pwc-archive/papers-with-abstracts huggingface dataset:
Neonatal Seizure Detection using Convolutional Neural Networks
Authors: Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
Date: 2017-09-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the seizure detector. The designed system is evaluated on a
large dataset of continuous unedited multi-channel neonatal EEG totaling 835
hours and comprising of 1389 seizures. The proposed deep architecture, with
sample-level filters, achieves an accuracy that is comparable to the
state-of-the-art SVM-based neonatal seizure detector, which operates on a set
of carefully designed hand-crafted features. The fully convolutional
architecture allows for the localization of EEG waveforms and patterns that
result in high seizure probabilities for further clinical examination.
Beyond SIFT using Binary features for Loop Closure Detection
Authors: Lei Han, Guyue Zhou, Lan Xu, Lu Fang
Date: 2017-09-18 00:00:00
Tasks: Loop Closure Detection

In this paper a binary feature based Loop Closure Detection (LCD) method is
proposed, which for the first time achieves higher precision-recall (PR)
performance compared with state-of-the-art SIFT feature based approaches. The
proposed system originates from our previous work Multi-Index hashing for Loop
closure Detection (MILD), which employs Multi-Index Hashing
(MIH)~\cite{greene1994multi} for Approximate Nearest Neighbor (ANN) search of
binary features. As the accuracy of MILD is limited by repeating textures and
inaccurate image similarity measurement, burstiness handling is introduced to
solve this problem and achieves considerable accuracy improvement.
Additionally, a comprehensive theoretical analysis on MIH used in MILD is
conducted to further explore the potentials of hashing methods for ANN search
of binary features from probabilistic perspective. This analysis provides more
freedom on best parameter choosing in MIH for different application scenarios.
Experiments on popular public datasets show that the proposed approach achieved
the highest accuracy compared with state-of-the-art while running at 30Hz for
databases containing thousands of images.
Analysis of gradient descent methods with non-diminishing, bounded errors
Authors: Arunselvan Ramaswamy, Shalabh Bhatnagar
Date: 2016-04-01 00:00:00

The main aim of this paper is to provide an analysis of gradient descent (GD)
algorithms with gradient errors that do not necessarily vanish, asymptotically.
In particular, sufficient conditions are presented for both stability (almost
sure boundedness of the iterates) and convergence of GD with bounded,
(possibly) non-diminishing gradient errors. In addition to ensuring stability,
such an algorithm is shown to converge to a small neighborhood of the minimum
set, which depends on the gradient errors. It is worth noting that the main
result of this paper can be used to show that GD with asymptotically vanishing
errors indeed converges to the minimum set. The results presented herein are
not only more general when compared to previous results, but our analysis of GD
with errors is new to the literature to the best of our knowledge. Our work
extends the contributions of Mangasarian & Solodov, Bertsekas & Tsitsiklis and
Tadic & Doucet. Using our framework, a simple yet effective implementation of
GD using simultaneous perturbation stochastic approximations (SP SA), with
constant sensitivity parameters, is presented. Another important improvement
over many previous results is that there are no `additional' restrictions
imposed on the step-sizes. In machine learning applications where step-sizes
are related to learning rates, our assumptions, unlike those of other papers,
do not affect these learning rates. Finally, we present experimental results to
validate our theory.
Temporal Action Detection with Structured Segment Networks
Authors: Yue Zhao, Yuanjun Xiong, Li-Min Wang, Zhirong Wu, Xiaoou Tang, Dahua Lin
Date: 2017-04-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Detection, Action Recognition, TAG

Detecting actions in untrimmed videos is an important yet challenging task.
In this paper, we present the structured segment network (SSN), a novel
framework which models the temporal structure of each action instance via a
structured temporal pyramid. On top of the pyramid, we further introduce a
decomposed discriminative model comprising two classifiers, respectively for
classifying actions and determining completeness. This allows the framework to
effectively distinguish positive proposals from background or incomplete ones,
thus leading to both accurate recognition and localization. These components
are integrated into a unified network that can be efficiently trained in an
end-to-end fashion. Additionally, a simple yet effective temporal action
proposal scheme, dubbed temporal actionness grouping (TAG) is devised to
generate high quality action proposals. On two challenging benchmarks, THUMOS14
and ActivityNet, our method remarkably outperforms previous state-of-the-art
methods, demonstrating superior accuracy and strong adaptivity in handling
actions with various temporal structures.
Sequence-to-point learning with neural networks for nonintrusive load monitoring
Authors: Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel Goddard, Charles Sutton
Date: 2016-12-29 00:00:00
Tasks: blind source separation

Energy disaggregation (a.k.a nonintrusive load monitoring, NILM), a
single-channel blind source separation problem, aims to decompose the mains
which records the whole house electricity consumption into appliance-wise
readings. This problem is difficult because it is inherently unidentifiable.
Recent approaches have shown that the identifiability problem could be reduced
by introducing domain knowledge into the model. Deep neural networks have been
shown to be a promising approach for these problems, but sliding windows are
necessary to handle the long sequences which arise in signal processing
problems, which raises issues about how to combine predictions from different
sliding windows. In this paper, we propose sequence-to-point learning, where
the input is a window of the mains and the output is a single point of the
target appliance. We use convolutional neural networks to train the model.
Interestingly, we systematically show that the convolutional neural networks
can inherently learn the signatures of the target appliances, which are
automatically added into the model to reduce the identifiability problem. We
applied the proposed neural network approaches to real-world household energy
data, and show that the methods achieve state-of-the-art performance, improving
two standard error measures by 84% and 92%.
Direct Pose Estimation with a Monocular Camera
Authors: Darius Burschka, Elmar Mair
Date: 2017-09-18 00:00:00
Tasks: Pose Estimation, Position

We present a direct method to calculate a 6DoF pose change of a monocular
camera for mobile navigation. The calculated pose is estimated up to a constant
unknown scale parameter that is kept constant over the entire reconstruction
process. This method allows a direct cal- culation of the metric position and
rotation without any necessity to fuse the information in a probabilistic
approach over longer frame sequence as it is the case in most currently used
VSLAM approaches. The algorithm provides two novel aspects to the field of
monocular navigation. It allows a direct pose estimation without any a-priori
knowledge about the world directly from any two images and it provides a
quality measure for the estimated motion parameters that allows to fuse the
resulting information in Kalman Filters. We present the mathematical
formulation of the approach together with experimental validation on real scene
images.
Minimal Effort Back Propagation for Convolutional Neural Networks
Authors: Bingzhen Wei, Xu sun, Xuancheng Ren, Jingjing Xu
Date: 2017-09-18 00:00:00

As traditional neural network consumes a significant amount of computing
resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet
effective technique to alleviate this problem. In this technique, only a small
subset of the full gradients are computed to update the model parameters. In
this paper we extend this technique into the Convolutional Neural Network(CNN)
to reduce calculation in back propagation, and the surprising results verify
its validity in CNN: only 5\% of the gradients are passed back but the model
still achieves the same effect as the traditional CNN, or even better. We also
show that the top-$k$ selection of gradients leads to a sparse calculation in
back propagation, which may bring significant computational benefits for high
computational complexity of convolution operation in CNN.
StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection
Authors: Sanghyun Woo, Soonmin Hwang, In So Kweon
Date: 2017-09-18 00:00:00

One-stage object detectors such as SSD or YOLO already have shown promising
accuracy with small memory footprint and fast speed. However, it is widely
recognized that one-stage detectors have difficulty in detecting small objects
while they are competitive with two-stage methods on large objects. In this
paper, we investigate how to alleviate this problem starting from the SSD
framework. Due to their pyramidal design, the lower layer that is responsible
for small objects lacks strong semantics(e.g contextual information). We
address this problem by introducing a feature combining module that spreads out
the strong semantics in a top-down manner. Our final model StairNet detector
unifies the multi-scale representations and semantic distribution effectively.
Experiments on PASCAL VOC 2007 and PASCAL VOC 2012 datasets demonstrate that
StairNet significantly improves the weakness of SSD and outperforms the other
state-of-the-art one-stage detectors.
Early prediction of the duration of protests using probabilistic Latent Dirichlet Allocation and Decision Trees
Authors: Satyakama Paul, Madhur Hasija, Tshilidzi Marwala
Date: 2017-09-18 00:00:00

Protests and agitations are an integral part of every democratic civil
society. In recent years, South Africa has seen a large increase in its
protests. The objective of this paper is to provide an early prediction of the
duration of protests from its free flowing English text description. Free
flowing descriptions of the protests help us in capturing its various nuances
such as multiple causes, courses of actions etc. Next we use a combination of
unsupervised learning (topic modeling) and supervised learning (decision trees)
to predict the duration of the protests. Our results show a high degree (close
to 90%) of accuracy in early prediction of the duration of protests.We expect
the work to help police and other security services in planning and managing
their resources in better handling protests in future.
A Capacity Scaling Law for Artificial Neural Networks
Authors: Gerald Friedland, Mario Krell
Date: 2017-08-20 00:00:00
Tasks: Experimental Design

We derive the calculation of two critical numbers predicting the behavior of
perceptron networks. First, we derive the calculation of what we call the
lossless memory (LM) dimension. The LM dimension is a generalization of the
Vapnik--Chervonenkis (VC) dimension that avoids structured data and therefore
provides an upper bound for perfectly fitting almost any training data. Second,
we derive what we call the MacKay (MK) dimension. This limit indicates a 50%
chance of not being able to train a given function. Our derivations are
performed by embedding a neural network into Shannon's communication model
which allows to interpret the two points as capacities measured in bits. We
present a proof and practical experiments that validate our upper bounds with
repeatable experiments using different network configurations, diverse
implementations, varying activation functions, and several learning algorithms.
The bottom line is that the two capacity points scale strictly linear with the
number of weights. Among other practical applications, our result allows to
compare and benchmark different neural network implementations independent of a
concrete learning task. Our results provide insight into the capabilities and
limits of neural networks and generate valuable know how for experimental
design decisions.
Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems
Authors: Bradford Heap, Michael Bain, Wayne Wobcke, Alfred Krzywicki, Susanne Schmeidl
Date: 2017-09-18 00:00:00
Tasks: General Classification, Multi-class Classification, text-classification, Text Classification

The bag-of-words model is a standard representation of text for many linear
classifier learners. In many problem domains, linear classifiers are preferred
over more complex models due to their efficiency, robustness and
interpretability, and the bag-of-words text representation can capture
sufficient information for linear classifiers to make highly accurate
predictions. However in settings where there is a large vocabulary, large
variance in the frequency of terms in the training corpus, many classes and
very short text (e.g., single sentences or document titles) the bag-of-words
representation becomes extremely sparse, and this can reduce the accuracy of
classifiers. A particular issue in such settings is that short texts tend to
contain infrequently occurring or rare terms which lack class-conditional
evidence. In this work we introduce a method for enriching the bag-of-words
model by complementing such rare term information with related terms from both
general and domain-specific Word Vector models. By reducing sparseness in the
bag-of-words models, our enrichment approach achieves improved classification
over several baseline classifiers in a variety of text classification problems.
Our approach is also efficient because it requires no change to the linear
classifier before or during training, since bag-of-words enrichment applies
only to text being classified.
Social Style Characterization from Egocentric Photo-streams
Authors: Maedeh Aghaei, Mariella Dimiccoli, Cristian Canton Ferrer, Petia Radeva
Date: 2017-09-18 00:00:00
Tasks: Diversity, Time Series, Time Series Analysis, Time Series Classification

This paper proposes a system for automatic social pattern characterization
using a wearable photo-camera. The proposed pipeline consists of three major
steps. First, detection of people with whom the camera wearer interacts and,
second, categorization of the detected social interactions into formal and
informal. These two steps act at event-level where each potential social event
is modeled as a multi-dimensional time-series, whose dimensions correspond to a
set of relevant features for each task, and a LSTM network is employed for
time-series classification. In the last step, recurrences of the same person
across the whole set of social interactions are clustered to achieve a
comprehensive understanding of the diversity and frequency of the social
relations of the user. Experiments over a dataset acquired by a user wearing a
photo-camera during a month show promising results on the task of social
pattern characterization from egocentric photo-streams.
Direction-Aware Semi-Dense SLAM
Authors: Julian Straub, Randi Cabezas, John Leonard, John W. Fisher III
Date: 2017-09-18 00:00:00
Tasks: Scene Understanding, Segmentation, Simultaneous Localization and Mapping

To aide simultaneous localization and mapping (SLAM), future perception
systems will incorporate forms of scene understanding. In a step towards fully
integrated probabilistic geometric scene understanding, localization and
mapping we propose the first direction-aware semi-dense SLAM system. It jointly
infers the directional Stata Center World (SCW) segmentation and a surfel-based
semi-dense map while performing real-time camera tracking. The joint SCW map
model connects a scene-wide Bayesian nonparametric Dirichlet Process
von-Mises-Fisher mixture model (DP-vMF) prior on surfel orientations with the
local surfel locations via a conditional random field (CRF). Camera tracking
leverages the SCW segmentation to improve efficiency via guided observation
selection. Results demonstrate improved SLAM accuracy and tracking efficiency
at state of the art performance.
Wide and deep volumetric residual networks for volumetric image classification
Authors: Varun Arvind, Anthony Costa, Marcus Badgeley, Samuel Cho, Eric Oermann
Date: 2017-09-18 00:00:00
Tasks: 3D Object Classification, Classification, General Classification, image-classification, Image Classification, Object

3D shape models that directly classify objects from 3D information have
become more widely implementable. Current state of the art models rely on deep
convolutional and inception models that are resource intensive. Residual neural
networks have been demonstrated to be easier to optimize and do not suffer from
vanishing/exploding gradients observed in deep networks. Here we implement a
residual neural network for 3D object classification of the 3D Princeton
ModelNet dataset. Further, we show that widening network layers dramatically
improves accuracy in shallow residual nets, and residual neural networks
perform comparable to state-of-the-art 3D shape net models, and we show that
widening network layers improves classification accuracy. We provide extensive
training and architecture parameters providing a better understanding of
available network architectures for use in 3D object classification.
Where to Focus: Deep Attention-based Spatially Recurrent Bilinear Networks for Fine-Grained Visual Recognition
Authors: Lin Wu, Yang Wang
Date: 2017-09-18 00:00:00
Tasks: Deep Attention, Fine-Grained Image Classification, Fine-Grained Visual Recognition, image-classification, Image Classification, Person Re-Identification

Fine-grained visual recognition typically depends on modeling subtle
difference from object parts. However, these parts often exhibit dramatic
visual variations such as occlusions, viewpoints, and spatial transformations,
making it hard to detect. In this paper, we present a novel attention-based
model to automatically, selectively and accurately focus on critical object
regions with higher importance against appearance variations. Given an image,
two different Convolutional Neural Networks (CNNs) are constructed, where the
outputs of two CNNs are correlated through bilinear pooling to simultaneously
focus on discriminative regions and extract relevant features. To capture
spatial distributions among the local regions with visual attention, soft
attention based spatial Long-Short Term Memory units (LSTMs) are incorporated
to realize spatially recurrent yet visually selective over local input
patterns. All the above intuitions equip our network with the following novel
model: two-stream CNN layers, bilinear pooling layer, spatial recurrent layer
with location attention are jointly trained via an end-to-end fashion to serve
as the part detector and feature extractor, whereby relevant features are
localized and extracted attentively. We show the significance of our network
against two well-known visual recognition tasks: fine-grained image
classification and person re-identification.
Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence
Authors: Haesol Park, Kyoung Mu Lee
Date: 2017-09-18 00:00:00
Proceeding: ICCV 2017 10
Tasks: Camera Pose Estimation, Deblurring, Pose Estimation, Super-Resolution, Video Deblurring

The conventional methods for estimating camera poses and scene structures
from severely blurry or low resolution images often result in failure. The
off-the-shelf deblurring or super-resolution methods may show visually pleasing
results. However, applying each technique independently before matching is
generally unprofitable because this naive series of procedures ignores the
consistency between images. In this paper, we propose a pioneering unified
framework that solves four problems simultaneously, namely, dense depth
reconstruction, camera pose estimation, super-resolution, and deblurring. By
reflecting a physical imaging process, we formulate a cost minimization problem
and solve it using an alternating optimization technique. The experimental
results on both synthetic and real videos show high-quality depth maps derived
from severely degraded images that contrast the failures of naive multi-view
stereo methods. Our proposed method also produces outstanding deblurred and
super-resolved images unlike the independent application or combination of
conventional video deblurring, super-resolution methods.
Towards Building a Knowledge Base of Monetary Transactions from a News Collection
Authors: Jan R. Benetka, Krisztian Balog, Kjetil Nørvåg
Date: 2017-09-18 00:00:00
Tasks: Articles

We address the problem of extracting structured representations of economic
events from a large corpus of news articles, using a combination of natural
language processing and machine learning techniques. The developed techniques
allow for semi-automatic population of a financial knowledge base, which, in
turn, may be used to support a range of data mining and exploration tasks. The
key challenge we face in this domain is that the same event is often reported
multiple times, with varying correctness of details. We address this challenge
by first collecting all information pertinent to a given event from the entire
corpus, then considering all possible representations of the event, and
finally, using a supervised learning method, to rank these representations by
the associated confidence scores. A main innovative element of our approach is
that it jointly extracts and stores all attributes of the event as a single
representation (quintuple). Using a purpose-built test set we demonstrate that
our supervised learning approach can achieve 25% improvement in F1-score over
baseline methods that consider the earliest, the latest or the most frequent
reporting of the event.
Per-instance Differential Privacy
Authors: Yu-Xiang Wang
Date: 2017-07-24 00:00:00

We consider a refinement of differential privacy --- per instance
differential privacy (pDP), which captures the privacy of a specific individual
with respect to a fixed data set. We show that this is a strict generalization
of the standard DP and inherits all its desirable properties, e.g.,
composition, invariance to side information and closedness to postprocessing,
except that they all hold for every instance separately. When the data is drawn
from a distribution, we show that per-instance DP implies generalization.
Moreover, we provide explicit calculations of the per-instance DP for the
output perturbation on a class of smooth learning problems. The result reveals
an interesting and intuitive fact that an individual has stronger privacy if
he/she has small "leverage score" with respect to the data set and if he/she
can be predicted more accurately using the leave-one-out data set. Our
simulation shows several orders-of-magnitude more favorable privacy and utility
trade-off when we consider the privacy of only the users in the data set. In a
case study on differentially private linear regression, provide a novel
analysis of the One-Posterior-Sample (OPS) estimator and show that when the
data set is well-conditioned it provides $(\epsilon,\delta)$-pDP for any target
individuals and matches the exact lower bound up to a
$1+\tilde{O}(n^{-1}\epsilon^{-2})$ multiplicative factor. We also demonstrate
how we can use a "pDP to DP conversion" step to design AdaOPS which uses
adaptive regularization to achieve the same results with
$(\epsilon,\delta)$-DP.