average position error
of 3.7mm for all subjects.
Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition
Authors: Bowen Cheng, Rong Xiao, Yandong Guo, Yuxiao Hu, Jian-Feng Wang, Lei Zhang
Date: 2018-09-17 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection, regression, Transfer Learning

We study in this paper how to initialize the parameters of multinomial
logistic regression (a fully connected layer followed with softmax and cross
entropy loss), which is widely used in deep neural network (DNN) models for
classification problems. As logistic regression is widely known not having a
closed-form solution, it is usually randomly initialized, leading to several
deficiencies especially in transfer learning where all the layers except for
the last task-specific layer are initialized using a pre-trained model. The
deficiencies include slow convergence speed, possibility of stuck in local
minimum, and the risk of over-fitting. To address those deficiencies, we first
study the properties of logistic regression and propose a closed-form
approximate solution named regularized Gaussian classifier (RGC). Then we adopt
this approximate solution to initialize the task-specific linear layer and
demonstrate superior performance over random initialization in terms of both
accuracy and convergence speed on various tasks and datasets. For example, for
image classification, our approach can reduce the training time by 10 times and
achieve 3.2% gain in accuracy for Flickr-style classification. For object
detection, our approach can also be 10 times faster in training for the same
accuracy, or 5% better in terms of mAP for VOC 2007 with slightly longer
training.
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
Authors: Bob D. de Vos, Floris F. Berendsen, Max A. Viergever, Hessam Sokooti, Marius Staring, Ivana Isgum
Date: 2018-09-17 00:00:00
Tasks: Affine Image Registration, Deep Learning, Image Registration, Medical Image Analysis

Image registration, the process of aligning two or more images, is the core
technique of many (semi-)automatic medical image analysis tasks. Recent studies
have shown that deep learning methods, notably convolutional neural networks
(ConvNets), can be used for image registration. Thus far training of ConvNets
for registration was supervised using predefined example registrations.
However, obtaining example registrations is not trivial. To circumvent the need
for predefined examples, and thereby to increase convenience of training
ConvNets for image registration, we propose the Deep Learning Image
Registration (DLIR) framework for \textit{unsupervised} affine and deformable
image registration. In the DLIR framework ConvNets are trained for image
registration by exploiting image similarity analogous to conventional
intensity-based image registration. After a ConvNet has been trained with the
DLIR framework, it can be used to register pairs of unseen images in one shot.
We propose flexible ConvNets designs for affine image registration and for
deformable image registration. By stacking multiple of these ConvNets into a
larger architecture, we are able to perform coarse-to-fine image registration.
We show for registration of cardiac cine MRI and registration of chest CT that
performance of the DLIR framework is comparable to conventional image
registration while being several orders of magnitude faster.
DeepDrum: An Adaptive Conditional Neural Network
Authors: Dimos Makris, Maximos Kaliakatsos-Papakostas, Katia Lida Kermanidis
Date: 2018-09-17 00:00:00

Considering music as a sequence of events with multiple complex dependencies,
the Long Short-Term Memory (LSTM) architecture has proven very efficient in
learning and reproducing musical styles. However, the generation of rhythms
requires additional information regarding musical structure and accompanying
instruments. In this paper we present DeepDrum, an adaptive Neural Network
capable of generating drum rhythms under constraints imposed by Feed-Forward
(Conditional) Layers which contain musical parameters along with given
instrumentation information (e.g. bass and guitar notes). Results on generated
drum sequences are presented indicating that DeepDrum is effective in producing
rhythms that resemble the learned style, while at the same time conforming to
given constraints that were unknown during the training process.
Span error bound for weighted SVM with applications in hyperparameter selection
Authors: Ioannis Sarafis, Christos Diou, Anastasios Delopoulos
Date: 2018-09-17 00:00:00

Weighted SVM (or fuzzy SVM) is the most widely used SVM variant owning its
effectiveness to the use of instance weights. Proper selection of the instance
weights can lead to increased generalization performance. In this work, we
extend the span error bound theory to weighted SVM and we introduce effective
hyperparameter selection methods for the weighted SVM algorithm. The
significance of the presented work is that enables the application of span
bound and span-rule with weighted SVM. The span bound is an upper bound of the
leave-one-out error that can be calculated using a single trained SVM model.
This is important since leave-one-out error is an almost unbiased estimator of
the test error. Similarly, the span-rule gives the actual value of the
leave-one-out error. Thus, one can apply span bound and span-rule as
computationally lightweight alternatives of leave-one-out procedure for
hyperparameter selection. The main theoretical contributions are: (a) we prove
the necessary and sufficient condition for the existence of the span of a
support vector in weighted SVM; and (b) we prove the extension of span bound
and span-rule to weighted SVM. We experimentally evaluate the span bound and
the span-rule for hyperparameter selection and we compare them with other
methods that are applicable to weighted SVM: the $K$-fold cross-validation and
the ${\xi}-{\alpha}$ bound. Experiments on 14 benchmark data sets and data sets
with importance scores for the training instances show that: (a) the condition
for the existence of span in weighted SVM is satisfied almost always; (b) the
span-rule is the most effective method for weighted SVM hyperparameter
selection; (c) the span-rule is the best predictor of the test error in the
mean square error sense; and (d) the span-rule is efficient and, for certain
problems, it can be calculated faster than $K$-fold cross-validation.
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement Learning
Authors: Amir H. Abdi, Pramit Saha, Praneeth Srungarapu, Sidney Fels
Date: 2018-09-17 00:00:00
Tasks: Deep Reinforcement Learning, Point Tracking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Motor control is a set of time-varying muscle excitations which generate desired motions for a biomechanical system. Muscle excitations cannot be directly measured from live subjects. An alternative approach is to estimate muscle activations using inverse motion-driven simulation. In this article, we propose a deep reinforcement learning method to estimate the muscle excitations in simulated biomechanical systems. Here, we introduce a custom-made reward function which incentivizes faster point-to-point tracking of target motion. Moreover, we deploy two new techniques, namely, episode-based hard update and dual buffer experience replay, to avoid feedback training loops. The proposed method is tested in four simulated 2D and 3D environments with 6 to 24 axial muscles. The results show that the models were able to learn muscle excitations for given motions after nearly 100,000 simulated steps. Moreover, the root mean square error in point-to-point reaching of the target across experiments was less than 1% of the length of the domain of motion. Our reinforcement learning method is far from the conventional dynamic approaches as the muscle control is derived functionally by a set of distributed neurons. This can open paths for neural activity interpretation of this phenomenon.