pwc-archive/papers-with-abstracts huggingface dataset:
Similar Image Search for Histopathology: SMILY
Authors: Narayan Hegde, Jason D. Hipp, Yun Liu, Michael E. Buck, Emily Reif, Daniel Smilkov, Michael Terry, Carrie J. Cai, Mahul B. Amin, Craig H. Mermel, Phil Q. Nelson, Lily H. Peng, Greg S. Corrado, Martin C. Stumpe
Date: 2019-01-30 00:00:00
Tasks: Image Retrieval

The increasing availability of large institutional and public histopathology
image datasets is enabling the searching of these datasets for diagnosis,
research, and education. Though these datasets typically have associated
metadata such as diagnosis or clinical notes, even carefully curated datasets
rarely contain annotations of the location of regions of interest on each
image. Because pathology images are extremely large (up to 100,000 pixels in
each dimension), further laborious visual search of each image may be needed to
find the feature of interest. In this paper, we introduce a deep learning based
reverse image search tool for histopathology images: Similar Medical Images
Like Yours (SMILY). We assessed SMILY's ability to retrieve search results in
two ways: using pathologist-provided annotations, and via prospective studies
where pathologists evaluated the quality of SMILY search results. As a negative
control in the second evaluation, pathologists were blinded to whether search
results were retrieved by SMILY or randomly. In both types of assessments,
SMILY was able to retrieve search results with similar histologic features,
organ site, and prostate cancer Gleason grade compared with the original query.
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to
improve the efficiency of searching large archives of histopathology images,
without the need to develop and implement specific tools for each application.
Texture-Aware Superpixel Segmentation
Authors: Remi Giraud, Vinh-Thong Ta, Nicolas Papadakis, Yannick Berthoumieu
Date: 2019-01-30 00:00:00
Tasks: Segmentation, Superpixels

Most superpixel algorithms compute a trade-off between spatial and color
features at the pixel level. Hence, they may need fine parameter tuning to
balance the two measures, and highly fail to group pixels with similar local
texture properties. In this paper, we address these issues with a new
Texture-Aware SuperPixel (TASP) method. To accurately segment textured and
smooth areas, TASP automatically adjusts its spatial constraint according to
the local feature variance. Then, to ensure texture homogeneity within
superpixels, a new pixel to superpixel patch-based distance is proposed. TASP
outperforms the segmentation accuracy of the state-of-the-art methods on
texture and also natural color image datasets.
Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons
Authors: Muhammad Amir Shafiq, Zhiling Long, Tariq Alshawi, Ghassan AlRegib
Date: 2019-01-30 00:00:00
Tasks: Saliency Detection

In this paper, we propose a novel approach for saliency detection for seismic
applications using 3D-FFT local spectra and multi-dimensional plane
projections. We develop a projection scheme by dividing a 3D-FFT local spectrum
of a data volume into three distinct components, each depicting changes along a
different dimension of the data. The saliency detection results obtained using
each projected component are then combined to yield a saliency map. To
accommodate the directional nature of seismic data, in this work, we modify the
center-surround model, proven to be biologically plausible for visual
attention, to incorporate directional comparisons around each voxel in a 3D
volume. Experimental results on real seismic dataset from the F3 block in
Netherlands offshore in the North Sea prove that the proposed algorithm is
effective, efficient, and scalable. Furthermore, a subjective comparison of the
results shows that it outperforms the state-of-the-art methods for saliency
detection.
Resolution enhancement in scanning electron microscopy using deep learning
Authors: Kevin de Haan, Zachary S. Ballard, Yair Rivenson, Yichen Wu, Aydogan Ozcan
Date: 2019-01-30 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Super-Resolution

We report resolution enhancement in scanning electron microscopy (SEM) images
using a generative adversarial network. We demonstrate the veracity of this
deep learning-based super-resolution technique by inferring unresolved features
in low-resolution SEM images and comparing them with the accurately
co-registered high-resolution SEM images of the same samples. Through spatial
frequency analysis, we also report that our method generates images with
frequency spectra matching higher resolution SEM images of the same
fields-of-view. By using this technique, higher resolution SEM images can be
taken faster, while also reducing both electron charging and damage to the
samples.
Neuroevolution with Perceptron Turing Machines
Authors: David Landaeta
Date: 2019-01-30 00:00:00

We introduce the perceptron Turing machine and show how it can be used to
create a system of neuroevolution. Advantages of this approach include
automatic scaling of solutions to larger problem sizes, the ability to
experiment with hand-coded solutions, and an enhanced potential for
understanding evolved solutions. Hand-coded solutions may be implemented in the
low-level language of Turing machines, which is the genotype used in
neuroevolution, but a high-level language called Lopro is introduced to make
the job easier.
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Authors: Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare
Date: 2019-01-30 00:00:00
Tasks: Distributional Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Since their introduction a year ago, distributional approaches to
reinforcement learning (distributional RL) have produced strong results
relative to the standard approach which models expected values (expected RL).
However, aside from convergence guarantees, there have been few theoretical
results investigating the reasons behind the improvements distributional RL
provides. In this paper we begin the investigation into this fundamental
question by analyzing the differences in the tabular, linear approximation, and
non-linear approximation settings. We prove that in many realizations of the
tabular and linear approximation settings, distributional RL behaves exactly
the same as expected RL. In cases where the two methods behave differently,
distributional RL can in fact hurt performance when it does not induce
identical behaviour. We then continue with an empirical analysis comparing
distributional and expected RL methods in control settings with non-linear
approximators to tease apart where the improvements from distributional RL
methods are coming from.
DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals
Authors: Chiyu "Max" Jiang, Dana Lynn Ona Lansigan, Philip Marcus, Matthias Nießner
Date: 2019-01-30 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Segmentation, Semantic Segmentation

We present a Deep Differentiable Simplex Layer (DDSL) for neural networks for geometric deep learning. The DDSL is a differentiable layer compatible with deep neural networks for bridging simplex mesh-based geometry representations (point clouds, line mesh, triangular mesh, tetrahedral mesh) with raster images (e.g., 2D/3D grids). The DDSL uses Non-Uniform Fourier Transform (NUFT) to perform differentiable, efficient, anti-aliased rasterization of simplex-based signals. We present a complete theoretical framework for the process as well as an efficient backpropagation algorithm. Compared to previous differentiable renderers and rasterizers, the DDSL generalizes to arbitrary simplex degrees and dimensions. In particular, we explore its applications to 2D shapes and illustrate two applications of this method: (1) mesh editing and optimization guided by neural network outputs, and (2) using DDSL for a differentiable rasterization loss to facilitate end-to-end training of polygon generators. We are able to validate the effectiveness of gradient-based shape optimization with the example of airfoil optimization, and using the differentiable rasterization loss to facilitate end-to-end training, we surpass state of the art for polygonal image segmentation given ground-truth bounding boxes.
Real-world Mapping of Gaze Fixations Using Instance Segmentation for Road Construction Safety Applications
Authors: Idris Jeelani, Khashayar Asadi, Hariharan Ramshankar, Kevin Han, Alex Albert
Date: 2019-01-30 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation, Transfer Learning

Research studies have shown that a large proportion of hazards remain
unrecognized, which expose construction workers to unanticipated safety risks.
Recent studies have also found that a strong correlation exists between viewing
patterns of workers, captured using eye-tracking devices, and their hazard
recognition performance. Therefore, it is important to analyze the viewing
patterns of workers to gain a better understanding of their hazard recognition
performance. This paper proposes a method that can automatically map the gaze
fixations collected using a wearable eye-tracker to the predefined areas of
interests. The proposed method detects these areas or objects (i.e., hazards)
of interests through a computer vision-based segmentation technique and
transfer learning. The mapped fixation data is then used to analyze the viewing
behaviors of workers and compute their attention distribution. The proposed
method is implemented on an under construction road as a case study to evaluate
the performance of the proposed method.
A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies
Authors: Adrián Bazaga, Mònica Roldán, Carmen Badosa, Cecilia Jiménez-Mallebrera, Josep M. Porta
Date: 2019-01-30 00:00:00
Tasks: General Classification

The development of machine learning systems for the diagnosis of rare
diseases is challenging mainly due the lack of data to study them. Despite this
challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD)
of low-prevalence, congenital muscular dystrophies from confocal microscopy
images. The proposed CAD system relies on a Convolutional Neural Network (CNN)
which performs an independent classification for non-overlapping patches tiling
the input image, and generates an overall decision summarizing the individual
decisions for the patches on the query image. This decision scheme points to
the possibly problematic areas in the input images and provides a global
quantitative evaluation of the state of the patients, which is fundamental for
diagnosis and to monitor the efficiency of therapies.
HyperGAN: A Generative Model for Diverse, Performant Neural Networks
Authors: Neale Ratzlaff, Li Fuxin
Date: 2019-01-30 00:00:00
Tasks: General Classification

Standard neural networks are often overconfident when presented with data outside the training distribution. We introduce HyperGAN, a new generative model for learning a distribution of neural network parameters. HyperGAN does not require restrictive assumptions on priors, and networks sampled from it can be used to quickly create very large and diverse ensembles. HyperGAN employs a novel mixer to project prior samples to a latent space with correlated dimensions, and samples from the latent space are then used to generate weights for each layer of a deep neural network. We show that HyperGAN can learn to generate parameters which label the MNIST and CIFAR-10 datasets with competitive performance to fully supervised learning, while learning a rich distribution of effective parameters. We also show that HyperGAN can also provide better uncertainty estimates than standard ensembles by evaluating on out of distribution data as well as adversarial examples.
The Wilderness Area Data Set: Adapting the Covertype data set for unsupervised learning
Authors: Richard Hugh Moulton, Jakub Zgraja
Date: 2019-01-30 00:00:00
Tasks: Clustering, General Classification

Benchmark data sets are of vital importance in machine learning research, as
indicated by the number of repositories that exist to make them publicly
available. Although many of these are usable in the stream mining context as
well, it is less obvious which data sets can be used to evaluate data stream
clustering algorithms. We note that the classic Covertype data set's size makes
it attractive for use in stream mining but unfortunately it is specifically
designed for classification. Here we detail the process of transforming the
Covertype data set into one amenable for unsupervised learning, which we call
the Wilderness Area data set. Our quantitative analysis allows us to conclude
that the Wilderness Area data set is more appropriate for unsupervised learning
than the original Covertype data set.
Metric Gaussian Variational Inference
Authors: Jakob Knollmüller, Torsten A. Enßlin
Date: 2019-01-30 00:00:00
Tasks: Bayesian Inference, Variational Inference

Solving Bayesian inference problems approximately with variational approaches can provide fast and accurate results. Capturing correlation within the approximation requires an explicit parametrization. This intrinsically limits this approach to either moderately dimensional problems, or requiring the strongly simplifying mean-field approach. We propose Metric Gaussian Variational Inference (MGVI) as a method that goes beyond mean-field. Here correlations between all model parameters are taken into account, while still scaling linearly in computational time and memory. With this method we achieve higher accuracy and in many cases a significant speedup compared to traditional methods. MGVI is an iterative method that performs a series of Gaussian approximations to the posterior. We alternate between approximating the covariance with the inverse Fisher information metric evaluated at an intermediate mean estimate and optimizing the KL-divergence for the given covariance with respect to the mean. This procedure is iterated until the uncertainty estimate is self-consistent with the mean parameter. We achieve linear scaling by avoiding to store the covariance explicitly at any time. Instead we draw samples from the approximating distribution relying on an implicit representation and numerical schemes to approximately solve linear equations. Those samples are used to approximate the KL-divergence and its gradient. The usage of natural gradient descent allows for rapid convergence. Formulating the Bayesian model in standardized coordinates makes MGVI applicable to any inference problem with continuous parameters. We demonstrate the high accuracy of MGVI by comparing it to HMC and its fast convergence relative to other established methods in several examples. We investigate real-data applications, as well as synthetic examples of varying size and complexity and up to a million model parameters.
Noise2Self: Blind Denoising by Self-Supervision
Authors: Joshua Batson, Loic Royer
Date: 2019-01-30 00:00:00
Tasks: Denoising

We propose a general framework for denoising high-dimensional measurements which requires no prior on the signal, no estimate of the noise, and no clean training data. The only assumption is that the noise exhibits statistical independence across different dimensions of the measurement, while the true signal exhibits some correlation. For a broad class of functions ("$\mathcal{J}$-invariant"), it is then possible to estimate the performance of a denoiser from noisy data alone. This allows us to calibrate $\mathcal{J}$-invariant versions of any parameterised denoising algorithm, from the single hyperparameter of a median filter to the millions of weights of a deep neural network. We demonstrate this on natural image and microscopy data, where we exploit noise independence between pixels, and on single-cell gene expression data, where we exploit independence between detections of individual molecules. This framework generalizes recent work on training neural nets from noisy images and on cross-validation for matrix factorization.
New insights on Multi-Solution Distribution of the P3P Problem
Authors: Bo Wang, Hao Hu, Caixia Zhang
Date: 2019-01-30 00:00:00

Traditionally, the P3P problem is solved by firstly transforming its 3
quadratic equations into a quartic one, then by locating the roots of the
resulting quartic equation and verifying whether a root does really correspond
to a true solution of the P3P problem itself. However, a root of the quartic
equation does not always correspond to a solution of the P3P problem. In this
work, we show that when the optical center is outside of all the 6 toroids
defined by the control point triangle, each positive root of the Grunert's
quartic equation must correspond to a true solution of the P3P problem, and the
corresponding P3P problem cannot have a unique solution, it must have either 2
positive solutions or 4 positive solutions. In addition, we show that when the
optical center passes through any one of the 3 toroids among these 6 toroids (
except possibly for two concentric circles) , the number of the solutions of
the corresponding P3P problem always changes by 1, either increased by 1 or
decreased by 1.Furthermore we show that such changed solutions always locate in
a small neighborhood of control points, hence the 3 toroids are critical
surfaces of the P3P problem and the 3 control points are 3 singular points of
solutions. A notable example is that when the optical center passes through the
outer surface of the union of the 6 toroids from the outside to inside, the
number of the solutions must always decrease by 1. Our results are the first to
give an explicit and geometrically intuitive relationship between the P3P
solutions and the roots of its quartic equation. It could act as some
theoretical guidance for P3P practitioners to properly arrange their control
points to avoid undesirable solutions.
GeNet: Deep Representations for Metagenomics
Authors: Mateo Rojas-Carulla, Ilya Tolstikhin, Guillermo Luque, Nicholas Youngblut, Ruth Ley, Bernhard Schölkopf
Date: 2019-01-30 00:00:00
Tasks: General Classification

We introduce GeNet, a method for shotgun metagenomic classification from raw
DNA sequences that exploits the known hierarchical structure between labels for
training. We provide a comparison with state-of-the-art methods Kraken and
Centrifuge on datasets obtained from several sequencing technologies, in which
dataset shift occurs. We show that GeNet obtains competitive precision and good
recall, with orders of magnitude less memory requirements. Moreover, we show
that a linear model trained on top of representations learned by GeNet achieves
recall comparable to state-of-the-art methods on the aforementioned datasets,
and achieves over 90% accuracy in a challenging pathogen detection problem.
This provides evidence of the usefulness of the representations learned by
GeNet for downstream biological tasks.
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need
Authors: Vighnesh Birodkar, Hossein Mobahi, Samy Bengio
Date: 2019-01-29 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object Recognition

Large datasets have been crucial to the success of deep learning models in
the recent years, which keep performing better as they are trained with more
labelled data. While there have been sustained efforts to make these models
more data-efficient, the potential benefit of understanding the data itself, is
largely untapped. Specifically, focusing on object recognition tasks, we wonder
if for common benchmark datasets we can do better than random subsets of the
data and find a subset that can generalize on par with the full dataset when
trained on. To our knowledge, this is the first result that can find notable
redundancies in CIFAR-10 and ImageNet datasets (at least 10%). Interestingly,
we observe semantic correlations between required and redundant images. We hope
that our findings can motivate further research into identifying additional
redundancies and exploiting them for more efficient training or
data-collection.
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Authors: Souhaib Ben Taieb, Gianluca Bontempi, Amir Atiya, Antti Sorjamaa
Date: 2011-08-16 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Variable Selection

Multi-step ahead forecasting is still an open challenge in time series
forecasting. Several approaches that deal with this complex problem have been
proposed in the literature but an extensive comparison on a large number of
tasks is still missing. This paper aims to fill this gap by reviewing existing
strategies for multi-step ahead forecasting and comparing them in theoretical
and practical terms. To attain such an objective, we performed a large scale
comparison of these different strategies using a large experimental benchmark
(namely the 111 series from the NN5 forecasting competition). In addition, we
considered the effects of deseasonalization, input variable selection, and
forecast combination on these strategies and on multi-step ahead forecasting at
large. The following three findings appear to be consistently supported by the
experimental results: Multiple-Output strategies are the best performing
approaches, deseasonalization leads to uniformly improved forecast accuracy,
and input selection is more effective when performed in conjunction with
deseasonalization.
Sequence Transduction with Recurrent Neural Networks
Authors: Alex Graves
Date: 2012-11-14 00:00:00
Tasks: Machine Translation, Phoneme Recognition, Speech Recognition, text-to-speech, Text to Speech, Translation

Many machine learning tasks can be expressed as the transformation---or
\emph{transduction}---of input sequences into output sequences: speech
recognition, machine translation, protein secondary structure prediction and
text-to-speech to name but a few. One of the key challenges in sequence
transduction is learning to represent both the input and output sequences in a
way that is invariant to sequential distortions such as shrinking, stretching
and translating. Recurrent neural networks (RNNs) are a powerful sequence
learning architecture that has proven capable of learning such representations.
However RNNs traditionally require a pre-defined alignment between the input
and output sequences to perform transduction. This is a severe limitation since
\emph{finding} the alignment is the most difficult aspect of many sequence
transduction problems. Indeed, even determining the length of the output
sequence is often challenging. This paper introduces an end-to-end,
probabilistic sequence transduction system, based entirely on RNNs, that is in
principle able to transform any input sequence into any finite, discrete output
sequence. Experimental results for phoneme recognition are provided on the
TIMIT speech corpus.
Detecting multiword phrases in mathematical text corpora
Authors: Winfried Gödert
Date: 2012-10-02 00:00:00
Tasks: Information Retrieval, Retrieval

We present an approach for detecting multiword phrases in mathematical text
corpora. The method used is based on characteristic features of mathematical
terminology. It makes use of a software tool named Lingo which allows to
identify words by means of previously defined dictionaries for specific word
classes as adjectives, personal names or nouns. The detection of multiword
groups is done algorithmically. Possible advantages of the method for indexing
and information retrieval and conclusions for applying dictionary-based methods
of automatic indexing instead of stemming procedures are discussed.