pwc-archive/papers-with-abstracts huggingface dataset:
Knowing what you know in brain segmentation using Bayesian deep neural networks
Authors: Patrick McClure, Nao Rho, John A. Lee, Jakub R. Kaczmarzyk, Charles Zheng, Satrajit S. Ghosh, Dylan Nielson, Adam G. Thomas, Peter Bandettini, Francisco Pereira
Date: 2018-12-03 00:00:00
Tasks: Brain Segmentation, Variational Inference

In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours. The network was trained and evaluated on a large dataset (n = 11,480), obtained by combining data from more than a hundred different sites, and also evaluated on another completely held-out dataset (n = 418). The network was trained using a novel spike-and-slab dropout-based variational inference approach. We show that, on these datasets, the proposed Bayesian DNN outperforms previously proposed methods, in terms of the similarity between the segmentation predictions and the FreeSurfer labels, and the usefulness of the estimate uncertainty of these predictions. In particular, we demonstrated that the prediction uncertainty of this network at each voxel is a good indicator of whether the network has made an error and that the uncertainty across the whole brain can predict the manual quality control ratings of a scan. The proposed Bayesian DNN method should be applicable to any new network architecture for addressing the segmentation problem.
Deep Learning Model for Finding New Superconductors
Authors: Tomohiko Konno, Hodaka Kurokawa, Fuyuki Nabeshima, Yuki Sakishita, Ryo Ogawa, Iwao Hosako, Atsutaka Maeda
Date: 2018-12-03 00:00:00
Tasks: Deep Learning, model

Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity. Here, we report the first deep learning model for finding new superconductors. We introduced the method named "reading periodic table" which represented the periodic table in a way that allows deep learning to learn to read the periodic table and to learn the law of elements for the purpose of discovering novel superconductors that are outside the training data. It is recognized that it is difficult for deep learning to predict something outside the training data. Although we used only the chemical composition of materials as information, we obtained an $R^{2}$ value of 0.92 for predicting $T_\text{c}$ for materials in a database of superconductors. We also introduced the method named "garbage-in" to create synthetic data of non-superconductors that do not exist. Non-superconductors are not reported, but the data must be required for deep learning to distinguish between superconductors and non-superconductors. We obtained three remarkable results. The deep learning can predict superconductivity for a material with a precision of 62%, which shows the usefulness of the model; it found the recently discovered superconductor CaBi2 and another one Hf0.5Nb0.2V2Zr0.3, neither of which is in the superconductor database; and it found Fe-based high-temperature superconductors (discovered in 2008) from the training data before 2008. These results open the way for the discovery of new high-temperature superconductor families. The candidate materials list, data, and method are openly available from the link https://github.com/tomo835g/Deep-Learning-to-find-Superconductors.
A Pixel-Based Framework for Data-Driven Clothing
Authors: Ning Jin, Yilin Zhu, Zhenglin Geng, Ronald Fedkiw
Date: 2018-12-03 00:00:00

With the aim of creating virtual cloth deformations more similar to real
world clothing, we propose a new computational framework that recasts three
dimensional cloth deformation as an RGB image in a two dimensional pattern
space. Then a three dimensional animation of cloth is equivalent to a sequence
of two dimensional RGB images, which in turn are driven/choreographed via
animation parameters such as joint angles. This allows us to leverage popular
CNNs to learn cloth deformations in image space. The two dimensional cloth
pixels are extended into the real world via standard body skinning techniques,
after which the RGB values are interpreted as texture offsets and displacement
maps. Notably, we illustrate that our approach does not require accurate
unclothed body shapes or robust skinning techniques. Additionally, we discuss
how standard image based techniques such as image partitioning for higher
resolution, GANs for merging partitioned image regions back together, etc., can
readily be incorporated into our framework.
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies
Authors: Ahmed Ashraf, Shehroz Khan, Nikhil Bhagwat, Mallar Chakravarty, Babak Taati
Date: 2018-12-03 00:00:00
Tasks: Transfer Learning

Medical imaging machine learning algorithms are usually evaluated on a single
dataset. Although training and testing are performed on different subsets of
the dataset, models built on one study show limited capability to generalize to
other studies. While database bias has been recognized as a serious problem in
the computer vision community, it has remained largely unnoticed in medical
imaging research. Transfer learning thus remains confined to the re-use of
feature representations requiring re-training on the new dataset. As a result,
machine learning models do not generalize even when trained on imaging datasets
that were captured to study the same variable of interest. The ability to
transfer knowledge gleaned from one study to another, without the need for
re-training, if possible, would provide reassurance that the models are
learning knowledge fundamental to the problem under study instead of latching
onto the idiosyncracies of a dataset. In this paper, we situate the problem of
dataset bias in the context of medical imaging studies. We show empirical
evidence that such a problem exists in medical datasets. We then present a
framework to unlearn study membership as a means to handle the problem of
database bias. Our main idea is to take the data from the original feature
space to an intermediate space where the data points are indistinguishable in
terms of which study they come from, while maintaining the recognition
capability with respect to the variable of interest. This will promote models
which learn the more general properties of the etiology under study instead of
aligning to dataset-specific peculiarities. Essentially, our proposed model
learns to unlearn the dataset bias.
Towards Accurate Generative Models of Video: A New Metric & Challenges
Authors: Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, Sylvain Gelly
Date: 2018-12-03 00:00:00
Tasks: Diversity, Representation Learning, Starcraft, Video Prediction

Recent advances in deep generative models have lead to remarkable progress in
synthesizing high quality images. Following their successful application in
image processing and representation learning, an important next step is to
consider videos. Learning generative models of video is a much harder task,
requiring a model to capture the temporal dynamics of a scene, in addition to
the visual presentation of objects. While recent attempts at formulating
generative models of video have had some success, current progress is hampered
by (1) the lack of qualitative metrics that consider visual quality, temporal
coherence, and diversity of samples, and (2) the wide gap between purely
synthetic video data sets and challenging real-world data sets in terms of
complexity. To this extent we propose Fr\'{e}chet Video Distance (FVD), a new
metric for generative models of video, and StarCraft 2 Videos (SCV), a
benchmark of game play from custom starcraft 2 scenarios that challenge the
current capabilities of generative models of video. We contribute a large-scale
human study, which confirms that FVD correlates well with qualitative human
judgment of generated videos, and provide initial benchmark results on SCV.
Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence
Authors: Yuji Yoshimura, Bill Cai, Zhoutong Wang, Carlo Ratti
Date: 2018-12-03 00:00:00
Tasks: Clustering, General Classification

This paper applies state-of-the-art techniques in deep learning and computer
vision to measure visual similarities between architectural designs by
different architects. Using a dataset consisting of web scraped images and an
original collection of images of architectural works, we first train a deep
convolutional neural network (DCNN) model capable of achieving 73% accuracy in
classifying works belonging to 34 different architects. Through examining the
weights in the trained DCNN model, we are able to quantitatively measure the
visual similarities between architects that are implicitly learned by our
model. Using this measure, we cluster architects that are identified to be
similar and compare our findings to conventional classification made by
architectural historians and theorists. Our clustering of architectural designs
remarkably corroborates conventional views in architectural history, and the
learned architectural features also coheres with the traditional understanding
of architectural designs.
Automatic hyperparameter selection in Autodock
Authors: Hojjat Rakhshani, Lhassane Idoumghar, Julien Lepagnot, Mathieu Brevilliers, Edward Keedwell
Date: 2018-12-02 00:00:00

Autodock is a widely used molecular modeling tool which predicts how small
molecules bind to a receptor of known 3D structure. The current version of
AutoDock uses meta-heuristic algorithms in combination with local search
methods for doing the conformation search. Appropriate settings of
hyperparameters in these algorithms are important, particularly for novice
users who often find it hard to identify the best configuration. In this work,
we design a surrogate based multi-objective algorithm to help such users by
automatically tuning hyperparameter settings. The proposed method iteratively
uses a radial basis function model and non-dominated sorting to evaluate the
sampled configurations during the search phase. Our experimental results using
Autodock show that the introduced component is practical and effective.
Probabilistic Verification of Fairness Properties via Concentration
Authors: Osbert Bastani, Xin Zhang, Armando Solar-Lezama
Date: 2018-12-02 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a scalable algorithm for verifying fairness specifications. Our algorithm obtains strong correctness guarantees based on adaptive concentration inequalities; such inequalities enable our algorithm to adaptively take samples until it has enough data to make a decision. We implement our algorithm in a tool called VeriFair, and show that it scales to large machine learning models, including a deep recurrent neural network that is more than five orders of magnitude larger than the largest previously-verified neural network. While our technique only gives probabilistic guarantees due to the use of random samples, we show that we can choose the probability of error to be extremely small.
Training for 'Unstable' CNN Accelerator:A Case Study on FPGA
Authors: KouZi Xing
Date: 2018-12-02 00:00:00

With the great advancements of convolution neural networks(CNN), CNN
accelerators are increasingly developed and deployed in the major computing
systems.To make use of the CNN accelerators, CNN models are trained via the
off-line training systems such as Caffe, Pytorch and Tensorflow on multi-core
CPUs and GPUs first and then compiled to the target accelerators. Although the
two-step process seems to be natural and has been widely applied, it assumes
that the accelerators' behavior can be fully modeled on CPUs and GPUs. This
does not hold true and the behavior of the CNN accelerators is un-deterministic
when the circuit works at 'unstable' mode when it is overclocked or is affected
by the environment like fault-prone aerospace. The exact behaviors of the
accelerators are determined by both the chip fabrication and the working
environment or status. In this case, applying the conventional off-line
training result to the accelerators directly may lead to considerable accuracy
loss.
  To address this problem, we propose to train for the 'unstable' CNN
accelerator and have the 'un-determined behavior' learned together with the
data in the same framework. Basically, it starts from the off-line trained
model and then integrates the uncertain circuit behaviors into the CNN models
through additional accelerator-specific training. The fine-tuned training makes
the CNN models less sensitive to the circuit uncertainty. We apply the design
method to both an overclocked CNN accelerator and a faulty accelerator.
According to our experiments on a subset of ImageNet, the accelerator-specific
training can improve the top 5 accuracy up to 3.4% and 2.4% on average when the
CNN accelerator is at extreme overclocking. When the accelerator is exposed to
a faulty environment, the top 5 accuracy improves up to 6.8% and 4.28% on
average under the most severe fault injection.
That's Mine! Learning Ownership Relations and Norms for Robots
Authors: Zhi-Xuan Tan, Jake Brawer, Brian Scassellati
Date: 2018-12-02 00:00:00
Tasks: Bayesian Inference, One-Shot Learning

The ability for autonomous agents to learn and conform to human norms is
crucial for their safety and effectiveness in social environments. While recent
work has led to frameworks for the representation and inference of simple
social rules, research into norm learning remains at an exploratory stage.
Here, we present a robotic system capable of representing, learning, and
inferring ownership relations and norms. Ownership is represented as a graph of
probabilistic relations between objects and their owners, along with a database
of predicate-based norms that constrain the actions permissible on owned
objects. To learn these norms and relations, our system integrates (i) a novel
incremental norm learning algorithm capable of both one-shot learning and
induction from specific examples, (ii) Bayesian inference of ownership
relations in response to apparent rule violations, and (iii) percept-based
prediction of an object's likely owners. Through a series of simulated and
real-world experiments, we demonstrate the competence and flexibility of the
system in performing object manipulation tasks that require a variety of norms
to be followed, laying the groundwork for future research into the acquisition
and application of social norms.
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
Authors: Richard Scalzo, David Kohn, Hugo Olierook, Gregory Houseman, Rohitash Chandra, Mark Girolami, Sally Cripps
Date: 2018-12-02 00:00:00

The rigorous quantification of uncertainty in geophysical inversions is a
challenging problem. Inversions are often ill-posed and the likelihood surface
may be multi-modal; properties of any single mode become inadequate uncertainty
measures, and sampling methods become inefficient for irregular posteriors or
high-dimensional parameter spaces. We explore the influences of different
choices made by the practitioner on the efficiency and accuracy of Bayesian
geophysical inversion methods that rely on Markov chain Monte Carlo sampling to
assess uncertainty, using a multi-sensor inversion of the three-dimensional
structure and composition of a region in the Cooper Basin of South Australia as
a case study. The inversion is performed using an updated version of the
Obsidian distributed inversion software. We find that the posterior for this
inversion has complex local covariance structure, hindering the efficiency of
adaptive sampling methods that adjust the proposal based on the chain history.
Within the context of a parallel-tempered Markov chain Monte Carlo scheme for
exploring high-dimensional multi-modal posteriors, a preconditioned
Crank-Nicholson proposal outperforms more conventional forms of random walk.
Aspects of the problem setup, such as priors on petrophysics or on 3-D
geological structure, affect the shape and separation of posterior modes,
influencing sampling performance as well as the inversion results. Use of
uninformative priors on sensor noise can improve inversion results by enabling
optimal weighting among multiple sensors even if noise levels are uncertain.
Efficiency could be further increased by using posterior gradient information
within proposals, which Obsidian does not currently support, but which could be
emulated using posterior surrogates.
Fuzzy expert system for prediction of prostate cancer
Authors: Juthika Mahanta, Subhasis Panda
Date: 2018-12-01 00:00:00
Tasks: Decision Making, Medical Diagnosis, Prediction

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is
prescribed in this article. Age, prostate-specific antigen (PSA), prostate
volume (PV) and $\%$ Free PSA ($\%$FPSA) are fed as inputs into the FES and
prostate cancer risk (PCR) is obtained as the output. Using knowledge based
rules in Mamdani type inference method the output is calculated. If PCR $\ge
50\%$, then the patient shall be advised to go for a biopsy test for
confirmation. The efficacy of the designed FES is tested against a clinical
data set. The true prediction for all the patients turns out to be $68.91\%$
whereas only for positive biopsy cases it rises to $73.77\%$. This simple yet
effective FES can be used as supportive tool for decision making in medical
diagnosis.
FineFool: Fine Object Contour Attack via Attention
Authors: Jinyin Chen, Haibin Zheng, Hui Xiong, Mengmeng Su
Date: 2018-12-01 00:00:00
Tasks: Adversarial Attack, Object

Machine learning models have been shown vulnerable to adversarial attacks
launched by adversarial examples which are carefully crafted by attacker to
defeat classifiers. Deep learning models cannot escape the attack either. Most
of adversarial attack methods are focused on success rate or perturbations
size, while we are more interested in the relationship between adversarial
perturbation and the image itself. In this paper, we put forward a novel
adversarial attack based on contour, named FineFool. Finefool not only has
better attack performance compared with other state-of-art white-box attacks in
aspect of higher attack success rate and smaller perturbation, but also capable
of visualization the optimal adversarial perturbation via attention on object
contour. To the best of our knowledge, Finefool is for the first time combines
the critical feature of the original clean image with the optimal perturbations
in a visible manner. Inspired by the correlations between adversarial
perturbations and object contour, slighter perturbations is produced via
focusing on object contour features, which is more imperceptible and difficult
to be defended, especially network add-on defense methods with the trade-off
between perturbations filtering and contour feature loss. Compared with
existing state-of-art attacks, extensive experiments are conducted to show that
Finefool is capable of efficient attack against defensive deep models.
Anomaly Detection for Network Connection Logs
Authors: Swapneel Mehta, Prasanth Kothuri, Daniel Lanza Garcia
Date: 2018-12-01 00:00:00
Tasks: Anomaly Detection, Clustering, General Classification

We leverage a streaming architecture based on ELK, Spark and Hadoop in order
to collect, store, and analyse database connection logs in near real-time. The
proposed system investigates outliers using unsupervised learning; widely
adopted clustering and classification algorithms for log data, highlighting the
subtle variances in each model by visualisation of outliers. Arriving at a
novel solution to evaluate untagged, unfiltered connection logs, we propose an
approach that can be extrapolated to a generalised system of analysing
connection logs across a large infrastructure comprising thousands of
individual nodes and generating hundreds of lines in logs per second.
General-to-Detailed GAN for Infrequent Class Medical Images
Authors: Tatsuki Koga, Naoki Nonaka, Jun Sakuma, Jun Seita
Date: 2018-11-28 00:00:00
Tasks: Data Augmentation

Deep learning has significant potential for medical imaging. However, since
the incident rate of each disease varies widely, the frequency of classes in a
medical image dataset is imbalanced, leading to poor accuracy for such
infrequent classes. One possible solution is data augmentation of infrequent
classes using synthesized images created by Generative Adversarial Networks
(GANs), but conventional GANs also require certain amount of images to learn.
To overcome this limitation, here we propose General-to-detailed GAN (GDGAN),
serially connected two GANs, one for general labels and the other for detailed
labels. GDGAN produced diverse medical images, and the network trained with an
augmented dataset outperformed other networks using existing methods with
respect to Area-Under-Curve (AUC) of Receiver Operating Characteristic (ROC)
curve.
Multiview Based 3D Scene Understanding On Partial Point Sets
Authors: Ye Zhu, Sven Ewan Shepstone, Pablo Martínez-Nuevo, Miklas Strøm Kristoffersen, Fabien Moutarde, Zhuang Fu
Date: 2018-11-30 00:00:00
Tasks: 3D Part Segmentation, 3D Shape Recognition, Scene Understanding, Segmentation, Semantic Segmentation

Deep learning within the context of point clouds has gained much research
interest in recent years mostly due to the promising results that have been
achieved on a number of challenging benchmarks, such as 3D shape recognition
and scene semantic segmentation. In many realistic settings however, snapshots
of the environment are often taken from a single view, which only contains a
partial set of the scene due to the field of view restriction of commodity
cameras. 3D scene semantic understanding on partial point clouds is considered
as a challenging task. In this work, we propose a processing approach for 3D
point cloud data based on a multiview representation of the existing 360{\deg}
point clouds. By fusing the original 360{\deg} point clouds and their
corresponding 3D multiview representations as input data, a neural network is
able to recognize partial point sets while improving the general performance on
complete point sets, resulting in an overall increase of 31.9% and 4.3% in
segmentation accuracy for partial and complete scene semantic understanding,
respectively. This method can also be applied in a wider 3D recognition context
such as 3D part segmentation.
EvoMSA: A Multilingual Evolutionary Approach for Sentiment Analysis
Authors: Mario Graff, Sabino Miranda-Jiménez, Eric S. Tellez, Daniela Moctezuma
Date: 2018-11-29 00:00:00
Tasks: Sentiment Analysis

Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain independent and multilingual by processing text using only language-independent techniques. EvoMSA is a classifier, based on Genetic Programming, that works by combining the output of different text classifiers and text models to produce the final prediction. We analyze EvoMSA on different SA competitions to provide a global overview of its performance, and as the results show, EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSA's components to measure their contribution to the performance; the idea is to facilitate a practitioner or newcomer to implement a competitive SA classifier. Finally, it is worth to mention that EvoMSA is available as open-source software.