pwc-archive/papers-with-abstracts huggingface dataset:
Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
Authors: Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam Harrison, Mohammadhad Bagheri, Ronald Summers
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering, Retrieval, Triplet

Radiologists in their daily work routinely find and annotate significant
abnormalities on a large number of radiology images. Such abnormalities, or
lesions, have collected over years and stored in hospitals' picture archiving
and communication systems. However, they are basically unsorted and lack
semantic annotations like type and location. In this paper, we aim to organize
and explore them by learning a deep feature representation for each lesion. A
large-scale and comprehensive dataset, DeepLesion, is introduced for this task.
DeepLesion contains bounding boxes and size measurements of over 32K lesions.
To model their similarity relationship, we leverage multiple supervision
information including types, self-supervised location coordinates and sizes.
They require little manual annotation effort but describe useful attributes of
the lesions. Then, a triplet network is utilized to learn lesion embeddings
with a sequential sampling strategy to depict their hierarchical similarity
structure. Experiments show promising qualitative and quantitative results on
lesion retrieval, clustering, and classification. The learned embeddings can be
further employed to build a lesion graph for various clinically useful
applications. We propose algorithms for intra-patient lesion matching and
missing annotation mining. Experimental results validate their effectiveness.
Community Aware Random Walk for Network Embedding
Authors: Mohammad Mehdi Keikha, Maseud Rahgozar, Masoud Asadpour
Date: 2017-10-14 00:00:00
Tasks: General Classification, Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Network Embedding

Social network analysis provides meaningful information about behavior of
network members that can be used for diverse applications such as
classification, link prediction. However, network analysis is computationally
expensive because of feature learning for different applications. In recent
years, many researches have focused on feature learning methods in social
networks. Network embedding represents the network in a lower dimensional
representation space with the same properties which presents a compressed
representation of the network. In this paper, we introduce a novel algorithm
named "CARE" for network embedding that can be used for different types of
networks including weighted, directed and complex. Current methods try to
preserve local neighborhood information of nodes, whereas the proposed method
utilizes local neighborhood and community information of network nodes to cover
both local and global structure of social networks. CARE builds customized
paths, which are consisted of local and global structure of network nodes, as a
basis for network embedding and uses the Skip-gram model to learn
representation vector of nodes. Subsequently, stochastic gradient descent is
applied to optimize our objective function and learn the final representation
of nodes. Our method can be scalable when new nodes are appended to network
without information loss. Parallelize generation of customized random walks is
also used for speeding up CARE. We evaluate the performance of CARE on multi
label classification and link prediction tasks. Experimental results on various
networks indicate that the proposed method outperforms others in both Micro and
Macro-f1 measures for different size of training data.
Multi-task, multi-label and multi-domain learning with residual convolutional networks for emotion recognition
Authors: Gerard Pons, David Masip
Date: 2018-02-19 00:00:00
Tasks: Deep Learning, Emotion Recognition, Multi-Task Learning

Automated emotion recognition in the wild from facial images remains a
challenging problem. Although recent advances in Deep Learning have supposed a
significant breakthrough in this topic, strong changes in pose, orientation and
point of view severely harm current approaches. In addition, the acquisition of
labeled datasets is costly, and current state-of-the-art deep learning
algorithms cannot model all the aforementioned difficulties. In this paper, we
propose to apply a multi-task learning loss function to share a common feature
representation with other related tasks. Particularly we show that emotion
recognition benefits from jointly learning a model with a detector of facial
Action Units (collective muscle movements). The proposed loss function
addresses the problem of learning multiple tasks with heterogeneously labeled
data, improving previous multi-task approaches. We validate the proposal using
two datasets acquired in non controlled environments, and an application to
predict compound facial emotion expressions.
Simultaneous Compression and Quantization: A Joint Approach for Efficient Unsupervised Hashing
Authors: Tuan Hoang, Thanh-Toan Do, Huu Le, Dang-Khoa Le-Tan, Ngai-Man Cheung
Date: 2018-02-19 00:00:00
Tasks: Image Retrieval, Quantization, Retrieval

For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss. A well-established hashing approach is Iterative Quantization (ITQ), which addresses these two requirements in separate steps. In this paper, we revisit the ITQ approach and propose novel formulations and algorithms to the problem. Specifically, we propose a novel approach, named Simultaneous Compression and Quantization (SCQ), to jointly learn to compress (reduce dimensionality) and binarize input data in a single formulation under strict orthogonal constraint. With this approach, we introduce a loss function and its relaxed version, termed Orthonormal Encoder (OnE) and Orthogonal Encoder (OgE) respectively, which involve challenging binary and orthogonal constraints. We propose to attack the optimization using novel algorithms based on recent advances in cyclic coordinate descent approach. Comprehensive experiments on unsupervised image retrieval demonstrate that our proposed methods consistently outperform other state-of-the-art hashing methods. Notably, our proposed methods outperform recent deep neural networks and GAN based hashing in accuracy, while being very computationally-efficient.
Osteoarthritis Disease Detection System using Self Organizing Maps Method based on Ossa Manus X-Ray
Authors: Putri Kurniasih, Dian Pratiwi
Date: 2018-02-19 00:00:00

Osteoarthritis is a disease found in the world, including in Indonesia. The
purpose of this study was to detect the disease Osteoarthritis using Self
Organizing mapping (SOM), and to know the procedure of artificial intelligence
on the methods of Self Organizing Mapping (SOM). In this system, there are
several stages to preserve to detect disease Osteoarthritis using Self
Organizing maps is the result of photographic images rontgen Ossa Manus normal
and sick with the resolution (150 x 200 pixels) do the repair phase contrast,
the Gray scale, thresholding process, Histogram of process , and do the last
process, where the process of doing training (Training) and testing on images
that have kept the shape data (.text). the conclusion is the result of testing
by using a data image, where 42 of data have 12 Normal image data and image
data 30 sick. On the results of the process of training data there are 8 X-ray
image revealed normal right and 19 data x-ray image of pain expressed is
correct. Then the accuracy on the process of training was 96.42%, and in the
process of testing normal true image 4 obtained revealed Normal, 9 data pain
stated true pain and 1 data imagery hurts stated incorrectly, then the accuracy
gained from the results of testing are 92,8%.
Ensemble computation approach to the Hough transform
Authors: Timur M. Khanipov
Date: 2018-02-19 00:00:00

It is demonstrated that the classical Hough transform with shift-elevation
parametrization of digital straight lines has additive complexity of at most
$\mathcal{O}(n^3 / \log n)$ on a $n\times n$ image. The proof is constructive
and uses ensemble computation approach to build summation circuits. The
proposed method has similarities with the fast Hough transform (FHT) and may be
considered a form of the "divide and conquer" technique. It is based on the
fact that lines with close slopes can be decomposed into common components,
allowing generalization for other pattern families. When applied to FHT
patterns, the algorithm yields exactly the $\Theta(n^2\log n)$ FHT asymptotics
which might suggest that the actual classical Hough transform circuits could
smaller size than $\Theta(n^3/ \log n)$.
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Authors: Linfeng Zhang, Han Wang, Weinan E
Date: 2017-12-10 00:00:00
Tasks: Deep Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A new approach for efficiently exploring the configuration space and
computing the free energy of large atomic and molecular systems is proposed,
motivated by an analogy with reinforcement learning. There are two major
components in this new approach. Like metadynamics, it allows for an efficient
exploration of the configuration space by adding an adaptively computed biasing
potential to the original dynamics. Like deep reinforcement learning, this
biasing potential is trained on the fly using deep neural networks, with data
collected judiciously from the exploration and an uncertainty indicator from
the neural network model playing the role of the reward function.
Parameterization using neural networks makes it feasible to handle cases with a
large set of collective variables. This has the potential advantage that
selecting precisely the right set of collective variables has now become less
critical for capturing the structural transformations of the system. The method
is illustrated by studying the full-atom, explicit solvent models of alanine
dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule
with 20 collective variables.
A Survey Of Methods For Explaining Black Box Models
Authors: Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Dino Pedreschi, Fosca Giannotti
Date: 2018-02-06 00:00:00
Tasks: General Classification, Survey

In the last years many accurate decision support systems have been
constructed as black boxes, that is as systems that hide their internal logic
to the user. This lack of explanation constitutes both a practical and an
ethical issue. The literature reports many approaches aimed at overcoming this
crucial weakness sometimes at the cost of scarifying accuracy for
interpretability. The applications in which black box decision systems can be
used are various, and each approach is typically developed to provide a
solution for a specific problem and, as a consequence, delineating explicitly
or implicitly its own definition of interpretability and explanation. The aim
of this paper is to provide a classification of the main problems addressed in
the literature with respect to the notion of explanation and the type of black
box system. Given a problem definition, a black box type, and a desired
explanation this survey should help the researcher to find the proposals more
useful for his own work. The proposed classification of approaches to open
black box models should also be useful for putting the many research open
questions in perspective.
Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement
Authors: Xinyu Lin, Ce Zhu, Yipeng Liu
Date: 2016-04-29 00:00:00
Tasks: Interest Point Detection

Three dimensional (3D) interest point detection plays a fundamental role in
3D computer vision and graphics. In this paper, we introduce a new method for
detecting mesh interest points based on geometric measures and sparse
refinement (GMSR). The key point of our approach is to calculate the 3D
interest point response function using two intuitive and effective geometric
properties of the local surface on a 3D mesh model, namely Euclidean distances
between the neighborhood vertices to the tangent plane of a vertex and the
angles of normal vectors of them. The response function is defined in
multi-scale space and can be utilized to effectively distinguish 3D interest
points from edges and flat areas. Those points with local maximal 3D interest
point response value are selected as the candidates of 3D interest points.
Finally, we utilize an $\ell_0$ norm based optimization method to refine the
candidates of 3D interest points by constraining its quality and quantity.
Numerical experiments demonstrate that our proposed GMSR based 3D interest
point detector outperforms current several state-of-the-art methods for
different kinds of 3D mesh models.
Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space
Authors: Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira, Matteo Salvatori
Date: 2018-02-16 00:00:00
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

In this paper, we study the problem of locating a predefined sequence of
patterns in a time series. In particular, the studied scenario assumes a
theoretical model is available that contains the expected locations of the
patterns. This problem is found in several contexts, and it is commonly solved
by first synthesizing a time series from the model, and then aligning it to the
true time series through dynamic time warping. We propose a technique that
increases the similarity of both time series before aligning them, by mapping
them into a latent correlation space. The mapping is learned from the data
through a machine-learning setup. Experiments on data from non-destructive
testing demonstrate that the proposed approach shows significant improvements
over the state of the art.
Network Unfolding Map by Edge Dynamics Modeling
Authors: Filipe Alves Neto Verri, Paulo Roberto Urio, Liang Zhao
Date: 2016-03-03 00:00:00

The emergence of collective dynamics in neural networks is a mechanism of the
animal and human brain for information processing. In this paper, we develop a
computational technique using distributed processing elements in a complex
network, which are called particles, to solve semi-supervised learning
problems. Three actions govern the particles' dynamics: generation, walking,
and absorption. Labeled vertices generate new particles that compete against
rival particles for edge domination. Active particles randomly walk in the
network until they are absorbed by either a rival vertex or an edge currently
dominated by rival particles. The result from the model evolution consists of
sets of edges arranged by the label dominance. Each set tends to form a
connected subnetwork to represent a data class. Although the intrinsic dynamics
of the model is a stochastic one, we prove there exists a deterministic version
with largely reduced computational complexity; specifically, with linear
growth. Furthermore, the edge domination process corresponds to an unfolding
map in such way that edges "stretch" and "shrink" according to the vertex-edge
dynamics. Consequently, the unfolding effect summarizes the relevant
relationships between vertices and the uncovered data classes. The proposed
model captures important details of connectivity patterns over the vertex-edge
dynamics evolution, in contrast to previous approaches which focused on only
vertex or only edge dynamics. Computer simulations reveal that the new model
can identify nonlinear features in both real and artificial data, including
boundaries between distinct classes and overlapping structures of data.
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
Authors: Jose Bernal, Kaisar Kushibar, Mariano Cabezas, Sergi Valverde, Arnau Oliver, Xavier Lladó
Date: 2018-01-19 00:00:00
Tasks: Brain Segmentation

Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has
attracted the attention of medical doctors and researchers since variations in
tissue volume help in diagnosing and monitoring neurological diseases. Several
proposals have been designed throughout the years comprising conventional
machine learning strategies as well as convolutional neural networks (CNN)
approaches. In particular, in this paper, we analyse a sub-group of deep
learning methods producing dense predictions. This branch, referred in the
literature as Fully CNN (FCNN), is of interest as these architectures can
process an input volume in less time than CNNs and local spatial dependencies
may be encoded since several voxels are classified at once. Our study focuses
on understanding architectural strengths and weaknesses of literature-like
approaches. Hence, we implement eight FCNN architectures inspired by robust
state-of-the-art methods on brain segmentation related tasks. We evaluate them
using the IBSR18, MICCAI2012 and iSeg2017 datasets as they contain infant and
adult data and exhibit varied voxel spacing, image quality, number of scans and
available imaging modalities. The discussion is driven in three directions:
comparison between 2D and 3D approaches, the importance of multiple modalities
and overlapping as a sampling strategy for training and testing models. To
encourage other researchers to explore the evaluation framework, a public
version is accessible to download from our research website.
$A^{4}NT$: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
Authors: Rakshith Shetty, Bernt Schiele, Mario Fritz
Date: 2017-11-06 00:00:00
Tasks: Attribute, Machine Translation, Translation

Text-based analysis methods allow to reveal privacy relevant author
attributes such as gender, age and identify of the text's author. Such methods
can compromise the privacy of an anonymous author even when the author tries to
remove privacy sensitive content. In this paper, we propose an automatic
method, called Adversarial Author Attribute Anonymity Neural Translation
($A^4NT$), to combat such text-based adversaries. We combine
sequence-to-sequence language models used in machine translation and generative
adversarial networks to obfuscate author attributes. Unlike machine translation
techniques which need paired data, our method can be trained on unpaired
corpora of text containing different authors. Importantly, we propose and
evaluate techniques to impose constraints on our $A^4NT$ to preserve the
semantics of the input text. $A^4NT$ learns to make minimal changes to the
input text to successfully fool author attribute classifiers, while aiming to
maintain the meaning of the input. We show through experiments on two different
datasets and three settings that our proposed method is effective in fooling
the author attribute classifiers and thereby improving the anonymity of
authors.
Simple Unsupervised Keyphrase Extraction using Sentence Embeddings
Authors: Kamil Bennani-Smires, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl, Martin Jaggi
Date: 2018-01-13 00:00:00
Proceeding: CONLL 2018 10
Tasks: Diversity, Keyphrase Extraction, Sentence, Sentence Embeddings

Keyphrase extraction is the task of automatically selecting a small set of
phrases that best describe a given free text document. Supervised keyphrase
extraction requires large amounts of labeled training data and generalizes very
poorly outside the domain of the training data. At the same time, unsupervised
systems have poor accuracy, and often do not generalize well, as they require
the input document to belong to a larger corpus also given as input. Addressing
these drawbacks, in this paper, we tackle keyphrase extraction from single
documents with EmbedRank: a novel unsupervised method, that leverages sentence
embeddings. EmbedRank achieves higher F-scores than graph-based state of the
art systems on standard datasets and is suitable for real-time processing of
large amounts of Web data. With EmbedRank, we also explicitly increase coverage
and diversity among the selected keyphrases by introducing an embedding-based
maximal marginal relevance (MMR) for new phrases. A user study including over
200 votes showed that, although reducing the phrases' semantic overlap leads to
no gains in F-score, our high diversity selection is preferred by humans.
Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling
Authors: Quentin Debard, Christian Wolf, Stéphane Canu, Julien Arné
Date: 2018-02-19 00:00:00
Tasks: Gesture Recognition

We propose a fully automatic method for learning gestures on big touch
devices in a potentially multi-user context. The goal is to learn general
models capable of adapting to different gestures, user styles and hardware
variations (e.g. device sizes, sampling frequencies and regularities).
  Based on deep neural networks, our method features a novel dynamic sampling
and temporal normalization component, transforming variable length gestures
into fixed length representations while preserving finger/surface contact
transitions, that is, the topology of the signal. This sequential
representation is then processed with a convolutional model capable, unlike
recurrent networks, of learning hierarchical representations with different
levels of abstraction.
  To demonstrate the interest of the proposed method, we introduce a new touch
gestures dataset with 6591 gestures performed by 27 people, which is, up to our
knowledge, the first of its kind: a publicly available multi-touch gesture
dataset for interaction.
  We also tested our method on a standard dataset of symbolic touch gesture
recognition, the MMG dataset, outperforming the state of the art and reporting
close to perfect performance.
Deep Residual Network for Joint Demosaicing and Super-Resolution
Authors: Ruofan Zhou, Radhakrishna Achanta, Sabine Süsstrunk
Date: 2018-02-19 00:00:00
Tasks: Demosaicking, Image Restoration, SSIM, Super-Resolution

In digital photography, two image restoration tasks have been studied
extensively and resolved independently: demosaicing and super-resolution. Both
these tasks are related to resolution limitations of the camera. Performing
super-resolution on a demosaiced images simply exacerbates the artifacts
introduced by demosaicing. In this paper, we show that such accumulation of
errors can be easily averted by jointly performing demosaicing and
super-resolution. To this end, we propose a deep residual network for learning
an end-to-end mapping between Bayer images and high-resolution images. By
training on high-quality samples, our deep residual demosaicing and
super-resolution network is able to recover high-quality super-resolved images
from low-resolution Bayer mosaics in a single step without producing the
artifacts common to such processing when the two operations are done
separately. We perform extensive experiments to show that our deep residual
network achieves demosaiced and super-resolved images that are superior to the
state-of-the-art both qualitatively and in terms of PSNR and SSIM metrics.
Personalized and Private Peer-to-Peer Machine Learning
Authors: Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, Marc Tommasi
Date: 2017-05-23 00:00:00
Tasks: BIG-bench Machine Learning

The rise of connected personal devices together with privacy concerns call
for machine learning algorithms capable of leveraging the data of a large
number of agents to learn personalized models under strong privacy
requirements. In this paper, we introduce an efficient algorithm to address the
above problem in a fully decentralized (peer-to-peer) and asynchronous fashion,
with provable convergence rate. We show how to make the algorithm
differentially private to protect against the disclosure of information about
the personal datasets, and formally analyze the trade-off between utility and
privacy. Our experiments show that our approach dramatically outperforms
previous work in the non-private case, and that under privacy constraints, we
can significantly improve over models learned in isolation.