pwc-archive/papers-with-abstracts huggingface dataset:
Phylogenetic Convolutional Neural Networks in Metagenomics
Authors: Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello
Date: 2017-09-06 00:00:00
Tasks: Deep Learning, Domain Adaptation, General Classification

Background: Convolutional Neural Networks can be effectively used only when
data are endowed with an intrinsic concept of neighbourhood in the input space,
as is the case of pixels in images. We introduce here Ph-CNN, a novel deep
learning architecture for the classification of metagenomics data based on the
Convolutional Neural Networks, with the patristic distance defined on the
phylogenetic tree being used as the proximity measure. The patristic distance
between variables is used together with a sparsified version of
MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space.
Results: Ph-CNN is tested with a domain adaptation approach on synthetic data
and on a metagenomics collection of gut microbiota of 38 healthy subjects and
222 Inflammatory Bowel Disease patients, divided in 6 subclasses.
Classification performance is promising when compared to classical algorithms
like Support Vector Machines and Random Forest and a baseline fully connected
neural network, e.g. the Multi-Layer Perceptron. Conclusion: Ph-CNN represents
a novel deep learning approach for the classification of metagenomics data.
Operatively, the algorithm has been implemented as a custom Keras layer taking
care of passing to the following convolutional layer not only the data but also
the ranked list of neighbourhood of each sample, thus mimicking the case of
image data, transparently to the user. Keywords: Metagenomics; Deep learning;
Convolutional Neural Networks; Phylogenetic trees
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
Authors: Harshit Gupta, Kyong Hwan Jin, Ha Q. Nguyen, Michael T. McCann, Michael Unser
Date: 2017-09-06 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Image Reconstruction

We present a new method for image reconstruction which replaces the projector
in a projected gradient descent (PGD) with a convolutional neural network
(CNN). CNNs trained as high-dimensional (image-to-image) regressors have
recently been used to efficiently solve inverse problems in imaging. However,
these approaches lack a feedback mechanism to enforce that the reconstructed
image is consistent with the measurements. This is crucial for inverse
problems, and more so in biomedical imaging, where the reconstructions are used
for diagnosis. In our scheme, the gradient descent enforces measurement
consistency, while the CNN recursively projects the solution closer to the
space of desired reconstruction images. We provide a formal framework to ensure
that the classical PGD converges to a local minimizer of a non-convex
constrained least-squares problem. When the projector is replaced with a CNN,
we propose a relaxed PGD, which always converges. Finally, we propose a simple
scheme to train a CNN to act like a projector. Our experiments on sparse view
Computed Tomography (CT) reconstruction for both noiseless and noisy
measurements show an improvement over the total-variation (TV) method and a
recent CNN-based technique.
Cross-Domain Image Retrieval with Attention Modeling
Authors: Xin Ji, Wei Wang, Meihui Zhang, Yang Yang
Date: 2017-09-06 00:00:00
Tasks: Image Retrieval, Retrieval, TAG

With the proliferation of e-commerce websites and the ubiquitousness of smart
phones, cross-domain image retrieval using images taken by smart phones as
queries to search products on e-commerce websites is emerging as a popular
application. One challenge of this task is to locate the attention of both the
query and database images. In particular, database images, e.g. of fashion
products, on e-commerce websites are typically displayed with other
accessories, and the images taken by users contain noisy background and large
variations in orientation and lighting. Consequently, their attention is
difficult to locate. In this paper, we exploit the rich tag information
available on the e-commerce websites to locate the attention of database
images. For query images, we use each candidate image in the database as the
context to locate the query attention. Novel deep convolutional neural network
architectures, namely TagYNet and CtxYNet, are proposed to learn the attention
weights and then extract effective representations of the images. Experimental
results on public datasets confirm that our approaches have significant
improvement over the existing methods in terms of the retrieval accuracy and
efficiency.
Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction
Authors: Jeong-Kyun Lee, Jae-Won Yea, Min-Gyu Park, Kuk-Jin Yoon
Date: 2017-04-25 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Clustering

In this paper, we propose a novel method to jointly solve scene layout
estimation and global registration problems for accurate indoor 3D
reconstruction. Given a sequence of range data, we first build a set of scene
fragments using KinectFusion and register them through pose graph optimization.
Afterwards, we alternate between layout estimation and layout-based global
registration processes in iterative fashion to complement each other. We
extract the scene layout through hierarchical agglomerative clustering and
energy-based multi-model fitting in consideration of noisy measurements. Having
the estimated scene layout in one hand, we register all the range data through
the global iterative closest point algorithm where the positions of 3D points
that belong to the layout such as walls and a ceiling are constrained to be
close to the layout. We experimentally verify the proposed method with the
publicly available synthetic and real-world datasets in both quantitative and
qualitative ways.
Domain-adaptive deep network compression
Authors: Marc Masana, Joost Van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M. Alvarez
Date: 2017-09-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Low-rank compression

Deep Neural Networks trained on large datasets can be easily transferred to
new domains with far fewer labeled examples by a process called fine-tuning.
This has the advantage that representations learned in the large source domain
can be exploited on smaller target domains. However, networks designed to be
optimal for the source task are often prohibitively large for the target task.
In this work we address the compression of networks after domain transfer.
  We focus on compression algorithms based on low-rank matrix decomposition.
Existing methods base compression solely on learned network weights and ignore
the statistics of network activations. We show that domain transfer leads to
large shifts in network activations and that it is desirable to take this into
account when compressing. We demonstrate that considering activation statistics
when compressing weights leads to a rank-constrained regression problem with a
closed-form solution. Because our method takes into account the target domain,
it can more optimally remove the redundancy in the weights. Experiments show
that our Domain Adaptive Low Rank (DALR) method significantly outperforms
existing low-rank compression techniques. With our approach, the fc6 layer of
VGG19 can be compressed more than 4x more than using truncated SVD alone --
with only a minor or no loss in accuracy. When applied to domain-transferred
networks it allows for compression down to only 5-20% of the original number of
parameters with only a minor drop in performance.
Deep Pyramidal Residual Networks
Authors: Dongyoon Han, Jiwhan Kim, Junmo Kim
Date: 2016-10-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, image-classification, Image Classification

Deep convolutional neural networks (DCNNs) have shown remarkable performance
in image classification tasks in recent years. Generally, deep neural network
architectures are stacks consisting of a large number of convolutional layers,
and they perform downsampling along the spatial dimension via pooling to reduce
memory usage. Concurrently, the feature map dimension (i.e., the number of
channels) is sharply increased at downsampling locations, which is essential to
ensure effective performance because it increases the diversity of high-level
attributes. This also applies to residual networks and is very closely related
to their performance. In this research, instead of sharply increasing the
feature map dimension at units that perform downsampling, we gradually increase
the feature map dimension at all units to involve as many locations as
possible. This design, which is discussed in depth together with our new
insights, has proven to be an effective means of improving generalization
ability. Furthermore, we propose a novel residual unit capable of further
improving the classification accuracy with our new network architecture.
Experiments on benchmark CIFAR-10, CIFAR-100, and ImageNet datasets have shown
that our network architecture has superior generalization ability compared to
the original residual networks. Code is available at
https://github.com/jhkim89/PyramidNet}
Phase transitions in Restricted Boltzmann Machines with generic priors
Authors: Adriano Barra, Giuseppe Genovese, Peter Sollich, Daniele Tantari
Date: 2016-12-09 00:00:00
Tasks: Retrieval

We study Generalised Restricted Boltzmann Machines with generic priors for
units and weights, interpolating between Boolean and Gaussian variables. We
present a complete analysis of the replica symmetric phase diagram of these
systems, which can be regarded as Generalised Hopfield models. We underline the
role of the retrieval phase for both inference and learning processes and we
show that retrieval is robust for a large class of weight and unit priors,
beyond the standard Hopfield scenario. Furthermore we show how the paramagnetic
phase boundary is directly related to the optimal size of the training set
necessary for good generalisation in a teacher-student scenario of unsupervised
learning.
Predicting Human Intentions from Motion Only: A 2D+3D Fusion Approach
Authors: Andrea Zunino, Jacopo Cavazza, Atesh Koul, Andrea Cavallo, Cristina Becchio, Vittorio Murino
Date: 2016-05-31 00:00:00
Tasks: General Classification

In this paper, we address the new problem of the prediction of human intents.
There is neuro-psychological evidence that actions performed by humans are
anticipated by peculiar motor acts which are discriminant of the type of action
going to be performed afterwards. In other words, an actual intent can be
forecast by looking at the kinematics of the immediately preceding movement. To
prove it in a computational and quantitative manner, we devise a new
experimental setup where, without using contextual information, we predict
human intents all originating from the same motor act. We posit the problem as
a classification task and we introduce a new multi-modal dataset consisting of
a set of motion capture marker 3D data and 2D video sequences, where, by only
analysing very similar movements in both training and test phases, we are able
to predict the underlying intent, i.e., the future, never observed action. We
also present an extensive experimental evaluation as a baseline, customizing
state-of-the-art techniques for either 3D and 2D data analysis. Realizing that
video processing methods lead to inferior performance but show complementary
information with respect to 3D data sequences, we developed a 2D+3D fusion
analysis where we achieve better classification accuracies, attesting the
superiority of the multimodal approach for the context-free prediction of human
intents.
Scene Text Recognition with Sliding Convolutional Character Models
Authors: Fei Yin, Yi-Chao Wu, Xu-Yao Zhang, Cheng-Lin Liu
Date: 2017-09-06 00:00:00
Tasks: Scene Text Recognition

Scene text recognition has attracted great interests from the computer vision
and pattern recognition community in recent years. State-of-the-art methods use
concolutional neural networks (CNNs), recurrent neural networks with long
short-term memory (RNN-LSTM) or the combination of them. In this paper, we
investigate the intrinsic characteristics of text recognition, and inspired by
human cognition mechanisms in reading texts, we propose a scene text
recognition method with character models on convolutional feature map. The
method simultaneously detects and recognizes characters by sliding the text
line image with character models, which are learned end-to-end on text line
images labeled with text transcripts. The character classifier outputs on the
sliding windows are normalized and decoded with Connectionist Temporal
Classification (CTC) based algorithm. Compared to previous methods, our method
has a number of appealing properties: (1) It avoids the difficulty of character
segmentation which hinders the performance of segmentation-based recognition
methods; (2) The model can be trained simply and efficiently because it avoids
gradient vanishing/exploding in training RNN-LSTM based models; (3) It bases on
character models trained free of lexicon, and can recognize unknown words. (4)
The recognition process is highly parallel and enables fast recognition. Our
experiments on several challenging English and Chinese benchmarks, including
the IIIT-5K, SVT, ICDAR03/13 and TRW15 datasets, demonstrate that the proposed
method yields superior or comparable performance to state-of-the-art methods
while the model size is relatively small.
Random Subspace with Trees for Feature Selection Under Memory Constraints
Authors: Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts
Date: 2017-09-04 00:00:00
Tasks: feature selection

Dealing with datasets of very high dimension is a major challenge in machine
learning. In this paper, we consider the problem of feature selection in
applications where the memory is not large enough to contain all features. In
this setting, we propose a novel tree-based feature selection approach that
builds a sequence of randomized trees on small subsamples of variables mixing
both variables already identified as relevant by previous models and variables
randomly selected among the other variables. As our main contribution, we
provide an in-depth theoretical analysis of this method in infinite sample
setting. In particular, we study its soundness with respect to common
definitions of feature relevance and its convergence speed under various
variable dependance scenarios. We also provide some preliminary empirical
results highlighting the potential of the approach.
Detecting animals in African Savanna with UAVs and the crowds
Authors: Nicolas Rey, Michele Volpi, Stéphane Joost, Devis Tuia
Date: 2017-09-06 00:00:00
Tasks: Management

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife
monitoring, with several advantages over traditional field-based methods. They
have readily been used to count birds, marine mammals and large herbivores in
different environments, tasks which are routinely performed through manual
counting in large collections of images. In this paper, we propose a
semi-automatic system able to detect large mammals in semi-arid Savanna. It
relies on an animal-detection system based on machine learning, trained with
crowd-sourced annotations provided by volunteers who manually interpreted
sub-decimeter resolution color images. The system achieves a high recall rate
and a human operator can then eliminate false detections with limited effort.
Our system provides good perspectives for the development of data-driven
management practices in wildlife conservation. It shows that the detection of
large mammals in semi-arid Savanna can be approached by processing data
provided by standard RGB cameras mounted on affordable fixed wings UAVs.
Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients
Authors: Eitam Sheetrit, Nir Nissim, Denis Klimov, Lior Fuchs, Yuval Elovici, Yuval Shahar
Date: 2017-09-06 00:00:00

Sepsis is a condition caused by the body's overwhelming and life-threatening
response to infection, which can lead to tissue damage, organ failure, and
finally death. Common signs and symptoms include fever, increased heart rate,
increased breathing rate, and confusion. Sepsis is difficult to predict,
diagnose, and treat. Patients who develop sepsis have an increased risk of
complications and death and face higher health care costs and longer
hospitalization. Today, sepsis is one of the leading causes of mortality among
populations in intensive care units (ICUs). In this paper, we look at the
problem of early detection of sepsis by using temporal data mining. We focus on
the use of knowledge-based temporal abstraction to create meaningful
interval-based abstractions, and on time-interval mining to discover frequent
interval-based patterns. We used 2,560 cases derived from the MIMIC-III
database. We found that the distribution of the temporal patterns whose
frequency is above 10% discovered in the records of septic patients during the
last 6 and 12 hours before onset of sepsis is significantly different from that
distribution within a similar period, during an equivalent time window during
hospitalization, in the records of non-septic patients. This discovery is
encouraging for the purpose of performing an early diagnosis of sepsis using
the discovered patterns as constructed features.
Optimal Sub-sampling with Influence Functions
Authors: Daniel Ting, Eric Brochu
Date: 2017-09-06 00:00:00
Tasks: regression

Sub-sampling is a common and often effective method to deal with the
computational challenges of large datasets. However, for most statistical
models, there is no well-motivated approach for drawing a non-uniform
subsample. We show that the concept of an asymptotically linear estimator and
the associated influence function leads to optimal sampling procedures for a
wide class of popular models. Furthermore, for linear regression models which
have well-studied procedures for non-uniform sub-sampling, we show our optimal
influence function based method outperforms previous approaches. We empirically
show the improved performance of our method on real datasets.
Blind image deblurring using class-adapted image priors
Authors: Marina Ljubenović, Mário A. T. Figueiredo
Date: 2017-09-06 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind image deblurring (BID) is an ill-posed inverse problem, usually
addressed by imposing prior knowledge on the (unknown) image and on the
blurring filter. Most of the work on BID has focused on natural images, using
image priors based on statistical properties of generic natural images.
However, in many applications, it is known that the image being recovered
belongs to some specific class (e.g., text, face, fingerprints), and exploiting
this knowledge allows obtaining more accurate priors. In this work, we propose
a method where a Gaussian mixture model (GMM) is used to learn a class-adapted
prior, by training on a dataset of clean images of that class. Experiments show
the competitiveness of the proposed method in terms of restoration quality when
dealing with images containing text, faces, or fingerprints. Additionally,
experiments show that the proposed method is able to handle text images at high
noise levels, outperforming state-of-the-art methods specifically designed for
BID of text images.
On the exact relationship between the denoising function and the data distribution
Authors: Heikki Arponen, Matti Herranen, Harri Valpola
Date: 2017-09-06 00:00:00
Tasks: Denoising, valid

We prove an exact relationship between the optimal denoising function and the
data distribution in the case of additive Gaussian noise, showing that
denoising implicitly models the structure of data allowing it to be exploited
in the unsupervised learning of representations. This result generalizes a
known relationship [2], which is valid only in the limit of small corruption
noise.
Embedded Binarized Neural Networks
Authors: Bradley McDanel, Surat Teerapittayanon, H. T. Kung
Date: 2017-09-06 00:00:00

We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing
current binarized neural networks (BNNs) in the literature to perform
feedforward inference efficiently on small embedded devices. We focus on
minimizing the required memory footprint, given that these devices often have
memory as small as tens of kilobytes (KB). Beyond minimizing the memory
required to store weights, as in a BNN, we show that it is essential to
minimize the memory used for temporaries which hold intermediate results
between layers in feedforward inference. To accomplish this, eBNN reorders the
computation of inference while preserving the original BNN structure, and uses
just a single floating-point temporary for the entire neural network. All
intermediate results from a layer are stored as binary values, as opposed to
floating-points used in current BNN implementations, leading to a 32x reduction
in required temporary space. We provide empirical evidence that our proposed
eBNN approach allows efficient inference (10s of ms) on devices with severely
limited memory (10s of KB). For example, eBNN achieves 95\% accuracy on the
MNIST dataset running on an Intel Curie with only 15 KB of usable memory with
an inference runtime of under 50 ms per sample. To ease the development of
applications in embedded contexts, we make our source code available that
allows users to train and discover eBNN models for a learning task at hand,
which fit within the memory constraint of the target device.
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Authors: Shashank Gupta, Sachin Pawar, Nitin Ramrakhiyani, Girish Palshikar, Vasudeva Varma
Date: 2017-09-06 00:00:00
Tasks: Pharmacovigilance

Social media is an useful platform to share health-related information due to
its vast reach. This makes it a good candidate for public-health monitoring
tasks, specifically for pharmacovigilance. We study the problem of extraction
of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from
twitter. Medical information extraction from social media is challenging,
mainly due to short and highly information nature of text, as compared to more
technical and formal medical reports.
  Current methods in ADR mention extraction relies on supervised learning
methods, which suffers from labeled data scarcity problem. The State-of-the-art
method uses deep neural networks, specifically a class of Recurrent Neural
Network (RNN) which are Long-Short-Term-Memory networks (LSTMs)
\cite{hochreiter1997long}. Deep neural networks, due to their large number of
free parameters relies heavily on large annotated corpora for learning the end
task. But in real-world, it is hard to get large labeled data, mainly due to
heavy cost associated with manual annotation. Towards this end, we propose a
novel semi-supervised learning based RNN model, which can leverage unlabeled
data also present in abundance on social media. Through experiments we
demonstrate the effectiveness of our method, achieving state-of-the-art
performance in ADR mention extraction.