pwc-archive/papers-with-abstracts huggingface dataset:
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Authors: Ye Liu, Lifang He, Bokai Cao, Philip S. Yu, Ann B. Ragin, Alex D. Leow
Date: 2018-06-19 00:00:00
Tasks: Clustering, Graph Embedding

Network analysis of human brain connectivity is critically important for
understanding brain function and disease states. Embedding a brain network as a
whole graph instance into a meaningful low-dimensional representation can be
used to investigate disease mechanisms and inform therapeutic interventions.
Moreover, by exploiting information from multiple neuroimaging modalities or
views, we are able to obtain an embedding that is more useful than the
embedding learned from an individual view. Therefore, multi-view multi-graph
embedding becomes a crucial task. Currently, only a few studies have been
devoted to this topic, and most of them focus on the vector-based strategy
which will cause structural information contained in the original graphs lost.
As a novel attempt to tackle this problem, we propose Multi-view Multi-graph
Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric
tensors and using tensor techniques to simultaneously leverage the dependencies
and correlations among multi-view and multi-graph brain networks. Extensive
experiments on real HIV and bipolar disorder brain network datasets demonstrate
the superior performance of M2E on clustering brain networks by leveraging the
multi-view multi-graph interactions.
An empirical study on evaluation metrics of generative adversarial networks
Authors: Qiantong Xu, Gao Huang, Yang Yuan, Chuan Guo, Yu Sun, Felix Wu, Kilian Weinberger
Date: 2018-06-19 00:00:00
Proceeding: ICLR 2018 1

Evaluating generative adversarial networks (GANs) is inherently challenging.
In this paper, we revisit several representative sample-based evaluation
metrics for GANs, and address the problem of how to evaluate the evaluation
metrics. We start with a few necessary conditions for metrics to produce
meaningful scores, such as distinguishing real from generated samples,
identifying mode dropping and mode collapsing, and detecting overfitting. With
a series of carefully designed experiments, we comprehensively investigate
existing sample-based metrics and identify their strengths and limitations in
practical settings. Based on these results, we observe that kernel Maximum Mean
Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to
satisfy most of the desirable properties, provided that the distances between
samples are computed in a suitable feature space. Our experiments also unveil
interesting properties about the behavior of several popular GAN models, such
as whether they are memorizing training samples, and how far they are from
learning the target distribution.
Spatio-Temporal Channel Correlation Networks for Action Classification
Authors: Ali Diba, Mohsen Fayyaz, Vivek Sharma, M. Mahdi Arzani, Rahman Yousefzadeh, Juergen Gall, Luc van Gool
Date: 2018-06-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Classification, General Classification

The work in this paper is driven by the question if spatio-temporal
correlations are enough for 3D convolutional neural networks (CNN)? Most of the
traditional 3D networks use local spatio-temporal features. We introduce a new
block that models correlations between channels of a 3D CNN with respect to
temporal and spatial features. This new block can be added as a residual unit
to different parts of 3D CNNs. We name our novel block 'Spatio-Temporal Channel
Correlation' (STC). By embedding this block to the current state-of-the-art
architectures such as ResNext and ResNet, we improved the performance by 2-3\%
on Kinetics dataset. Our experiments show that adding STC blocks to current
state-of-the-art architectures outperforms the state-of-the-art methods on the
HMDB51, UCF101 and Kinetics datasets. The other issue in training 3D CNNs is
about training them from scratch with a huge labeled dataset to get a
reasonable performance. So the knowledge learned in 2D CNNs is completely
ignored. Another contribution in this work is a simple and effective technique
to transfer knowledge from a pre-trained 2D CNN to a randomly initialized 3D
CNN for a stable weight initialization. This allows us to significantly reduce
the number of training samples for 3D CNNs. Thus, by fine-tuning this network,
we beat the performance of generic and recent methods in 3D CNNs, which were
trained on large video datasets, e.g. Sports-1M, and fine-tuned on the target
datasets, e.g. HMDB51/UCF101.
Multimodal feature fusion for CNN-based gait recognition: an empirical comparison
Authors: Francisco Manuel Castro, Manuel Jesús Marín-Jiménez, Nicolás Guil, Nicolás Pérez de la Blanca
Date: 2018-06-19 00:00:00
Tasks: Gait Recognition, Optical Flow Estimation

People identification in video based on the way they walk (i.e. gait) is a relevant task in computer vision using a non-invasive approach. Standard and current approaches typically derive gait signatures from sequences of binary energy maps of subjects extracted from images, but this process introduces a large amount of non-stationary noise, thus, conditioning their efficacy. In contrast, in this paper we focus on the raw pixels, or simple functions derived from them, letting advanced learning techniques to extract relevant features. Therefore, we present a comparative study of different Convolutional Neural Network (CNN) architectures by using three different modalities (i.e. gray pixels, optical flow channels and depth maps) on two widely-adopted and challenging datasets: TUM-GAID and CASIA-B. In addition, we perform a comparative study between different early and late fusion methods used to combine the information obtained from each kind of modalities. Our experimental results suggest that (i) the raw pixel values represent a competitive input modality, compared to the traditional state-of-the-art silhouette-based features (e.g. GEI), since equivalent or better results are obtained; (ii) the fusion of the raw pixel information with information from optical flow and depth maps allows to obtain state-of-the-art results on the gait recognition task with an image resolution several times smaller than the previously reported results; and, (iii) the selection and the design of the CNN architecture are critical points that can make a difference between state-of-the-art results or poor ones.
Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios
Authors: Shabab Bazrafkan, Peter Corcoran
Date: 2018-06-19 00:00:00
Tasks: General Classification, Generative Adversarial Network

Conditional generators learn the data distribution for each class in a
multi-class scenario and generate samples for a specific class given the right
input from the latent space. In this work, a method known as "Versatile
Auxiliary Classifier with Generative Adversarial Network" for multi-class
scenarios is presented. In this technique, the Generative Adversarial Networks
(GAN)'s generator is turned into a conditional generator by placing a
multi-class classifier in parallel with the discriminator network and
backpropagate the classification error through the generator. This technique is
versatile enough to be applied to any GAN implementation. The results on two
databases and comparisons with other method are provided as well.
Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing
Authors: W. Max Lees, Adam Wunderlich, Peter Jeavons, Paul D. Hale, Michael R. Souryal
Date: 2018-06-19 00:00:00
Tasks: BIG-bench Machine Learning, Descriptive, General Classification

In the United States, the Federal Communications Commission has adopted rules
permitting commercial wireless networks to share spectrum with federal
incumbents in the 3.5~GHz Citizens Broadband Radio Service band. These rules
require commercial systems to vacate the band when sensors detect radars
operated by the U.S. military; a key example being the SPN-43 air traffic
control radar. Such sensors require highly-accurate detection algorithms to
meet their operating requirements. In this paper, using a library of over
14,000 3.5~GHz band spectrograms collected by a recent measurement campaign, we
investigate the performance of thirteen methods for SPN-43 radar detection.
Namely, we compare classical methods from signal detection theory and machine
learning to several deep learning architectures. We demonstrate that machine
learning algorithms appreciably outperform classical signal detection methods.
Specifically, we find that a three-layer convolutional neural network offers a
superior tradeoff between accuracy and computational complexity. Last, we apply
this three-layer network to generate descriptive statistics for the full
3.5~GHz spectrogram library. Our findings highlight potential weaknesses of
classical methods and strengths of modern machine learning algorithms for radar
detection in the 3.5~GHz band.
Notes on Abstract Argumentation Theory
Authors: Anthony Peter Young
Date: 2018-06-18 00:00:00
Tasks: Abstract Argumentation

This note reviews Section 2 of Dung's seminal 1995 paper on abstract argumentation theory. In particular, we clarify and make explicit all of the proofs mentioned therein, and provide more examples to illustrate the definitions, with the aim to help readers approaching abstract argumentation theory for the first time. However, we provide minimal commentary and will refer the reader to Dung's paper for the intuitions behind various concepts. The appropriate mathematical prerequisites are provided in the appendices.
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Authors: Nils Strodthoff, Claas Strodthoff
Date: 2018-06-18 00:00:00
Tasks: Specificity

Objective: We aim to provide an algorithm for the detection of myocardial
infarction that operates directly on ECG data without any preprocessing and to
investigate its decision criteria. Approach: We train an ensemble of fully
convolutional neural networks on the PTB ECG dataset and apply state-of-the-art
attribution methods. Main results: Our classifier reaches 93.3% sensitivity and
89.7% specificity evaluated using 10-fold cross-validation with sampling based
on patients. The presented method outperforms state-of-the-art approaches and
reaches the performance level of human cardiologists for detection of
myocardial infarction. We are able to discriminate channel-specific regions
that contribute most significantly to the neural network's decision.
Interestingly, the network's decision is influenced by signs also recognized by
human cardiologists as indicative of myocardial infarction. Significance: Our
results demonstrate the high prospects of algorithmic ECG analysis for future
clinical applications considering both its quantitative performance as well as
the possibility of assessing decision criteria on a per-example basis, which
enhances the comprehensibility of the approach.
A large-scale evaluation framework for EEG deep learning architectures
Authors: Felix A. Heilmeyer, Robin T. Schirrmeister, Lukas D. J. Fiederer, Martin Völker, Joos Behncke, Tonio Ball
Date: 2018-06-18 00:00:00
Tasks: Deep Learning, EEG, Eeg Decoding, Electroencephalogram (EEG)

EEG is the most common signal source for noninvasive BCI applications. For
such applications, the EEG signal needs to be decoded and translated into
appropriate actions. A recently emerging EEG decoding approach is deep learning
with Convolutional or Recurrent Neural Networks (CNNs, RNNs) with many
different architectures already published. Here we present a novel framework
for the large-scale evaluation of different deep-learning architectures on
different EEG datasets. This framework comprises (i) a collection of EEG
datasets currently including 100 examples (recording sessions) from six
different classification problems, (ii) a collection of different EEG decoding
algorithms, and (iii) a wrapper linking the decoders to the data as well as
handling structured documentation of all settings and (hyper-) parameters and
statistics, designed to ensure transparency and reproducibility. As an
applications example we used our framework by comparing three publicly
available CNN architectures: the Braindecode Deep4 ConvNet, Braindecode Shallow
ConvNet, and two versions of EEGNet. We also show how our framework can be used
to study similarities and differences in the performance of different decoding
methods across tasks. We argue that the deep learning EEG framework as
described here could help to tap the full potential of deep learning for BCI
applications.
Learning Treatment Regimens from Electronic Medical Records
Authors: Khanh-Hung Hoang, Tu-Bao Ho
Date: 2018-06-16 00:00:00

Appropriate treatment regimens play a vital role in improving patient health
status. Although some achievements have been made, few of the recent studies of
learning treatment regimens have exploited different kinds of patient
information due to the difficulty in adopting heterogeneous data to many data
mining methods. Moreover, current studies seem too rigid with fixed intervals
of treatment periods corresponding to the varying lengths of hospital stay. To
this end, this work proposes a generic data-driven framework which can derive
group-treatment regimens from electronic medical records by utilizing a
mixed-variate restricted Boltzmann machine and incorporating medical domain
knowledge. We conducted experiments on coronary artery disease as a case study.
The obtained results show that the framework is promising and capable of
assisting physicians in making clinical decisions.
Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets
Authors: Florian Ziel, Rick Steinert, Sven Husmann
Date: 2015-01-05 00:00:00

In our paper we analyze the relationship between the day-ahead electricity
price of the Energy Exchange Austria (EXAA) and other day-ahead electricity
prices in Europe. We focus on markets, which settle their prices after the
EXAA, which enables traders to include the EXAA price into their calculations.
For each market we employ econometric models to incorporate the EXAA price and
compare them with their counterparts without the price of the Austrian
exchange. By employing a forecasting study, we find that electricity price
models can be improved when EXAA prices are considered.
Biogeographical network analysis of plant species distribution in the Mediterranean region
Authors: Maxime Lenormand, Guillaume Papuga, Olivier Argagnon, Maxence Soubeyrand, Guilhem De Barros, Samuel Alleaume, Sandra Luque
Date: 2018-03-06 00:00:00

The delimitation of bioregions helps to understand historical and ecological drivers of species distribution. In this work, we performed a network analysis of the spatial distribution patterns of plants in south of France (Languedoc-Roussillon and Provence-Alpes-C\^ote d'Azur) to analyze the biogeographical structure of the French Mediterranean flora at different scales. We used a network approach to identify and characterize biogeographical regions, based on a large database containing 2.5 million of geolocalized plant records corresponding to more than 3500 plant species. This methodology is performed following five steps, from the biogeographical bipartite network construction, to the identification of biogeographical regions under the form of spatial network communities, the analysis of their interactions and the identification of clusters of plant species based on the species contribution to the biogeographical regions. First, we identified two sub-networks that distinguish Mediterranean and temperate biota. Then, we separated eight statistically significant bioregions that present a complex spatial structure. Some of them are spatially well delimited, and match with particular geological entities. On the other hand fuzzy transitions arise between adjacent bioregions that share a common geological setting, but are spread along a climatic gradient. The proposed network approach illustrates the biogeographical structure of the flora in southern France, and provides precise insights into the relationships between bioregions. This approach sheds light on ecological drivers shaping the distribution of Mediterranean biota: the interplay between a climatic gradient and geological substrate shapes biodiversity patterns. Finally this work exemplifies why fragmented distributions are common in the Mediterranean region, isolating groups of species that share a similar eco-evolutionary history.
Factoring nonnegative matrices with linear programs
Authors: Victor Bittorf, Benjamin Recht, Christopher Re, Joel A. Tropp
Date: 2012-06-06 00:00:00
Proceeding: NeurIPS 2012 12

This paper describes a new approach, based on linear programming, for
computing nonnegative matrix factorizations (NMFs). The key idea is a
data-driven model for the factorization where the most salient features in the
data are used to express the remaining features. More precisely, given a data
matrix X, the algorithm identifies a matrix C such that X approximately equals
CX and some linear constraints. The constraints are chosen to ensure that the
matrix C selects features; these features can then be used to find a low-rank
NMF of X. A theoretical analysis demonstrates that this approach has guarantees
similar to those of the recent NMF algorithm of Arora et al. (2012). In
contrast with this earlier work, the proposed method extends to more general
noise models and leads to efficient, scalable algorithms. Experiments with
synthetic and real datasets provide evidence that the new approach is also
superior in practice. An optimized C++ implementation can factor a
multigigabyte matrix in a matter of minutes.
Learning Instance Segmentation by Interaction
Authors: Deepak Pathak, Yide Shentu, Dian Chen, Pulkit Agrawal, Trevor Darrell, Sergey Levine, Jitendra Malik
Date: 2018-06-21 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

We present an approach for building an active agent that learns to segment
its visual observations into individual objects by interacting with its
environment in a completely self-supervised manner. The agent uses its current
segmentation model to infer pixels that constitute objects and refines the
segmentation model by interacting with these pixels. The model learned from
over 50K interactions generalizes to novel objects and backgrounds. To deal
with noisy training signal for segmenting objects obtained by self-supervised
interactions, we propose robust set loss. A dataset of robot's interactions
along-with a few human labeled examples is provided as a benchmark for future
research. We test the utility of the learned segmentation model by providing
results on a downstream vision-based control task of rearranging multiple
objects into target configurations from visual inputs alone. Videos, code, and
robotic interaction dataset are available at
https://pathak22.github.io/seg-by-interaction/
Quantizing deep convolutional networks for efficient inference: A whitepaper
Authors: Raghuraman Krishnamoorthi
Date: 2018-06-21 00:00:00
Tasks: Quantization

We present an overview of techniques for quantizing convolutional neural
networks for inference with integer weights and activations. Per-channel
quantization of weights and per-layer quantization of activations to 8-bits of
precision post-training produces classification accuracies within 2% of
floating point networks for a wide variety of CNN architectures. Model sizes
can be reduced by a factor of 4 by quantizing weights to 8-bits, even when
8-bit arithmetic is not supported. This can be achieved with simple, post
training quantization of weights.We benchmark latencies of quantized networks
on CPUs and DSPs and observe a speedup of 2x-3x for quantized implementations
compared to floating point on CPUs. Speedups of up to 10x are observed on
specialized processors with fixed point SIMD capabilities, like the Qualcomm
QDSPs with HVX.
  Quantization-aware training can provide further improvements, reducing the
gap to floating point to 1% at 8-bit precision. Quantization-aware training
also allows for reducing the precision of weights to four bits with accuracy
losses ranging from 2% to 10%, with higher accuracy drop for smaller
networks.We introduce tools in TensorFlow and TensorFlowLite for quantizing
convolutional networks and review best practices for quantization-aware
training to obtain high accuracy with quantized weights and activations. We
recommend that per-channel quantization of weights and per-layer quantization
of activations be the preferred quantization scheme for hardware acceleration
and kernel optimization. We also propose that future processors and hardware
accelerators for optimized inference support precisions of 4, 8 and 16 bits.
Interpretable Discovery in Large Image Data Sets
Authors: Kiri L. Wagstaff, Jake Lee
Date: 2018-06-21 00:00:00
Tasks: General Classification, Novelty Detection

Automated detection of new, interesting, unusual, or anomalous images within
large data sets has great value for applications from surveillance (e.g.,
airport security) to science (observations that don't fit a given theory can
lead to new discoveries). Many image data analysis systems are turning to
convolutional neural networks (CNNs) to represent image content due to their
success in achieving high classification accuracy rates. However, CNN
representations are notoriously difficult for humans to interpret. We describe
a new strategy that combines novelty detection with CNN image features to
achieve rapid discovery with interpretable explanations of novel image content.
We applied this technique to familiar images from ImageNet as well as to a
scientific image collection from planetary science.
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?
Authors: Saeed Izadi, Kathleen P. Moriarty, Ghassan Hamarneh
Date: 2018-06-21 00:00:00
Tasks: Deep Learning, Super-Resolution

Confocal laser endomicroscopy (CLE) is a novel imaging modality that provides
in vivo histological cross-sections of examined tissue. Recently, attempts have
been made to develop miniaturized in vivo imaging devices, specifically
confocal laser microscopes, for both clinical and research applications.
However, current implementations of miniature CLE components, such as confocal
lenses, compromise image resolution, signal-to-noise ratio, or both, which
negatively impacts the utility of in vivo imaging. In this work, we demonstrate
that software-based techniques can be used to recover lost information due to
endomicroscopy hardware miniaturization and reconstruct images of higher
resolution. Particularly, a densely connected convolutional neural network is
used to reconstruct a high-resolution CLE image from a low-resolution input. In
the proposed network, each layer is directly connected to all subsequent
layers, which results in an effective combination of low-level and high-level
features and efficient information flow throughout the network. To train and
evaluate our network, we use a dataset of 181 high-resolution CLE images. Both
quantitative and qualitative results indicate superiority of the proposed
network compared to traditional interpolation techniques and competing
learning-based methods. This work demonstrates that software-based
super-resolution is a viable approach to compensate for loss of resolution due
to endoscopic hardware miniaturization.