pwc-archive/papers-with-abstracts huggingface dataset:
Learning to attend in a brain-inspired deep neural network
Authors: Hossein Adeli, Gregory Zelinsky
Date: 2018-11-23 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Recent machine learning models have shown that including attention as a
component results in improved model accuracy and interpretability, despite the
concept of attention in these approaches only loosely approximating the brain's
attention mechanism. Here we extend this work by building a more brain-inspired
deep network model of the primate ATTention Network (ATTNet) that learns to
shift its attention so as to maximize the reward. Using deep reinforcement
learning, ATTNet learned to shift its attention to the visual features of a
target category in the context of a search task. ATTNet's dorsal layers also
learned to prioritize these shifts of attention so as to maximize success of
the ventral pathway classification and receive greater reward. Model behavior
was tested against the fixations made by subjects searching images for the same
cued category. Both subjects and ATTNet showed evidence for attention being
preferentially directed to target goals, behaviorally measured as oculomotor
guidance to targets. More fundamentally, ATTNet learned to shift its attention
to target like objects and spatially route its visual inputs to accomplish the
task. This work makes a step toward a better understanding of the role of
attention in the brain and other computational systems.
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study
Authors: Ahmad S. Tarawneh, Ceyhun Celik, Ahmad B. Hassanat, Dmitry Chetverikov
Date: 2018-11-23 00:00:00
Tasks: Content-Based Image Retrieval, Dimensionality Reduction, Image Retrieval, Retrieval

Research on content-based image retrieval (CBIR) has been under development
for decades, and numerous methods have been competing to extract the most
discriminative features for improved representation of the image content.
Recently, deep learning methods have gained attention in computer vision,
including CBIR. In this paper, we present a comparative investigation of
different features, including low-level and high-level features, for CBIR. We
compare the performance of CBIR systems using different deep features with
state-of-the-art low-level features such as SIFT, SURF, HOG, LBP, and LTP,
using different dictionaries and coefficient learning techniques. Furthermore,
we conduct comparisons with a set of primitive and popular features that have
been used in this field, including colour histograms and Gabor features. We
also investigate the discriminative power of deep features using certain
similarity measures under different validation approaches. Furthermore, we
investigate the effects of the dimensionality reduction of deep features on the
performance of CBIR systems using principal component analysis, discrete
wavelet transform, and discrete cosine transform. Unprecedentedly, the
experimental results demonstrate high (95\% and 93\%) mean average precisions
when using the VGG-16 FC7 deep features of Corel-1000 and Coil-20 datasets with
10-D and 20-D K-SVD, respectively.
Hierarchical visuomotor control of humanoids
Authors: Josh Merel, Arun Ahuja, Vu Pham, Saran Tunyasuvunakool, Si-Qi Liu, Dhruva Tirumala, Nicolas Heess, Greg Wayne
Date: 2018-11-23 00:00:00
Proceeding: ICLR 2019 5

We aim to build complex humanoid agents that integrate perception, motor
control, and memory. In this work, we partly factor this problem into low-level
motor control from proprioception and high-level coordination of the low-level
skills informed by vision. We develop an architecture capable of surprisingly
flexible, task-directed motor control of a relatively high-DoF humanoid body by
combining pre-training of low-level motor controllers with a high-level,
task-focused controller that switches among low-level sub-policies. The
resulting system is able to control a physically-simulated humanoid body to
solve tasks that require coupling visual perception from an unstabilized
egocentric RGB camera during locomotion in the environment. For a supplementary
video link, see https://youtu.be/7GISvfbykLE .
Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
Authors: Hans E. Atlason, Askell Love, Sigurdur Sigurdsson, Vilmundur Gudnason, Lotta M. Ellingsen
Date: 2018-11-23 00:00:00
Tasks: Brain Lesion Segmentation From Mri, Lesion Detection, Lesion Segmentation

Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery
(FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain are
common in the brains of the elderly population and may be caused by ischemia or
demyelination. Lesions are biomarkers for various neurodegenerative diseases,
making accurate quantification of them important for both disease diagnosis and
progression. Automatic lesion detection using supervised learning requires
manually annotated images, which can often be impractical to acquire.
Unsupervised lesion detection, on the other hand, does not require any manual
delineation; however, these methods can be challenging to construct due to the
variability in lesion load, placement of lesions, and voxel intensities. Here
we present a novel approach to address this problem using a convolutional
autoencoder, which learns to segment brain lesions as well as the white matter,
gray matter, and cerebrospinal fluid by reconstructing FLAIR images as conical
combinations of softmax layer outputs generated from the corresponding T1, T2,
and FLAIR images. Some of the advantages of this model are that it accurately
learns to segment lesions regardless of lesion load, and it can be used to
quickly and robustly segment new images that were not in the training set.
Comparisons with state-of-the-art segmentation methods evaluated on ground
truth manual labels indicate that the proposed method works well for generating
accurate lesion segmentations without the need for manual annotations.
A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection
Authors: Hady Ahmady Phoulady, Peter R. Mouton
Date: 2018-11-23 00:00:00
Tasks: Benchmarking, Cervical Nucleus Detection, Deep Learning, General Classification, image-classification, Image Classification

Analyzing Pap cytology slides is an important tasks in detecting and grading
precancerous and cancerous cervical cancer stages. Processing cytology images
usually involve segmenting nuclei and overlapping cells. We introduce a
cervical cytology dataset that can be used to evaluate nucleus detection, as
well as image classification methods in the cytology image processing area.
This dataset contains 93 real image stacks with their grade labels and manually
annotated nuclei within images. We also present two methods: a baseline method
based on a previously proposed approach, and a deep learning method, and
compare their results with other state-of-the-art methods. Both the baseline
method and the deep learning method outperform other state-of-the-art methods
by significant margins. Along with the dataset, we publicly make the evaluation
code and the baseline method available to download for further benchmarking.
A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers
Authors: Jiang Zhang, Yuanqing Xia, Ganghui Shen
Date: 2018-11-23 00:00:00

Autonomous path planning algorithms are significant to planetary exploration
rovers, since relying on commands from Earth will heavily reduce their
efficiency of executing exploration missions. This paper proposes a novel
learning-based algorithm to deal with global path planning problem for
planetary exploration rovers. Specifically, a novel deep convolutional neural
network with double branches (DB-CNN) is designed and trained, which can plan
path directly from orbital images of planetary surfaces without implementing
environment mapping. Moreover, the planning procedure requires no prior
knowledge about planetary surface terrains. Finally, experimental results
demonstrate that DB-CNN achieves better performance on global path planning and
faster convergence during training compared with the existing Value Iteration
Network (VIN).
On Filter Size in Graph Convolutional Networks
Authors: Dinh Van Tran, Nicolò Navarin, Alessandro Sperduti
Date: 2018-11-23 00:00:00

Recently, many researchers have been focusing on the definition of neural
networks for graphs. The basic component for many of these approaches remains
the graph convolution idea proposed almost a decade ago. In this paper, we
extend this basic component, following an intuition derived from the well-known
convolutional filters over multi-dimensional tensors. In particular, we derive
a simple, efficient and effective way to introduce a hyper-parameter on graph
convolutions that influences the filter size, i.e. its receptive field over the
considered graph. We show with experimental results on real-world graph
datasets that the proposed graph convolutional filter improves the predictive
performance of Deep Graph Convolutional Networks.
MR-GAN: Manifold Regularized Generative Adversarial Networks
Authors: Qunwei Li, Bhavya Kailkhura, Rushil Anirudh, Yi Zhou, Yingbin Liang, Pramod Varshney
Date: 2018-11-22 00:00:00

Despite the growing interest in generative adversarial networks (GANs),
training GANs remains a challenging problem, both from a theoretical and a
practical standpoint. To address this challenge, in this paper, we propose a
novel way to exploit the unique geometry of the real data, especially the
manifold information. More specifically, we design a method to regularize GAN
training by adding an additional regularization term referred to as manifold
regularizer. The manifold regularizer forces the generator to respect the
unique geometry of the real data manifold and generate high quality data.
Furthermore, we theoretically prove that the addition of this regularization
term in any class of GANs including DCGAN and Wasserstein GAN leads to improved
performance in terms of generalization, existence of equilibrium, and
stability. Preliminary experiments show that the proposed manifold
regularization helps in avoiding mode collapse and leads to stable training.
A temporal neural network model for object recognition using a biologically plausible decision making layer
Authors: Hamed Heidari Gorji, Sajjad Zabbah, Reza Ebrahimpour
Date: 2018-06-25 00:00:00
Tasks: Decision Making, Object Recognition

Brain can recognize different objects as ones that it has experienced before.
The recognition accuracy and its processing time depend on task properties such
as viewing condition, level of noise and etc. Recognition accuracy can be well
explained by different models. However, less attention has been paid to the
processing time and the ones that do, are not biologically plausible. By
extracting features temporally as well as utilizing an accumulation to bound
decision making model, an object recognition model accounting for both
recognition time and accuracy is proposed. To temporally extract informative
features in support of possible classes of stimuli, a hierarchical spiking
neural network, called spiking HMAX is modified. In the decision making part of
the model the extracted information accumulates over time using accumulator
units. The input category is determined as soon as any of the accumulators
reaches a threshold, called decision bound. Results show that not only does the
model follow human accuracy in a psychophysics task better than the classic
spiking HMAX model, but also it predicts human response time in each choice.
Results provide enough evidence that temporal representation of features are
informative since they can improve the accuracy of a biological plausible
decision maker over time. This is also in line with the well-known idea of
speed accuracy trade-off in decision making studies.
GuacaMol: Benchmarking Models for De Novo Molecular Design
Authors: Nathan Brown, Marco Fiscato, Marwin H. S. Segler, Alain C. Vaucher
Date: 2018-11-22 00:00:00
Tasks: Benchmarking, Drug Discovery, Molecular Property Prediction

De novo design seeks to generate molecules with required property profiles by
virtual design-make-test cycles. With the emergence of deep learning and neural
generative models in many application areas, models for molecular design based
on neural networks appeared recently and show promising results. However, the
new models have not been profiled on consistent tasks, and comparative studies
to well-established algorithms have only seldom been performed.
  To standardize the assessment of both classical and neural models for de novo
molecular design, we propose an evaluation framework, GuacaMol, based on a
suite of standardized benchmarks. The benchmark tasks encompass measuring the
fidelity of the models to reproduce the property distribution of the training
sets, the ability to generate novel molecules, the exploration and exploitation
of chemical space, and a variety of single and multi-objective optimization
tasks. The benchmarking open-source Python code, and a leaderboard can be found
on https://benevolent.ai/guacamol
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
Authors: Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
Date: 2018-11-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Rhythm, Style Transfer

In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness. In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation. We introduce TimbreTron, a method for musical timbre transfer which applies "image" domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer. We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance. Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
Authors: Jesus Arjona Martinez, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant
Date: 2018-10-18 00:00:00

At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm, employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.
Inference of the three-dimensional chromatin structure and its temporal behavior
Authors: Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros, María Rodríguez Martínez, Maria Anna Rapsomaniki
Date: 2018-11-22 00:00:00

Understanding the three-dimensional (3D) structure of the genome is essential
for elucidating vital biological processes and their links to human disease. To
determine how the genome folds within the nucleus, chromosome conformation
capture methods such as HiC have recently been employed. However, computational
methods that exploit the resulting high-throughput, high-resolution data are
still suffering from important limitations. In this work, we explore the idea
of manifold learning for the 3D chromatin structure inference and present a
novel method, REcurrent Autoencoders for CHromatin 3D structure prediction
(REACH-3D). Our framework employs autoencoders with recurrent neural units to
reconstruct the chromatin structure. In comparison to existing methods,
REACH-3D makes no transfer function assumption and permits dynamic analysis.
Evaluating REACH-3D on synthetic data indicated high agreement with the ground
truth. When tested on real experimental HiC data, REACH-3D recovered most
faithfully the expected biological properties and obtained the highest
correlation coefficient with microscopy measurements. Last, REACH-3D was
applied to dynamic HiC data, where it successfully modeled chromatin
conformation during the cell cycle.
ICPRAI 2018 SI: On dynamic ensemble selection and data preprocessing for multi-class imbalance learning
Authors: Rafael M. O. Cruz, Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti
Date: 2018-11-22 00:00:00
Tasks: General Classification

Class-imbalance refers to classification problems in which many more
instances are available for certain classes than for others. Such imbalanced
datasets require special attention because traditional classifiers generally
favor the majority class which has a large number of instances. Ensemble of
classifiers have been reported to yield promising results. However, the
majority of ensemble methods applied to imbalanced learning are static ones.
Moreover, they only deal with binary imbalanced problems. Hence, this paper
presents an empirical analysis of dynamic selection techniques and data
preprocessing methods for dealing with multi-class imbalanced problems. We
considered five variations of preprocessing methods and fourteen dynamic
selection schemes. Our experiments conducted on 26 multi-class imbalanced
problems show that the dynamic ensemble improves the AUC and the G-mean as
compared to the static ensemble. Moreover, data preprocessing plays an
important role in such cases.
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network
Authors: Shenlong Lou, Yan Luo, Qiancong Fan, Feng Chen, Yiping Chen, Cheng Wang, Jonathan Li
Date: 2018-11-22 00:00:00
Tasks: Super-Resolution

It is widely recognized that the deeper networks or networks with more
feature maps have better performance. Existing studies mainly focus on
extending the network depth and increasing the feature maps of networks. At the
same time, horizontal expansion network (e.g. Inception Model) as an
alternative way to improve network performance has not been fully investigated.
Accordingly, we proposed NeuroTreeNet (NTN), as a new horizontal extension
network through the combination of random forest and Inception Model. Based on
the tree structure, in which each branch represents a network and the root node
features are shared to child nodes, network parameters are effectively reduced.
By combining all features of leaf nodes, even less feature maps achieved better
performance. In addition, the relationship between tree structure and the
performance of NTN was investigated in depth. Comparing to other networks (e.g.
VDSR\_5) with equal magnitude parameters, our model showed preferable
performance in super resolution reconstruction task.
Running on Fumes--Preventing Out-of-Gas Vulnerabilities in Ethereum Smart Contracts using Static Resource Analysis
Authors: Elvira Albert, Pablo Gordillo, Albert Rubio, Ilya Sergey
Date: 2018-11-22 00:00:00

Gas is a measurement unit of the computational effort that it will take to execute every single operation that takes part in the Ethereum blockchain platform. Each instruction executed by the Ethereum Virtual Machine (EVM) has an associated gas consumption specified by Ethereum. If a transaction exceeds the amount of gas allotted by the user (known as gas limit), an out-of-gas exception is raised. There is a wide family of contract vulnerabilities due to out-of-gas behaviours. We report on the design and implementation of GASTAP, a Gas-Aware Smart contracT Analysis Platform, which takes as input a smart contract (either in EVM, disassembled EVM, or in Solidity source code) and automatically infers sound gas upper bounds for all its public functions. Our bounds ensure that if the gas limit paid by the user is higher than our inferred gas bounds, the contract is free of out-of-gas vulnerabilities.
Creating a contemporary corpus of similes in Serbian by using natural language processing
Authors: Nikola Milosevic, Goran Nenadic
Date: 2018-11-22 00:00:00

Simile is a figure of speech that compares two things through the use of
connection words, but where comparison is not intended to be taken literally.
They are often used in everyday communication, but they are also a part of
linguistic cultural heritage. In this paper we present a methodology for
semi-automated collection of similes from the World Wide Web using text mining
and machine learning techniques. We expanded an existing corpus by collecting
442 similes from the internet and adding them to the existing corpus collected
by Vuk Stefanovic Karadzic that contained 333 similes. We, also, introduce
crowdsourcing to the collection of figures of speech, which helped us to build
corpus containing 787 unique similes.
Machine learning enables long time scale molecular photodynamics simulations
Authors: Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger, Sebastian Mai, Leticia González, Philipp Marquetand
Date: 2018-11-22 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency

Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy.
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
Authors: Mina Rezaei, Haojin Yang, Christoph Meinel
Date: 2018-11-22 00:00:00
Tasks: Brain Tumor Segmentation, Cardiac Segmentation, Classification, General Classification, Generative Adversarial Network, Segmentation, Semantic Segmentation, Tumor Segmentation

We propose a new generative adversarial architecture to mitigate imbalance
data problem for the task of medical image semantic segmentation where the
majority of pixels belong to a healthy region and few belong to lesion or
non-health region. A model trained with imbalanced data tends to bias towards
healthy data which is not desired in clinical applications. We design a new
conditional GAN with two components: a generative model and a discriminative
model to mitigate imbalanced data problem through selective weighted loss.
While the generator is trained on sequential magnetic resonance images (MRI) to
learn semantic segmentation and disease classification, the discriminator
classifies whether a generated output is real or fake. The proposed
architecture achieved state-of-the-art results on ACDC-2017 for cardiac
segmentation and diseases classification. We have achieved competitive results
on BraTS-2017 for brain tumor segmentation and brain diseases classification.