pwc-archive/papers-with-abstracts huggingface dataset:
A multi-contrast MRI approach to thalamus segmentation
Authors: Veronica Corona, Jan Lellmann, Peter Nestor, Carola-Bibiane Schoenlieb, Julio Acosta-Cabronero
Date: 2018-07-27 00:00:00
Tasks: Anatomy, Image Segmentation, Segmentation, Semantic Segmentation

Thalamic alterations are relevant to many neurological disorders including
Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine
interventions to improve symptom severity in movement disorders, for example,
often consist of surgery or deep brain stimulation to diencephalic nuclei.
Therefore, accurate delineation of grey matter thalamic subregions is of the
upmost clinical importance. MRI is highly appropriate for structural
segmentation as it provides different views of the anatomy from a single
scanning session. Though with several contrasts potentially available, it is
also of increasing importance to develop new image segmentation techniques that
can operate multi-spectrally. We hereby propose a new segmentation method for
use with multi-modality data, which we evaluated for automated segmentation of
major thalamic subnuclear groups using T1-, T2*-weighted and quantitative
susceptibility mapping (QSM) information. The proposed method consists of four
steps: highly iterative image co-registration, manual segmentation on the
average training-data template, supervised learning for pattern recognition,
and a final convex optimisation step imposing further spatial constraints to
refine the solution. This led to solutions in greater agreement with manual
segmentation than the standard Morel atlas based approach. Furthermore, we show
that the multi-contrast approach boosts segmentation performances. We then
investigated whether prior knowledge using the training-template contours could
further improve convex segmentation accuracy and robustness, which led to
highly precise multi-contrast segmentations in single subjects. This approach
can be extended to most 3D imaging data types and any region of interest
discernible in single scans or multi-subject templates.
False Positive Reduction by Actively Mining Negative Samples for Pulmonary Nodule Detection in Chest Radiographs
Authors: Sejin Park, Woochan Hwang, Kyu Hwan Jung, Joon Beom Seo, Namkug Kim
Date: 2018-07-26 00:00:00
Tasks: Sensitivity

Generating large quantities of quality labeled data in medical imaging is
very time consuming and expensive. The performance of supervised algorithms for
various tasks on imaging has improved drastically over the years, however the
availability of data to train these algorithms have become one of the main
bottlenecks for implementation. To address this, we propose a semi-supervised
learning method where pseudo-negative labels from unlabeled data are used to
further refine the performance of a pulmonary nodule detection network in chest
radiographs. After training with the proposed network, the false positive rate
was reduced to 0.1266 from 0.4864 while maintaining sensitivity at 0.89.
A writer-independent approach for offline signature verification using deep convolutional neural networks features
Authors: Victor L. F. Souza, Adriano L. I. Oliveira, Robert Sabourin
Date: 2018-07-26 00:00:00

The use of features extracted using a deep convolutional neural network (CNN)
combined with a writer-dependent (WD) SVM classifier resulted in significant
improvement in performance of handwritten signature verification (HSV) when
compared to the previous state-of-the-art methods. In this work it is
investigated whether the use of these CNN features provide good results in a
writer-independent (WI) HSV context, based on the dichotomy transformation
combined with the use of an SVM writer-independent classifier. The experiments
performed in the Brazilian and GPDS datasets show that (i) the proposed
approach outperformed other WI-HSV methods from the literature, (ii) in the
global threshold scenario, the proposed approach was able to outperform the
writer-dependent method with CNN features in the Brazilian dataset, (iii) in an
user threshold scenario, the results are similar to those obtained by the
writer-dependent method with CNN features.
High Dimensional Model Representation as a Glass Box in Supervised Machine Learning
Authors: Caleb Deen Bastian, Herschel Rabitz
Date: 2018-07-26 00:00:00
Tasks: BIG-bench Machine Learning

Prediction and explanation are key objects in supervised machine learning,
where predictive models are known as black boxes and explanatory models are
known as glass boxes. Explanation provides the necessary and sufficient
information to interpret the model output in terms of the model input. It
includes assessments of model output dependence on important input variables
and measures of input variable importance to model output. High dimensional
model representation (HDMR), also known as the generalized functional ANOVA
expansion, provides useful insight into the input-output behavior of supervised
machine learning models. This article gives applications of HDMR in supervised
machine learning. The first application is characterizing information leakage
in ``big-data'' settings. The second application is reduced-order
representation of elementary symmetric polynomials. The third application is
analysis of variance with correlated variables. The last application is
estimation of HDMR from kernel machine and decision tree black box
representations. These results suggest HDMR to have broad utility within
machine learning as a glass box representation.
Automatic Processing and Solar Cell Detection in Photovoltaic Electroluminescence Images
Authors: Evgenii Sovetkin, Ansgar Steland
Date: 2018-07-26 00:00:00
Tasks: Cell Detection, Change Detection

Electroluminescence (EL) imaging is a powerful and established technique for
assessing the quality of photovoltaic (PV) modules, which consist of many
electrically connected solar cells arranged in a grid. The analysis of
imperfect real-world images requires reliable methods for preprocessing,
detection and extraction of the cells. We propose several methods for those
tasks, which, however, can be modified to related imaging problems where
similar geometric objects need to be detected accurately. Allowing for images
taken under difficult outdoor conditions, we present methods to correct for
rotation and perspective distortions. The next important step is the extraction
of the solar cells of a PV module, for instance to pass them to a procedure to
detect and analyze defects on their surface. We propose a method based on
specialized Hough transforms, which allows to extract the cells even when the
module is surrounded by disturbing background and a fast method based on
cumulated sums (CUSUM) change detection to extract the cell area of single-cell
mini-module, where the correction of perspective distortion is implicitly done.
The methods are highly automatized to allow for big data analyses. Their
application to a large database of EL images substantiates that the methods
work reliably on a large scale for real-world images. Simulations show that the
approach achieves high accuracy, reliability and robustness. This even holds
for low contrast images as evaluated by comparing the simulated accuracy for a
low and a high contrast image.
Crossbar-aware neural network pruning
Authors: Ling Liang, Lei Deng, Yueling Zeng, Xing Hu, Yu Ji, Xin Ma, Guoqi Li, Yuan Xie
Date: 2018-07-25 00:00:00
Tasks: Network Pruning

Crossbar architecture based devices have been widely adopted in neural
network accelerators by taking advantage of the high efficiency on
vector-matrix multiplication (VMM) operations. However, in the case of
convolutional neural networks (CNNs), the efficiency is compromised
dramatically due to the large amounts of data reuse. Although some mapping
methods have been designed to achieve a balance between the execution
throughput and resource overhead, the resource consumption cost is still huge
while maintaining the throughput.
  Network pruning is a promising and widely studied leverage to shrink the
model size. Whereas, previous work didn`t consider the crossbar architecture
and the corresponding mapping method, which cannot be directly utilized by
crossbar-based neural network accelerators. Tightly combining the crossbar
structure and its mapping, this paper proposes a crossbar-aware pruning
framework based on a formulated L0-norm constrained optimization problem.
Specifically, we design an L0-norm constrained gradient descent (LGD) with
relaxant probabilistic projection (RPP) to solve this problem. Two grains of
sparsity are successfully achieved: i) intuitive crossbar-grain sparsity and
ii) column-grain sparsity with output recombination, based on which we further
propose an input feature maps (FMs) reorder method to improve the model
accuracy. We evaluate our crossbar-aware pruning framework on median-scale
CIFAR10 dataset and large-scale ImageNet dataset with VGG and ResNet models.
Our method is able to reduce the crossbar overhead by 44%-72% with little
accuracy degradation. This work greatly saves the resource and the related
energy cost, which provides a new co-design solution for mapping CNNs onto
various crossbar devices with significantly higher efficiency.
KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
Authors: Wayne Xin Zhao, Gaole He, Hongjian Dou, Jin Huang, Siqi Ouyang, Ji-Rong Wen
Date: 2018-07-30 00:00:00
Tasks: Knowledge-Aware Recommendation, Recommendation Systems

To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB). In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase. Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e. popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset.
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
Authors: Sébastien Bubeck, Nicolò Cesa-Bianchi
Date: 2012-04-25 00:00:00

Multi-armed bandit problems are the most basic examples of sequential
decision problems with an exploration-exploitation trade-off. This is the
balance between staying with the option that gave highest payoffs in the past
and exploring new options that might give higher payoffs in the future.
Although the study of bandit problems dates back to the Thirties,
exploration-exploitation trade-offs arise in several modern applications, such
as ad placement, website optimization, and packet routing. Mathematically, a
multi-armed bandit is defined by the payoff process associated with each
option. In this survey, we focus on two extreme cases in which the analysis of
regret is particularly simple and elegant: i.i.d. payoffs and adversarial
payoffs. Besides the basic setting of finitely many actions, we also analyze
some of the most important variants and extensions, such as the contextual
bandit model.
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
Authors: Vahid Mirjalili, Sebastian Raschka, Arun Ross
Date: 2018-07-31 00:00:00
Tasks: Attribute, Data Augmentation, Diversity

Recent research has proposed the use of Semi Adversarial Networks (SAN) for
imparting privacy to face images. SANs are convolutional autoencoders that
perturb face images such that the perturbed images cannot be reliably used by
an attribute classifier (e.g., a gender classifier) but can still be used by a
face matcher for matching purposes. However, the generalizability of SANs
across multiple arbitrary gender classifiers has not been demonstrated in the
literature. In this work, we tackle the generalization issue by designing an
ensemble SAN model that generates a diverse set of perturbed outputs for a
given input face image. This is accomplished by enforcing diversity among the
individual models in the ensemble through the use of different data
augmentation techniques. The goal is to ensure that at least one of the
perturbed output faces will confound an arbitrary, previously unseen gender
classifier. Extensive experiments using different unseen gender classifiers and
face matchers are performed to demonstrate the efficacy of the proposed
paradigm in imparting gender privacy to face images.
Egocentric Spatial Memory
Authors: Mengmi Zhang, Keng Teck Ma, Shih-Cheng Yen, Joo Hwee Lim, Qi Zhao, Jiashi Feng
Date: 2018-07-31 00:00:00
Tasks: Feature Engineering

Egocentric spatial memory (ESM) defines a memory system with encoding,
storing, recognizing and recalling the spatial information about the
environment from an egocentric perspective. We introduce an integrated deep
neural network architecture for modeling ESM. It learns to estimate the
occupancy state of the world and progressively construct top-down 2D global
maps from egocentric views in a spatially extended environment. During the
exploration, our proposed ESM model updates belief of the global map based on
local observations using a recurrent neural network. It also augments the local
mapping with a novel external memory to encode and store latent representations
of the visited places over long-term exploration in large environments which
enables agents to perform place recognition and hence, loop closure. Our
proposed ESM network contributes in the following aspects: (1) without feature
engineering, our model predicts free space based on egocentric views
efficiently in an end-to-end manner; (2) different from other deep
learning-based mapping system, ESMN deals with continuous actions and states
which is vitally important for robotic control in real applications. In the
experiments, we demonstrate its accurate and robust global mapping capacities
in 3D virtual mazes and realistic indoor environments by comparing with several
competitive baselines.
What am I Searching for: Zero-shot Target Identity Inference in Visual Search
Authors: Mengmi Zhang, Gabriel Kreiman
Date: 2018-07-31 00:00:00

Can we infer intentions from a person's actions? As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior. We conducted two psychophysics experiments where we monitored eye movements while subjects searched for a target object. We defined the fixations falling on non-target objects as "error fixations". Using those error fixations, we developed a model (InferNet) to infer what the target was. InferNet uses a pre-trained convolutional neural network to extract features from the error fixations and computes a similarity map between the error fixations and all locations across the search image. The model consolidates the similarity maps across layers and integrates these maps across all error fixations. InferNet successfully identifies the subject's goal and outperforms competitive null models, even without any object-specific training on the inference task.
End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
Authors: Michael Andrews, Manfred Paulini, Sergei Gleyzer, Barnabas Poczos
Date: 2018-07-31 00:00:00
Tasks: General Classification

This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. We demonstrate the ability of end-to-end classifiers to learn from the angular distribution of the photons recorded as electromagnetic showers, their intrinsic shapes, and the energy of their constituent hits, even when the underlying particles are not fully resolved, delivering a clear advantage in such cases over purely kinematics-based classifiers.
Computing the Strategy to Commit to in Polymatrix Games (Extended Version)
Authors: Giuseppe De Nittis, Alberto Marchesi, Nicola Gatti
Date: 2018-07-31 00:00:00

Leadership games provide a powerful paradigm to model many real-world
settings. Most literature focuses on games with a single follower who acts
optimistically, breaking ties in favour of the leader. Unfortunately, for
real-world applications, this is unlikely. In this paper, we look for
efficiently solvable games with multiple followers who play either
optimistically or pessimistically, i.e., breaking ties in favour or against the
leader. We study the computational complexity of finding or approximating an
optimistic or pessimistic leader-follower equilibrium in specific classes of
succinct games---polymatrix like---which are equivalent to 2-player Bayesian
games with uncertainty over the follower, with interdependent or independent
types. Furthermore, we provide an exact algorithm to find a pessimistic
equilibrium for those game classes. Finally, we show that in general polymatrix
games the computation is harder even when players are forced to play pure
strategies.
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
Authors: Mandy Guo, Qinlan Shen, Yinfei Yang, Heming Ge, Daniel Cer, Gustavo Hernandez Abrego, Keith Stevens, Noah Constant, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
Date: 2018-07-31 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, NMT, Parallel Corpus Mining, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embeddings, Translation

This paper presents an effective approach for parallel corpus mining using
bilingual sentence embeddings. Our embedding models are trained to produce
similar representations exclusively for bilingual sentence pairs that are
translations of each other. This is achieved using a novel training method that
introduces hard negatives consisting of sentences that are not translations but
that have some degree of semantic similarity. The quality of the resulting
embeddings are evaluated on parallel corpus reconstruction and by assessing
machine translation systems trained on gold vs. mined sentence pairs. We find
that the sentence embeddings can be used to reconstruct the United Nations
Parallel Corpus at the sentence level with a precision of 48.9% for en-fr and
54.9% for en-es. When adapted to document level matching, we achieve a parallel
document matching accuracy that is comparable to the significantly more
computationally intensive approach of [Jakob 2010]. Using reconstructed
parallel data, we are able to train NMT models that perform nearly as well as
models trained on the original data (within 1-2 BLEU).
Deep End-to-end Fingerprint Denoising and Inpainting
Authors: Youness Mansar
Date: 2018-07-31 00:00:00
Tasks: Denoising

This work describes our winning solution for the Chalearn LAP In-painting
Competition Track 3 - Fingerprint Denoising and In-painting. The objective of
this competition is to reduce noise, remove the background pattern and replace
missing parts of fingerprint images in order to simplify the verification made
by humans or third-party software. In this paper, we use a U-Net like CNN model
that performs all those steps end-to-end after being trained on the competition
data in a fully supervised way. This architecture and training procedure
achieved the best results on all three metrics of the competition.
Deep Dual Pyramid Network for Barcode Segmentation using Barcode-30k Database
Authors: Qijie Zhao, Feng Ni, Yang song, Yongtao Wang, Zhi Tang
Date: 2018-07-31 00:00:00
Tasks: Segmentation, Semantic Segmentation

Digital signs(such as barcode or QR code) are widely used in our daily life,
and for many applications, we need to localize them on images. However,
difficult cases such as targets with small scales, half-occlusion, shape
deformation and large illumination changes cause challenges for conventional
methods. In this paper, we address this problem by producing a large-scale
dataset and adopting a deep learning based semantic segmentation approach.
Specifically, a synthesizing method was proposed to generate well-annotated
images containing barcode and QR code labels, which contributes to largely
decrease the annotation time. Through the synthesis strategy, we introduce a
dataset that contains 30000 images with Barcode and QR code - Barcode-30k.
Moreover, we further propose a dual pyramid structure based segmentation
network - BarcodeNet, which is mainly formed with two novel modules, Prior
Pyramid Pooling Module(P3M) and Pyramid Refine Module(PRM). We validate the
effectiveness of BarcodeNet on the proposed synthetic dataset, and it yields
the result of mIoU accuracy 95.36\% on validation set. Additional segmentation
results of real images have shown that accurate segmentation performance is
achieved.
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators
Authors: Jie Chen, Ronny Luss
Date: 2018-07-31 00:00:00
Tasks: Stochastic Optimization

Stochastic gradient descent (SGD), which dates back to the 1950s, is one of the most popular and effective approaches for performing stochastic optimization. Research on SGD resurged recently in machine learning for optimizing convex loss functions and training nonconvex deep neural networks. The theory assumes that one can easily compute an unbiased gradient estimator, which is usually the case due to the sample average nature of empirical risk minimization. There exist, however, many scenarios (e.g., graphs) where an unbiased estimator may be as expensive to compute as the full gradient because training examples are interconnected. Recently, Chen et al. (2018) proposed using a consistent gradient estimator as an economic alternative. Encouraged by empirical success, we show, in a general setting, that consistent estimators result in the same convergence behavior as do unbiased ones. Our analysis covers strongly convex, convex, and nonconvex objectives. We verify the results with illustrative experiments on synthetic and real-world data. This work opens several new research directions, including the development of more efficient SGD updates with consistent estimators and the design of efficient training algorithms for large-scale graphs.