pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Planning Networks
Authors: Norman Tasfi, Miriam Capretz
Date: 2018-12-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep
reinforcement learning, that combines model-based and model-free aspects for
online planning. Our architecture learns to dynamically construct plans using a
learned state-transition model by selecting and traversing between simulated
states and actions to maximize information before acting. In contrast to
model-free methods, model-based planning lets the agent efficiently test action
hypotheses without performing costly trial-and-error in the environment. DPN
learns to efficiently form plans by expanding a single action-conditional state
transition at a time instead of exhaustively evaluating each action, reducing
the required number of state-transitions during planning by up to 96%. We
observe various emergent planning patterns used to solve environments,
including classical search methods such as breadth-first and depth-first
search. DPN shows improved data efficiency, performance, and generalization to
new and unseen domains in comparison to several baselines.
Kymatio: Scattering Transforms in Python
Authors: Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg
Date: 2018-12-28 00:00:00
Tasks: BIG-bench Machine Learning, CPU, GPU

The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks. All transforms may be executed on a GPU (in addition to CPU), offering a considerable speed up over CPU implementations. The package also has a small memory footprint, resulting inefficient memory usage. The source code, documentation, and examples are available undera BSD license at https://www.kymat.io/
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras
Authors: Adrian Cosma, Ion Emilian Radoi, Valentin Radu
Date: 2018-12-28 00:00:00
Tasks: Activity Recognition, Pose Estimation

Recent advancements in energy-efficient hardware technology is driving the
exponential growth we are experiencing in the Internet of Things (IoT) space,
with more pervasive computations being performed near to data generation
sources. A range of intelligent devices and applications performing local
detection is emerging (activity recognition, fitness monitoring, etc.) bringing
with them obvious advantages such as reducing detection latency for improved
interaction with devices and safeguarding user data by not leaving the device.
Video processing holds utility for many emerging applications and data
labelling in the IoT space. However, performing this video processing with deep
neural networks at the edge of the Internet is not trivial. In this paper we
show that pedestrian location estimation using deep neural networks is
achievable on fixed cameras with limited compute resources. Our approach uses
pose estimation from key body points detection to extend pedestrian skeleton
when whole body not in image (occluded by obstacles or partially outside of
frame), which achieves better location estimation performance (infrence time
and memory footprint) compared to fitting a bounding box over pedestrian and
scaling. We collect a sizable dataset comprising of over 2100 frames in videos
from one and two surveillance cameras pointing from different angles at the
scene, and annotate each frame with the exact position of person in image, in
42 different scenarios of activity and occlusion. We compare our pose
estimation based location detection with a popular detection algorithm, YOLOv2,
for overlapping bounding-box generation, our solution achieving faster
inference time (15x speedup) at half the memory footprint, within resource
capabilities on embedded devices, which demonstrate that CamLoc is an efficient
solution for location estimation in videos on smart-cameras.
CFA Bayer image sequence denoising and demosaicking chain
Authors: Antoni Buades, Joan Duran
Date: 2018-12-28 00:00:00
Tasks: Demosaicking, Denoising

The demosaicking provokes the spatial and color correlation of noise, which
is afterwards enhanced by the imaging pipeline. The correct removal previous or
simultaneously with the demosaicking process is not usually considered in the
literature. We present a novel imaging chain including a denoising of the Bayer
CFA and a demosaicking method for image sequences. The proposed algorithm uses
a spatio-temporal patch method for the noise removal and demosaicking of the
CFA. The experimentation, including real examples, illustrates the superior
performance of the proposed chain, avoiding the creation of artifacts and
colored spots in the final image.
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Authors: Jie Yang, Si-Qi Liu, Sasa Grbic, Arnaud Arindra Adiyoso Setio, Zhoubing Xu, Eli Gibson, Guillaume Chabin, Bogdan Georgescu, Andrew F. Laine, Dorin Comaniciu
Date: 2018-12-28 00:00:00
Tasks: Binary Classification, General Classification

Though large-scale datasets are essential for training deep learning systems,
it is expensive to scale up the collection of medical imaging datasets.
Synthesizing the objects of interests, such as lung nodules, in medical images
based on the distribution of annotated datasets can be helpful for improving
the supervised learning tasks, especially when the datasets are limited by size
and class balance. In this paper, we propose the class-aware adversarial
synthesis framework to synthesize lung nodules in CT images. The framework is
built with a coarse-to-fine patch in-painter (generator) and two class-aware
discriminators. By conditioning on the random latent variables and the target
nodule labels, the trained networks are able to generate diverse nodules given
the same context. By evaluating on the public LIDC-IDRI dataset, we demonstrate
an example application of the proposed framework for improving the accuracy of
the lung nodule malignancy estimation as a binary classification problem, which
is important in the lung screening scenario. We show that combining the real
image patches and the synthetic lung nodules in the training set can improve
the mean AUC classification score across different network architectures by 2%.
State representation learning with recurrent capsule networks
Authors: Louis Annabi, Michael Garcia Ortiz
Date: 2018-12-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Unsupervised learning of compact and relevant state representations has been
proved very useful at solving complex reinforcement learning tasks. In this
paper, we propose a recurrent capsule network that learns such representations
by trying to predict the future observations in an agent's trajectory.
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Authors: Junhao Wen, Jorge Samper-Gonzalez, Simona Bottani, Alexandre Routier, Ninon Burgos, Thomas Jacquemont, Sabrina Fontanella, Stanley Durrleman, Stephane Epelbaum, Anne Bertrand, Olivier Colliot
Date: 2018-12-28 00:00:00
Tasks: Classification, Diffusion  MRI, General Classification, Small Data Image Classification

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, classification performance obtained with different approaches is difficult to compare and these studies are also difficult to reproduce. In the present paper, we first extend a previously proposed framework to diffusion MRI data for AD classification. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 0.05 up to 0.40 relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML.
Honey Authentication with Machine Learning Augmented Bright-Field Microscopy
Authors: Peter He, Alexis Gkantiragas, Gerard Glowacki
Date: 2018-12-28 00:00:00
Tasks: BIG-bench Machine Learning

Honey has been collected and used by humankind as both a food and medicine
for thousands of years. However, in the modern economy, honey has become
subject to mislabelling and adulteration making it the third most faked food
product in the world. The international scale of fraudulent honey has had both
economic and environmental ramifications. In this paper, we propose a novel
method of identifying fraudulent honey using machine learning augmented
microscopy.
Drug cell line interaction prediction
Authors: Pengfei Liu
Date: 2018-12-28 00:00:00
Tasks: Drug Discovery, Prediction

Understanding the phenotypic drug response on cancer cell lines plays a vital
rule in anti-cancer drug discovery and re-purposing. The Genomics of Drug
Sensitivity in Cancer (GDSC) database provides open data for researchers in
phenotypic screening to test their models and methods. Previously, most
research in these areas starts from the fingerprints or features of drugs,
instead of their structures. In this paper, we introduce a model for phenotypic
screening, which is called twin Convolutional Neural Network for drugs in
SMILES format (tCNNS). tCNNS is comprised of CNN input channels for drugs in
SMILES format and cancer cell lines respectively. Our model achieves $0.84$ for
the coefficient of determinant($R^2$) and $0.92$ for Pearson
correlation($R_p$), which are significantly better than previous
works\cite{ammad2014integrative,haider2015copula,menden2013machine}. Besides
these statistical metrics, tCNNS also provides some insights into phenotypic
screening.
A discrete version of CMA-ES
Authors: Eric Benhamou, Jamal Atif, Rida Laraki
Date: 2018-12-27 00:00:00

Modern machine learning uses more and more advanced optimization techniques
to find optimal hyper parameters. Whenever the objective function is
non-convex, non continuous and with potentially multiple local minima, standard
gradient descent optimization methods fail. A last resource and very different
method is to assume that the optimum(s), not necessarily unique, is/are
distributed according to a distribution and iteratively to adapt the
distribution according to tested points. These strategies originated in the
early 1960s, named Evolution Strategy (ES) have culminated with the CMA-ES
(Covariance Matrix Adaptation) ES. It relies on a multi variate normal
distribution and is supposed to be state of the art for general optimization
program. However, it is far from being optimal for discrete variables. In this
paper, we extend the method to multivariate binomial correlated distributions.
For such a distribution, we show that it shares similar features to the multi
variate normal: independence and correlation is equivalent and correlation is
efficiently modeled by interaction between different variables. We discuss this
distribution in the framework of the exponential family. We prove that the
model can estimate not only pairwise interactions among the two variables but
also is capable of modeling higher order interactions. This allows creating a
version of CMA ES that can accommodate efficiently discrete variables. We
provide the corresponding algorithm and conclude.
Multi-task Prediction of Patient Workload
Authors: Mohammad Hessam Olya, Dongxiao Zhu, Kai Yang
Date: 2018-12-27 00:00:00
Tasks: Decision Making, Multi-Task Learning, Prediction

Developing reliable workload predictive models can affect many aspects of
clinical decision making procedure. The primary challenge in healthcare systems
is handling the demand uncertainty over the time. This issue becomes more
critical for the healthcare facilities that provide service for chronic disease
treatment because of the need for continuous treatments over the time. Although
some researchers focused on exploring the methods for workload prediction
recently, few types of research mainly focused on forecasting a quantitative
measure for the workload of healthcare providers. Also, among the mentioned
studies most of them just focused on workload prediction within one facility.
The drawback of the previous studies is the problem is not investigated for
multiple facilities where the quality of provided service, the equipment, and
resources used for provided service as well as the diagnosis and treatment
procedures may differ even for patients with similar conditions. To tackle the
mentioned issue, this paper suggests a framework for patient workload
prediction by using patients data from VA facilities across the US. To capture
the information of patients with similar attributes and make the prediction
more accurate, a heuristic cluster based algorithm for single task learning as
well as a multi task learning approach are developed in this research.
Tied Hidden Factors in Neural Networks for End-to-End Speaker Recognition
Authors: Antonio Miguel, Jorge Llombart, Alfonso Ortega, Eduardo Lleida
Date: 2018-12-27 00:00:00
Tasks: Speaker Recognition, Speaker Verification

In this paper we propose a method to model speaker and session variability
and able to generate likelihood ratios using neural networks in an end-to-end
phrase dependent speaker verification system. As in Joint Factor Analysis, the
model uses tied hidden variables to model speaker and session variability and a
MAP adaptation of some of the parameters of the model. In the training
procedure our method jointly estimates the network parameters and the values of
the speaker and channel hidden variables. This is done in a two-step
backpropagation algorithm, first the network weights and factor loading
matrices are updated and then the hidden variables, whose gradients are
calculated by aggregating the corresponding speaker or session frames, since
these hidden variables are tied. The last layer of the network is defined as a
linear regression probabilistic model whose inputs are the previous layer
outputs. This choice has the advantage that it produces likelihoods and
additionally it can be adapted during the enrolment using MAP without the need
of a gradient optimization. The decisions are made based on the ratio of the
output likelihoods of two neural network models, speaker adapted and universal
background model. The method was evaluated on the RSR2015 database.
KI, Philosophie, Logik
Authors: Karl Schlechta
Date: 2018-12-27 00:00:00
Tasks: Philosophy

This is a short (and personal) introduction in German to the connections
between artificial intelligence, philosophy, and logic, and to the author's
work.
  Dies ist eine kurze (und persoenliche) Einfuehrung in die Zusammenhaenge
zwischen Kuenstlicher Intelligenz, Philosophie, und Logik, und in die Arbeiten
des Autors.
Low-Cost Device Prototype for Automatic Medical Diagnosis Using Deep Learning Methods
Authors: Neil Deshmukh
Date: 2018-12-27 00:00:00
Tasks: Medical Diagnosis

This paper introduces a novel low-cost device prototype for the automatic diagnosis of diseases, utilizing inputted symptoms and personal background. The engineering goal is to solve the problem of limited healthcare access with a single device. Diagnosing diseases automatically is an immense challenge, owing to their variable properties and symptoms. On the other hand, Neural Networks have developed into a powerful tool in the field of machine learning, one that is showing to be extremely promising at computing diagnosis even with inconsistent variables. In this research, a cheap device was created to allow for straightforward diagnosis and treatment of human diseases. By utilizing Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), outfitted on a Raspberry Pi Zero processor ($5), the device is able to detect up to 1537 different diseases and conditions and utilize a CNN for on-device visual diagnostics. The user can input the symptoms using the buttons on the device and can take pictures using the same mechanism. The algorithm processes inputted symptoms, providing diagnosis and possible treatment options for common conditions. The purpose of this work was to be able to diagnose diseases through an affordable processor with high accuracy, as it is currently achieving an accuracy of 90% for Top-5 symptom-based diagnoses, and 91% for visual skin diseases. The NNs achieve performance far above any other tested system, and its efficiency and ease of use will prove it to be a helpful tool for people around the world. This device could potentially provide low-cost universal access to vital diagnostics and treatment options.
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models
Authors: Daniel T. Chang
Date: 2018-12-26 00:00:00
Tasks: Attribute

Latent representations are the essence of deep generative models and
determine their usefulness and power. For latent representations to be useful
as generative concept representations, their latent space must support latent
space interpolation, attribute vectors and concept vectors, among other things.
We investigate and discuss latent variable modeling, including latent variable
models, latent representations and latent spaces, particularly hierarchical
latent representations and latent space vectors and geometry. Our focus is on
that used in variational autoencoders and generative adversarial networks.
Deep Autoencoder for Recommender Systems: Parameter Influence Analysis
Authors: Dai Hoang Tran, Zawar Hussain, Wei Emma Zhang, Nguyen Lu Dang Khoa, Nguyen H. Tran, Quan Z. Sheng
Date: 2018-12-25 00:00:00
Tasks: Recommendation Systems

Recommender systems have recently attracted many researchers in the deep
learning community. The state-of-the-art deep neural network models used in
recommender systems are typically multilayer perceptron and deep Autoencoder
(DAE), among which DAE usually shows better performance due to its superior
capability to reconstruct the inputs. However, we found existing DAE
recommendation systems that have similar implementations on similar datasets
result in vastly different parameter settings. In this work, we have built a
flexible DAE model, named FlexEncoder that uses configurable parameters and
unique features to analyse the parameter influences on the prediction accuracy
of recommender systems. This will help us identify the best-performance
parameters given a dataset. Extensive evaluation on the MovieLens datasets are
conducted, which drives our conclusions on the influences of DAE parameters.
Specifically, we find that DAE parameters strongly affect the prediction
accuracy of the recommender systems, and the effect is transferable to similar
datasets in a larger size. We open our code to public which could benefit both
new users for DAE -- they can quickly understand how DAE works for
recommendation systems, and experienced DAE users -- it easier for them to tune
the parameters on different datasets.
Deep Representation Learning for Clustering of Health Tweets
Authors: Oguzhan Gencoglu
Date: 2018-12-25 00:00:00
Tasks: Clustering, Representation Learning

Twitter has been a prominent social media platform for mining
population-level health data and accurate clustering of health-related tweets
into topics is important for extracting relevant health insights. In this work,
we propose deep convolutional autoencoders for learning compact representations
of health-related tweets, further to be employed in clustering. We compare our
method to several conventional tweet representation methods including
bag-of-words, term frequency-inverse document frequency, Latent Dirichlet
Allocation and Non-negative Matrix Factorization with 3 different clustering
algorithms. Our results show that the clustering performance using proposed
representation learning scheme significantly outperforms that of conventional
methods for all experiments of different number of clusters. In addition, we
propose a constraint on the learned representations during the neural network
training in order to further enhance the clustering performance. All in all,
this study introduces utilization of deep neural network-based architectures,
i.e., deep convolutional autoencoders, for learning informative representations
of health-related tweets.
Multiple Sclerosis Lesion Inpainting Using Non-Local Partial Convolutions
Authors: Hao Xiong, Chaoyue Wang, DaCheng Tao, Michael Barnett, Chenyu Wang
Date: 2018-12-24 00:00:00

Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) that results in focal injury to the grey and white matter. The presence of white matter lesions biases morphometric analyses such as registration, individual longitudinal measurements and tissue segmentation for brain volume measurements. Lesion-inpainting with intensities derived from surrounding healthy tissue represents one approach to alleviate such problems. However, existing methods inpaint lesions based on texture information derived from local surrounding tissue, often leading to inconsistent inpainting and the generation of artifacts such as intensity discrepancy and blurriness. Based on these observations, we propose non-local partial convolutions (NLPC) that integrates a Unet-like network with the non-local module. The non-local module is exploited to capture long range dependencies between the lesion area and remaining normal-appearing brain regions. Then, the lesion area is filled by referring to normal-appearing regions with more similar features. This method generates inpainted regions that appear more realistic and natural. Our quantitative experimental results also demonstrate superiority of this technique of existing state-of-the-art inpainting methods.