pwc-archive/papers-with-abstracts huggingface dataset:
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Authors: Zhang Li, Zheyu Hu, Jiaolong Xu, Tao Tan, Hui Chen, Zhi Duan, Ping Liu, Jun Tang, Guoping Cai, Quchang Ouyang, Yuling Tang, Geert Litjens, Qiang Li
Date: 2018-03-14 00:00:00
Tasks: Deep Learning, Lung Cancer Diagnosis

Aim: Early detection and correct diagnosis of lung cancer are the most
important steps in improving patient outcome. This study aims to assess which
deep learning models perform best in lung cancer diagnosis. Methods: Non-small
cell lung carcinoma and small cell lung carcinoma biopsy specimens were
consecutively obtained and stained. The specimen slides were diagnosed by two
experienced pathologists (over 20 years). Several deep learning models were
trained to discriminate cancer and non-cancer biopsies. Result: Deep learning
models give reasonable AUC from 0.8810 to 0.9119. Conclusion: The deep learning
analysis could help to speed up the detection process for the whole-slide image
(WSI) and keep the comparable detection rate with human observer.
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
Authors: Carl-Johan Hoel, Krister Wolff, Leo Laine
Date: 2018-03-14 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a method, based on deep reinforcement learning, for
automatically generating a general purpose decision making function. A Deep
Q-Network agent was trained in a simulated environment to handle speed and lane
change decisions for a truck-trailer combination. In a highway driving case, it
is shown that the method produced an agent that matched or surpassed the
performance of a commonly used reference model. To demonstrate the generality
of the method, the exact same algorithm was also tested by training it for an
overtaking case on a road with oncoming traffic. Furthermore, a novel way of
applying a convolutional neural network to high level input that represents
interchangeable objects is also introduced.
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Authors: Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord
Date: 2018-03-14 00:00:00
Tasks: AI2 Reasoning Challenge, ARC, Question Answering, Retrieval

We present a new question set, text corpus, and baselines assembled to
encourage AI research in advanced question answering. Together, these
constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful
knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC
question set is partitioned into a Challenge Set and an Easy Set, where the
Challenge Set contains only questions answered incorrectly by both a
retrieval-based algorithm and a word co-occurence algorithm. The dataset
contains only natural, grade-school science questions (authored for human
tests), and is the largest public-domain set of this kind (7,787 questions). We
test several baselines on the Challenge Set, including leading neural models
from the SQuAD and SNLI tasks, and find that none are able to significantly
outperform a random baseline, reflecting the difficult nature of this task. We
are also releasing the ARC Corpus, a corpus of 14M science sentences relevant
to the task, and implementations of the three neural baseline models tested.
Can your model perform better? We pose ARC as a challenge to the community.
SentEval: An Evaluation Toolkit for Universal Sentence Representations
Authors: Alexis Conneau, Douwe Kiela
Date: 2018-03-14 00:00:00
Proceeding: LREC 2018 5
Tasks: General Classification, Multi-class Classification, Natural Language Inference, Sentence, Sentence Similarity

We introduce SentEval, a toolkit for evaluating the quality of universal
sentence representations. SentEval encompasses a variety of tasks, including
binary and multi-class classification, natural language inference and sentence
similarity. The set of tasks was selected based on what appears to be the
community consensus regarding the appropriate evaluations for universal
sentence representations. The toolkit comes with scripts to download and
preprocess datasets, and an easy interface to evaluate sentence encoders. The
aim is to provide a fairer, less cumbersome and more centralized way for
evaluating sentence representations.
Averaging Weights Leads to Wider Optima and Better Generalization
Authors: Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson
Date: 2018-03-14 00:00:00
Tasks: Image Classification, Stochastic Optimization

Deep neural networks are typically trained by optimizing a loss function with
an SGD variant, in conjunction with a decaying learning rate, until
convergence. We show that simple averaging of multiple points along the
trajectory of SGD, with a cyclical or constant learning rate, leads to better
generalization than conventional training. We also show that this Stochastic
Weight Averaging (SWA) procedure finds much flatter solutions than SGD, and
approximates the recent Fast Geometric Ensembling (FGE) approach with a single
model. Using SWA we achieve notable improvement in test accuracy over
conventional SGD training on a range of state-of-the-art residual networks,
PyramidNets, DenseNets, and Shake-Shake networks on CIFAR-10, CIFAR-100, and
ImageNet. In short, SWA is extremely easy to implement, improves
generalization, and has almost no computational overhead.
Near-Optimal Coresets of Kernel Density Estimates
Authors: Jeff M. Phillips, Wai Ming Tai
Date: 2018-02-06 00:00:00

We construct near-optimal coresets for kernel density estimates for points in
$\mathbb{R}^d$ when the kernel is positive definite. Specifically we show a
polynomial time construction for a coreset of size $O(\sqrt{d}/\varepsilon\cdot
\sqrt{\log 1/\varepsilon} )$, and we show a near-matching lower bound of size
$\Omega(\min\{\sqrt{d}/\varepsilon, 1/\varepsilon^2\})$. When $d\geq
1/\varepsilon^2$, it is known that the size of coreset can be
$O(1/\varepsilon^2)$. The upper bound is a polynomial-in-$(1/\varepsilon)$
improvement when $d \in [3,1/\varepsilon^2)$ and the lower bound is the first
known lower bound to depend on $d$ for this problem. Moreover, the upper bound
restriction that the kernel is positive definite is significant in that it
applies to a wide-variety of kernels, specifically those most important for
machine learning. This includes kernels for information distances and the sinc
kernel which can be negative.
Approximate Query Matching for Image Retrieval
Authors: Abhijit Suprem, Polo Chau
Date: 2018-03-14 00:00:00
Tasks: Graph Generation, Image Retrieval, Object, Retrieval, Scene Graph Generation, Semantic Segmentation

Traditional image recognition involves identifying the key object in a
portrait-type image with a single object focus (ILSVRC, AlexNet, and VGG). More
recent approaches consider dense image recognition - segmenting an image with
appropriate bounding boxes and performing image recognition within these
bounding boxes (Semantic segmentation). The Visual Genome dataset [5] is an
attempt to bridge these various approaches to a cohesive dataset for each
subtask - bounding box generation, image recognition, captioning, and a new
operation: scene graph generation. Our focus is on using such scene graphs to
perform graph search on image databases to holistically retrieve images based
on a search criteria. We develop a method to store scene graphs and metadata in
graph databases (using Neo4J) and to perform fast approximate retrieval of
images based on a graph search query. We process more complex queries than
single object search, e.g. "girl eating cake" retrieves images that contain the
specified relation as well as variations.
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Authors: Can Karakus, Yifan Sun, Suhas Diggavi, Wotao Yin
Date: 2018-03-14 00:00:00
Tasks: Distributed Optimization, regression

Performance of distributed optimization and learning systems is bottlenecked
by "straggler" nodes and slow communication links, which significantly delay
computation. We propose a distributed optimization framework where the dataset
is "encoded" to have an over-complete representation with built-in redundancy,
and the straggling nodes in the system are dynamically left out of the
computation at every iteration, whose loss is compensated by the embedded
redundancy. We show that oblivious application of several popular optimization
algorithms on encoded data, including gradient descent, L-BFGS, proximal
gradient under data parallelism, and coordinate descent under model
parallelism, converge to either approximate or exact solutions of the original
problem when stragglers are treated as erasures. These convergence results are
deterministic, i.e., they establish sample path convergence for arbitrary
sequences of delay patterns or distributions on the nodes, and are independent
of the tail behavior of the delay distribution. We demonstrate that equiangular
tight frames have desirable properties as encoding matrices, and propose
efficient mechanisms for encoding large-scale data. We implement the proposed
technique on Amazon EC2 clusters, and demonstrate its performance over several
learning problems, including matrix factorization, LASSO, ridge regression and
logistic regression, and compare the proposed method with uncoded,
asynchronous, and data replication strategies.
Neural Stereoscopic Image Style Transfer
Authors: Xinyu Gong, HaoZhi Huang, Lin Ma, Fumin Shen, Wei Liu, Tong Zhang
Date: 2018-02-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Style Transfer

Neural style transfer is an emerging technique which is able to endow
daily-life images with attractive artistic styles. Previous work has succeeded
in applying convolutional neural networks (CNNs) to style transfer for
monocular images or videos. However, style transfer for stereoscopic images is
still a missing piece. Different from processing a monocular image, the two
views of a stylized stereoscopic pair are required to be consistent to provide
observers a comfortable visual experience. In this paper, we propose a novel
dual path network for view-consistent style transfer on stereoscopic images.
While each view of the stereoscopic pair is processed in an individual path, a
novel feature aggregation strategy is proposed to effectively share information
between the two paths. Besides a traditional perceptual loss being used for
controlling the style transfer quality in each view, a multi-layer view loss is
leveraged to enforce the network to coordinate the learning of both the paths
to generate view-consistent stylized results. Extensive experiments show that,
compared against previous methods, our proposed model can produce stylized
stereoscopic images which achieve decent view consistency.
Self-Similar Epochs: Value in Arrangement
Authors: Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias
Date: 2018-03-14 00:00:00
Proceeding: ICLR 2019 5

Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples. This practice means that sub-epochs comprise of independent random samples of the training data that may not preserve informative structure present in the full data. We hypothesize that the training can be more effective with {\em self-similar} arrangements that potentially allow each epoch to provide benefits of multiple ones. We study this for "matrix factorization" -- the common task of learning metric embeddings of entities such as queries, videos, or words from example pairwise associations. We construct arrangements that preserve the weighted Jaccard similarities of rows and columns and experimentally observe training acceleration of 3\%-37\% on synthetic and recommendation datasets. Principled arrangements of training examples emerge as a novel and potentially powerful enhancement to SGD that merits further exploration.
Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks - Application on Synthetic Aperture Radar Images
Authors: Gabriele Costante, Thomas A. Ciarfuglia, Filippo Biondi
Date: 2018-03-14 00:00:00
Tasks: Decoder

Synthetic aperture radar (SAR) interferometry (InSAR) is performed using
repeat-pass geometry. InSAR technique is used to estimate the topographic
reconstruction of the earth surface. The main problem of the range-Doppler
focusing technique is the nature of the two-dimensional SAR result, affected by
the layover indetermination. In order to resolve this problem, a minimum of two
sensor acquisitions, separated by a baseline and extended in the
cross-slant-range, are needed. However, given its multi-temporal nature, these
techniques are vulnerable to atmosphere and Earth environment parameters
variation in addition to physical platform instabilities. Furthermore, either
two radars are needed or an interferometric cycle is required (that spans from
days to weeks), which makes real time DEM estimation impossible. In this work,
the authors propose a novel experimental alternative to the InSAR method that
uses single-pass acquisitions, using a data driven approach implemented by Deep
Neural Networks. We propose a fully Convolutional Neural Network (CNN)
Encoder-Decoder architecture, training it on radar images in order to estimate
DEMs from single pass image acquisitions. Our results on a set of Sentinel
images show that this method is able to learn to some extent the statistical
properties of the DEM. The results of this exploratory analysis are encouraging
and open the way to the solution of single-pass DEM estimation problem with
data driven approaches.
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Authors: Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle Champley, Timo Bremer
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Computed Tomography (CT), CT Reconstruction

Computed Tomography (CT) reconstruction is a fundamental component to a wide
variety of applications ranging from security, to healthcare. The classical
techniques require measuring projections, called sinograms, from a full
180$^\circ$ view of the object. This is impractical in a limited angle
scenario, when the viewing angle is less than 180$^\circ$, which can occur due
to different factors including restrictions on scanning time, limited
flexibility of scanner rotation, etc. The sinograms obtained as a result, cause
existing techniques to produce highly artifact-laden reconstructions. In this
paper, we propose to address this problem through implicit sinogram completion,
on a challenging real world dataset containing scans of common checked-in
luggage. We propose a system, consisting of 1D and 2D convolutional neural
networks, that operates on a limited angle sinogram to directly produce the
best estimate of a reconstruction. Next, we use the x-ray transform on this
reconstruction to obtain a "completed" sinogram, as if it came from a full
180$^\circ$ measurement. We feed this to standard analytical and iterative
reconstruction techniques to obtain the final reconstruction. We show with
extensive experimentation that this combined strategy outperforms many
competitive baselines. We also propose a measure of confidence for the
reconstruction that enables a practitioner to gauge the reliability of a
prediction made by our network. We show that this measure is a strong indicator
of quality as measured by the PSNR, while not requiring ground truth at test
time. Finally, using a segmentation experiment, we show that our reconstruction
preserves the 3D structure of objects effectively.
Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats
Authors: Krunoslav Vinicki, Pierluigi Ferrari, Maja Belic, Romana Turk
Date: 2018-03-13 00:00:00
Tasks: Autonomous Driving, Deep Learning

Recent advances in artificial intelligence (AI), specifically in computer
vision (CV) and deep learning (DL), have created opportunities for novel
systems in many fields. In the last few years, deep learning applications have
demonstrated impressive results not only in fields such as autonomous driving
and robotics, but also in the field of medicine, where they have, in some
cases, even exceeded human-level performance. However, despite the huge
potential, adoption of deep learning-based methods is still slow in many areas,
especially in veterinary medicine, where we haven't been able to find any
research papers using modern convolutional neural networks (CNNs) in medical
image processing. We believe that using deep learning-based medical imaging can
enable more accurate, faster and less expensive diagnoses in veterinary
medicine. In order to do so, however, these methods have to be accessible to
everyone in this field, not just to computer scientists. To show the potential
of this technology, we present results on a real-world task in veterinary
medicine that is usually done manually: feline reticulocyte percentage. Using
an open source Keras implementation of the Single-Shot MultiBox Detector (SSD)
model architecture and training it on only 800 labeled images, we achieve an
accuracy of 98.7% at predicting the correct number of aggregate reticulocytes
in microscope images of cat blood smears. The main motivation behind this paper
is to show not only that deep learning can approach or even exceed human-level
performance on a task like this, but also that anyone in the field can
implement it, even without a background in computer science.
Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection
Authors: Chengyang Li, Dan Song, Ruofeng Tong, Min Tang
Date: 2018-03-14 00:00:00
Tasks: Multispectral Object Detection, Pedestrian Detection

Multispectral images of color-thermal pairs have shown more effective than a
single color channel for pedestrian detection, especially under challenging
illumination conditions. However, there is still a lack of studies on how to
fuse the two modalities effectively. In this paper, we deeply compare six
different convolutional network fusion architectures and analyse their
adaptations, enabling a vanilla architecture to obtain detection performances
comparable to the state-of-the-art results. Further, we discover that
pedestrian detection confidences from color or thermal images are correlated
with illumination conditions. With this in mind, we propose an
Illumination-aware Faster R-CNN (IAF RCNN). Specifically, an Illumination-aware
Network is introduced to give an illumination measure of the input image. Then
we adaptively merge color and thermal sub-networks via a gate function defined
over the illumination value. The experimental results on KAIST Multispectral
Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.
Measurement-based adaptation protocol with quantum reinforcement learning
Authors: F. Albarrán-Arriagada, J. C. Retamal, E. Solano, L. Lamata
Date: 2018-03-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Machine learning employs dynamical algorithms that mimic the human capacity
to learn, where the reinforcement learning ones are among the most similar to
humans in this respect. On the other hand, adaptability is an essential aspect
to perform any task efficiently in a changing environment, and it is
fundamental for many purposes, such as natural selection. Here, we propose an
algorithm based on successive measurements to adapt one quantum state to a
reference unknown state, in the sense of achieving maximum overlap. The
protocol naturally provides many identical copies of the reference state, such
that in each measurement iteration more information about it is obtained. In
our protocol, we consider a system composed of three parts, the "environment"
system, which provides the reference state copies; the register, which is an
auxiliary subsystem that interacts with the environment to acquire information
from it; and the agent, which corresponds to the quantum state that is adapted
by digital feedback with input corresponding to the outcome of the measurements
on the register. With this proposal we can achieve an average fidelity between
the environment and the agent of more than $90\% $ with less than $30$
iterations of the protocol. In addition, we extend the formalism to $ d
$-dimensional states, reaching an average fidelity of around $80\% $ in less
than $400$ iterations for $d=$ 11, for a variety of genuinely quantum and
semiclassical states. This work paves the way for the development of quantum
reinforcement learning protocols using quantum data and for the future
deployment of semi-autonomous quantum systems.
Predicting Oral Disintegrating Tablet Formulations by Neural Network Techniques
Authors: Run Han, Yilong Yang, Xiaoshan Li, Defang Ouyang
Date: 2018-03-14 00:00:00
Tasks: Prediction, Small Data Image Classification

Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be
dissolved on the tongue within 3min or less especially for geriatric and
pediatric patients. Current ODT formulation studies usually rely on the
personal experience of pharmaceutical experts and trial-and-error in the
laboratory, which is inefficient and time-consuming. The aim of current
research was to establish the prediction model of ODT formulations with direct
compression process by Artificial Neural Network (ANN) and Deep Neural Network
(DNN) techniques. 145 formulation data were extracted from Web of Science. All
data sets were divided into three parts: training set (105 data), validation
set (20) and testing set (20). ANN and DNN were compared for the prediction of
the disintegrating time. The accuracy of the ANN model has reached 85.60%,
80.00% and 75.00% on the training set, validation set and testing set
respectively, whereas that of the DNN model was 85.60%, 85.00% and 80.00%,
respectively. Compared with the ANN, DNN showed the better prediction for ODT
formulations. It is the first time that deep neural network with the improved
dataset selection algorithm is applied to formulation prediction on small data.
The proposed predictive approach could evaluate the critical parameters about
quality control of formulation, and guide research and process development. The
implementation of this prediction model could effectively reduce drug product
development timeline and material usage, and proactively facilitate the
development of a robust drug product.
Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB
Authors: Aitor Alvarez-Gila, Joost Van de Weijer, Estibaliz Garrote
Date: 2017-09-01 00:00:00
Tasks: Image Reconstruction

Hyperspectral signal reconstruction aims at recovering the original spectral
input that produced a certain trichromatic (RGB) response from a capturing
device or observer. Given the heavily underconstrained, non-linear nature of
the problem, traditional techniques leverage different statistical properties
of the spectral signal in order to build informative priors from real world
object reflectances for constructing such RGB to spectral signal mapping.
However, most of them treat each sample independently, and thus do not benefit
from the contextual information that the spatial dimensions can provide. We
pose hyperspectral natural image reconstruction as an image to image mapping
learning problem, and apply a conditional generative adversarial framework to
help capture spatial semantics. This is the first time Convolutional Neural
Networks -and, particularly, Generative Adversarial Networks- are used to solve
this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop
of 33.2% and a Relative RMSE drop of 54.0% on the ICVL natural hyperspectral
image dataset.