pwc-archive/papers-with-abstracts huggingface dataset:
Towards Functorial Language-Games
Authors: Jules Hedges, Martha Lewis
Date: 2018-07-20 00:00:00

In categorical compositional semantics of natural language one studies
functors from a category of grammatical derivations (such as a Lambek pregroup)
to a semantic category (such as real vector spaces). We compositionally build
game-theoretic semantics of sentences by taking the semantic category to be the
category whose morphisms are open games. This requires some modifications to
the grammar category to compensate for the failure of open games to form a
compact closed category. We illustrate the theory using simple examples of
Wittgenstein's language-games.
Wireless Multi-Sensor Networks for Smart Cities: A Prototype System with Statistical Data Analysis
Authors: Balázs Csanád Csáji, Zsolt Kemény, Gianfranco Pedone, András Kuti, József Váncza
Date: 2018-07-20 00:00:00
Tasks: Management

As urbanization proceeds at an astonishing rate, cities have to continuously
improve their solutions that affect the safety, health and overall wellbeing of
their residents. Smart city projects worldwide build on advanced sensor,
information and communication technologies to help dealing with issues like air
pollution, waste management, traffic optimization, and energy efficiency. The
paper reports about the prototype of a smart city initiative in Budapest which
applies various sensors installed on the public lighting system and a
cloud-based analytical module. While the installed wireless multi-sensor
network gathers information about a number of stressors, the module integrates
and statistically processes the data. The module can handle inconsistent,
missing and noisy data and can extrapolate the measurements in time and space,
namely, it can create short-term forecasts and smoothed maps, both accompanied
by reliability estimates. The resulting database uses geometric representations
and can serve as an information centre for public services.
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Authors: Santiago Estrada, Sailesh Conjeti, Muneer Ahmad, Nassir Navab, Martin Reuter
Date: 2018-07-20 00:00:00
Tasks: Segmentation, Semantic Segmentation

Increased information sharing through short and long-range skip connections
between layers in fully convolutional networks have demonstrated significant
improvement in performance for semantic segmentation. In this paper, we propose
Competitive Dense Fully Convolutional Networks (CDFNet) by introducing
competitive maxout activations in place of naive feature concatenation for
inducing competition amongst layers. Within CDFNet, we propose two
architectural contributions, namely competitive dense block (CDB) and
competitive unpooling block (CUB) to induce competition at local and global
scales for short and long-range skip connections respectively. This extension
is demonstrated to boost learning of specialized sub-networks targeted at
segmenting specific anatomies, which in turn eases the training of complex
tasks. We present the proof-of-concept on the challenging task of whole body
segmentation in the publicly available VISCERAL benchmark and demonstrate
improved performance over multiple learning and registration based
state-of-the-art methods.
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
Authors: Priyanka Mandikal, K L Navaneet, Mayank Agarwal, R. Venkatesh Babu
Date: 2018-07-20 00:00:00
Tasks: 3D Point Cloud Reconstruction, 3D Reconstruction, Diversity, Point cloud reconstruction, Single-View 3D Reconstruction

3D reconstruction from single view images is an ill-posed problem. Inferring
the hidden regions from self-occluded images is both challenging and ambiguous.
We propose a two-pronged approach to address these issues. To better
incorporate the data prior and generate meaningful reconstructions, we propose
3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first
train a 3D point cloud auto-encoder and then learn a mapping from the 2D image
to the corresponding learnt embedding. To tackle the issue of uncertainty in
the reconstruction, we predict multiple reconstructions that are consistent
with the input view. This is achieved by learning a probablistic latent space
with a novel view-specific diversity loss. Thorough quantitative and
qualitative analysis is performed to highlight the significance of the proposed
approach. We outperform state-of-the-art approaches on the task of single-view
3D reconstruction on both real and synthetic datasets while generating multiple
plausible reconstructions, demonstrating the generalizability and utility of
our approach.
Escaping the Curse of Dimensionality in Similarity Learning: Efficient Frank-Wolfe Algorithm and Generalization Bounds
Authors: Kuan Liu, Aurélien Bellet
Date: 2018-07-20 00:00:00
Tasks: Generalization Bounds, Metric Learning

Similarity and metric learning provides a principled approach to construct a task-specific similarity from weakly supervised data. However, these methods are subject to the curse of dimensionality: as the number of features grows large, poor generalization is to be expected and training becomes intractable due to high computational and memory costs. In this paper, we propose a similarity learning method that can efficiently deal with high-dimensional sparse data. This is achieved through a parameterization of similarity functions by convex combinations of sparse rank-one matrices, together with the use of a greedy approximate Frank-Wolfe algorithm which provides an efficient way to control the number of active features. We show that the convergence rate of the algorithm, as well as its time and memory complexity, are independent of the data dimension. We further provide a theoretical justification of our modeling choices through an analysis of the generalization error, which depends logarithmically on the sparsity of the solution rather than on the number of features. Our experiments on datasets with up to one million features demonstrate the ability of our approach to generalize well despite the high dimensionality as well as its superiority compared to several competing methods.
Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
Authors: Gabriel Maicas, Gerard Snaauw, Andrew P. Bradley, Ian Reid, Gustavo Carneiro
Date: 2018-07-20 00:00:00
Tasks: General Classification, Lesion Detection

There is a heated debate on how to interpret the decisions provided by deep
learning models (DLM), where the main approaches rely on the visualization of
salient regions to interpret the DLM classification process. However, these
approaches generally fail to satisfy three conditions for the problem of lesion
detection from medical images: 1) for images with lesions, all salient regions
should represent lesions, 2) for images containing no lesions, no salient
region should be produced,and 3) lesions are generally small with relatively
smooth borders. We propose a new model-agnostic paradigm to interpret DLM
classification decisions supported by a novel definition of saliency that
incorporates the conditions above. Our model-agnostic 1-class saliency detector
(MASD) is tested on weakly supervised breast lesion detection from DCE-MRI,
achieving state-of-the-art detection accuracy when compared to current
visualization methods.
A Generalized Vector Space Model for Ontology-Based Information Retrieval
Authors: Vuong M. Ngo, Tru H. Cao
Date: 2018-07-20 00:00:00
Tasks: Information Retrieval, Retrieval

Named entities (NE) are objects that are referred to by names such as people,
organizations and locations. Named entities and keywords are important to the
meaning of a document. We propose a generalized vector space model that
combines named entities and keywords. In the model, we take into account
different ontological features of named entities, namely, aliases, classes and
identifiers. Moreover, we use entity classes to represent the latent
information of interrogative words in Wh-queries, which are ignored in
traditional keyword-based searching. We have implemented and tested the
proposed model on a TREC dataset, as presented and discussed in the paper.
Dialectical GAN for SAR Image Translation: From Sentinel-1 to TerraSAR-X
Authors: Dongyang Ao, Corneliu Octavian Dumitru, Gottfried Schwarz, Mihai Datcu
Date: 2018-07-20 00:00:00
Tasks: Generative Adversarial Network, Translation

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in
different electromagnetic spectrum where the human visual system is not
accustomed to. Thus, with more and more SAR applications, the demand for
enhanced high-quality SAR images has increased considerably. However,
high-quality SAR images entail high costs due to the limitations of current SAR
devices and their image processing resources. To improve the quality of SAR
images and to reduce the costs of their generation, we propose a Dialectical
Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR
images. This method is based on the analysis of hierarchical SAR information
and the "dialectical" structure of GAN frameworks. As a demonstration, a
typical example will be shown where a low-resolution SAR image (e.g., a
Sentinel-1 image) with large ground coverage is translated into a
high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional
algorithms are compared, and a new algorithm is proposed based on a network
framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial
Network - Gradient Penalty) loss functions and Spatial Gram matrices under the
rule of dialectics. Experimental results show that the SAR image translation
works very well when we compare the results of our proposed method with the
selected traditional methods.
Physical Adversarial Examples for Object Detectors
Authors: Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Florian Tramer, Atul Prakash, Tadayoshi Kohno, Dawn Song
Date: 2018-07-20 00:00:00
Tasks: Object, object-detection, Object Detection

Deep neural networks (DNNs) are vulnerable to adversarial
examples-maliciously crafted inputs that cause DNNs to make incorrect
predictions. Recent work has shown that these attacks generalize to the
physical domain, to create perturbations on physical objects that fool image
classifiers under a variety of real-world conditions. Such attacks pose a risk
to deep learning models used in safety-critical cyber-physical systems. In this
work, we extend physical attacks to more challenging object detection models, a
broader class of deep learning algorithms widely used to detect and label
multiple objects within a scene. Improving upon a previous physical attack on
image classifiers, we create perturbed physical objects that are either ignored
or mislabeled by object detection models. We implement a Disappearance Attack,
in which we cause a Stop sign to "disappear" according to the detector-either
by covering thesign with an adversarial Stop sign poster, or by adding
adversarial stickers onto the sign. In a video recorded in a controlled lab
environment, the state-of-the-art YOLOv2 detector failed to recognize these
adversarial Stop signs in over 85% of the video frames. In an outdoor
experiment, YOLO was fooled by the poster and sticker attacks in 72.5% and
63.5% of the video frames respectively. We also use Faster R-CNN, a different
object detection model, to demonstrate the transferability of our adversarial
perturbations. The created poster perturbation is able to fool Faster R-CNN in
85.9% of the video frames in a controlled lab environment, and 40.2% of the
video frames in an outdoor environment. Finally, we present preliminary results
with a new Creation Attack, where in innocuous physical stickers fool a model
into detecting nonexistent objects.
Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
Authors: Joris Guérin, Byron Boots
Date: 2018-07-20 00:00:00
Tasks: Clustering, Image Clustering

For many image clustering problems, replacing raw image data with features
extracted by a pretrained convolutional neural network (CNN), leads to better
clustering performance. However, the specific features extracted, and, by
extension, the selected CNN architecture, can have a major impact on the
clustering results. In practice, this crucial design choice is often decided
arbitrarily due to the impossibility of using cross-validation with
unsupervised learning problems. However, information contained in the different
pretrained CNN architectures may be complementary, even when pretrained on the
same data. To improve clustering performance, we rephrase the image clustering
problem as a multi-view clustering (MVC) problem that considers multiple
different pretrained feature extractors as different "views" of the same data.
We then propose a multi-input neural network architecture that is trained
end-to-end to solve the MVC problem effectively. Our experimental results,
conducted on three different natural image datasets, show that: 1. using
multiple pretrained CNNs jointly as feature extractors improves image
clustering; 2. using an end-to-end approach improves MVC; and 3. combining both
produces state-of-the-art results for the problem of image clustering.
Learning the effect of latent variables in Gaussian Graphical models with unobserved variables
Authors: Marina Vinyes, Guillaume Obozinski
Date: 2018-07-20 00:00:00

The edge structure of the graph defining an undirected graphical model
describes precisely the structure of dependence between the variables in the
graph. In many applications, the dependence structure is unknown and it is
desirable to learn it from data, often because it is a preliminary step to be
able to ascertain causal effects. This problem, known as structure learning, is
hard in general, but for Gaussian graphical models it is slightly easier
because the structure of the graph is given by the sparsity pattern of the
precision matrix of the joint distribution, and because independence coincides
with decorrelation. A major difficulty too often ignored in structure learning
is the fact that if some variables are not observed, the marginal dependence
graph over the observed variables will possibly be significantly more complex
and no longer reflect the direct dependencies that are potentially associated
with causal effects. In this work, we consider a family of latent variable
Gaussian graphical models in which the graph of the joint distribution between
observed and unobserved variables is sparse, and the unobserved variables are
conditionally independent given the others. Prior work was able to recover the
connectivity between observed variables, but could only identify the subspace
spanned by unobserved variables, whereas we propose a convex optimization
formulation based on structured matrix sparsity to estimate the complete
connectivity of the complete graph including unobserved variables, given the
knowledge of the number of missing variables, and a priori knowledge of their
level of connectivity. Our formulation is supported by a theoretical result of
identifiability of the latent dependence structure for sparse graphs in the
infinite data limit. We propose an algorithm leveraging recent active set
methods, which performs well in the experiments on synthetic data.
Twitter Sentiment Analysis System
Authors: Shaunak Joshi, Deepali Deshpande
Date: 2018-07-20 00:00:00
Tasks: General Classification, Sentiment Analysis, Twitter Sentiment Analysis

Social media is increasingly used by humans to express their feelings and
opinions in the form of short text messages. Detecting sentiments in the text
has a wide range of applications including identifying anxiety or depression of
individuals and measuring well-being or mood of a community. Sentiments can be
expressed in many ways that can be seen such as facial expression and gestures,
speech and by written text. Sentiment Analysis in text documents is essentially
a content-based classification problem involving concepts from the domains of
Natural Language Processing as well as Machine Learning. In this paper,
sentiment recognition based on textual data and the techniques used in
sentiment analysis are discussed.
Learning Representations for Soft Skill Matching
Authors: Luiza Sayfullina, Eric Malmi, Juho Kannala
Date: 2018-07-20 00:00:00
Tasks: Binary text classification, text-classification, Text Classification

Employers actively look for talents having not only specific hard skills but
also various soft skills. To analyze the soft skill demands on the job market,
it is important to be able to detect soft skill phrases from job advertisements
automatically. However, a naive matching of soft skill phrases can lead to
false positive matches when a soft skill phrase, such as friendly, is used to
describe a company, a team, or another entity, rather than a desired candidate.
  In this paper, we propose a phrase-matching-based approach which
differentiates between soft skill phrases referring to a candidate vs.
something else. The disambiguation is formulated as a binary text
classification problem where the prediction is made for the potential soft
skill based on the context where it occurs. To inform the model about the soft
skill for which the prediction is made, we develop several approaches,
including soft skill masking and soft skill tagging.
  We compare several neural network based approaches, including CNN, LSTM and
Hierarchical Attention Model. The proposed tagging-based input representation
using LSTM achieved the highest recall of 83.92% on the job dataset when fixing
a precision to 95%.
Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN
Authors: Andrey V. Savchenko
Date: 2018-07-20 00:00:00
Tasks: Clustering, Face Identification, Face Recognition

This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. We propose the two-stage approach, in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. We modified the MobileNet, which is preliminarily trained to perform face recognition, in order to additionally recognize age and gender. In the second stage of our approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The born year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. We experimentally demonstrated that our facial clustering quality is competitive with the state-of-the-art neural networks, though our implementation is much computationally cheaper. Moreover, our approach is characterized by more accurate video-based age/gender recognition when compared to the publicly available models.
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction
Authors: Po-Yu Kao, Thuyen Ngo, Angela Zhang, Jefferson W. Chen, B. S. Manjunath
Date: 2018-07-20 00:00:00
Tasks: Brain Tumor Segmentation, Prediction, Segmentation, Survival Prediction, Tumor Segmentation

This paper introduces a novel methodology to integrate human brain
connectomics and parcellation for brain tumor segmentation and survival
prediction. For segmentation, we utilize an existing brain parcellation atlas
in the MNI152 1mm space and map this parcellation to each individual subject
data. We use deep neural network architectures together with hard negative
mining to achieve the final voxel level classification. For survival
prediction, we present a new method for combining features from connectomics
data, brain parcellation information, and the brain tumor mask. We leverage the
average connectome information from the Human Connectome Project and map each
subject brain volume onto this common connectome space. From this, we compute
tractographic features that describe potential neural disruptions due to the
brain tumor. These features are then used to predict the overall survival of
the subjects. The main novelty in the proposed methods is the use of normalized
brain parcellation data and tractography data from the human connectome project
for analyzing MR images for segmentation and survival prediction. Experimental
results are reported on the BraTS2018 dataset.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Authors: Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood
Date: 2018-07-20 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Inference, Probabilistic Programming

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way. The execution of existing simulators as probabilistic programs enables highly interpretable posterior inference in the structured model defined by the simulator code base. We demonstrate the technique in particle physics, on a scientifically accurate simulation of the tau lepton decay, which is a key ingredient in establishing the properties of the Higgs boson. Inference efficiency is achieved via inference compilation where a deep recurrent neural network is trained to parameterize proposal distributions and control the stochastic simulator in a sequential importance sampling scheme, at a fraction of the computational cost of a Markov chain Monte Carlo baseline.
PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
Authors: Yair Rivenson, Tairan Liu, Zhensong Wei, Yibo Zhang, Aydogan Ozcan
Date: 2018-07-20 00:00:00
Tasks: Generative Adversarial Network, Virtual Staining

Using a deep neural network, we demonstrate a digital staining technique,
which we term PhaseStain, to transform quantitative phase images (QPI) of
labelfree tissue sections into images that are equivalent to brightfield
microscopy images of the same samples that are histochemically stained. Through
pairs of image data (QPI and the corresponding brightfield images, acquired
after staining) we train a generative adversarial network (GAN) and demonstrate
the effectiveness of this virtual staining approach using sections of human
skin, kidney and liver tissue, matching the brightfield microscopy images of
the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's
trichrome stain, respectively. This digital staining framework might further
strengthen various uses of labelfree QPI techniques in pathology applications
and biomedical research in general, by eliminating the need for chemical
staining, reducing sample preparation related costs and saving time. Our
results provide a powerful example of some of the unique opportunities created
by data driven image transformations enabled by deep learning.