pwc-archive/papers-with-abstracts huggingface dataset:
Towards a glaucoma risk index based on simulated hemodynamics from fundus images
Authors: José Ignacio Orlando, João Barbosa Breda, Karel van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant
Date: 2018-05-25 00:00:00

Glaucoma is the leading cause of irreversible but preventable blindness in
the world. Its major treatable risk factor is the intra-ocular pressure,
although other biomarkers are being explored to improve the understanding of
the pathophysiology of the disease. It has been recently observed that glaucoma
induces changes in the ocular hemodynamics. However, its effects on the
functional behavior of the retinal arterioles have not been studied yet. In
this paper we propose a first approach for characterizing those changes using
computational hemodynamics. The retinal blood flow is simulated using a 0D
model for a steady, incompressible non Newtonian fluid in rigid domains. The
simulation is performed on patient-specific arterial trees extracted from
fundus images. We also propose a novel feature representation technique to
comprise the outcomes of the simulation stage into a fixed length feature
vector that can be used for classification studies. Our experiments on a new
database of fundus images show that our approach is able to capture
representative changes in the hemodynamics of glaucomatous patients. Code and
data are publicly available in https://ignaciorlando.github.io.
Recurrent Relational Networks
Authors: Rasmus Berg Palm, Ulrich Paquet, Ole Winther
Date: 2017-11-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Diagnostic, Question Answering, Relational Reasoning

This paper is concerned with learning to solve tasks that require a chain of
interdependent steps of relational inference, like answering complex questions
about the relationships between objects, or solving puzzles where the smaller
elements of a solution mutually constrain each other. We introduce the
recurrent relational network, a general purpose module that operates on a graph
representation of objects. As a generalization of Santoro et al. [2017]'s
relational network, it can augment any neural network model with the capacity
to do many-step relational reasoning. We achieve state of the art results on
the bAbI textual question-answering dataset with the recurrent relational
network, consistently solving 20/20 tasks. As bAbI is not particularly
challenging from a relational reasoning point of view, we introduce
Pretty-CLEVR, a new diagnostic dataset for relational reasoning. In the
Pretty-CLEVR set-up, we can vary the question to control for the number of
relational reasoning steps that are required to obtain the answer. Using
Pretty-CLEVR, we probe the limitations of multi-layer perceptrons, relational
and recurrent relational networks. Finally, we show how recurrent relational
networks can learn to solve Sudoku puzzles from supervised training data, a
challenging task requiring upwards of 64 steps of relational reasoning. We
achieve state-of-the-art results amongst comparable methods by solving 96.6% of
the hardest Sudoku puzzles.
DeepProbLog: Neural Probabilistic Logic Programming
Authors: Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester, Luc De Raedt
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Learning, Program induction

We introduce DeepProbLog, a probabilistic logic programming language that
incorporates deep learning by means of neural predicates. We show how existing
inference and learning techniques can be adapted for the new language. Our
experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic
representations and inference, 1) program induction, 2) probabilistic (logic)
programming, and 3) (deep) learning from examples. To the best of our
knowledge, this work is the first to propose a framework where general-purpose
neural networks and expressive probabilistic-logical modeling and reasoning are
integrated in a way that exploits the full expressiveness and strengths of both
worlds and can be trained end-to-end based on examples.
CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation
Authors: Xiao Liu, Shengchuan Zhang, Hong Liu, Xin Liu, Cheng Deng, Rongrong Ji
Date: 2018-05-28 00:00:00
Tasks: Attribute, Decoder, Face Hallucination, Hallucination, Image-to-Image Translation, Style Transfer, Translation

In this paper, we aim at solving the multi-domain image-to-image translation
problem with a unified model in an unsupervised manner. The most successful
work in this area refers to StarGAN, which works well in tasks like face
attribute modulation. However, StarGAN is unable to match multiple translation
mappings when encountering general translations with very diverse domain
shifts. On the other hand, StarGAN adopts an Encoder-Decoder-Discriminator
(EDD) architecture, where the model is time-consuming and unstable to train. To
this end, we propose a Compact, effective, robust, and fast GAN model, termed
CerfGAN, to solve the above problem. In principle, CerfGAN contains a novel
component, i.e., a multi-class discriminator (MCD), which gives the model an
extremely powerful ability to match multiple translation mappings. To stabilize
the training process, MCD also plays a role of the encoder in CerfGAN, which
saves a lot of computation and memory costs. We perform extensive experiments
to verify the effectiveness of the proposed method. Quantitatively, CerfGAN is
demonstrated to handle a serial of image-to-image translation tasks including
style transfer, season transfer, face hallucination, etc, where the input
images are sampled from diverse domains. The comparisons to several recently
proposed approaches demonstrate the superiority and novelty of the proposed
method.
Learning to Play General Video-Games via an Object Embedding Network
Authors: William Woof, Ke Chen
Date: 2018-03-14 00:00:00
Tasks: Deep Reinforcement Learning, Object, Reinforcement Learning

Deep reinforcement learning (DRL) has proven to be an effective tool for
creating general video-game AI. However most current DRL video-game agents
learn end-to-end from the video-output of the game, which is superfluous for
many applications and creates a number of additional problems. More
importantly, directly working on pixel-based raw video data is substantially
distinct from what a human player does.In this paper, we present a novel method
which enables DRL agents to learn directly from object information. This is
obtained via use of an object embedding network (OEN) that compresses a set of
object feature vectors of different lengths into a single fixed-length unified
feature vector representing the current game-state and fulfills the DRL
simultaneously. We evaluate our OEN-based DRL agent by comparing to several
state-of-the-art approaches on a selection of games from the GVG-AI
Competition. Experimental results suggest that our object-based DRL agent
yields performance comparable to that of those approaches used in our
comparative study.
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Authors: Patrick Schwab, Djordje Miladinovic, Walter Karlen
Date: 2018-02-06 00:00:00
Tasks: Feature Importance, Mixture-of-Experts

Knowledge of the importance of input features towards decisions made by
machine-learning models is essential to increase our understanding of both the
models and the underlying data. Here, we present a new approach to estimating
feature importance with neural networks based on the idea of distributing the
features of interest among experts in an attentive mixture of experts (AME).
AMEs use attentive gating networks trained with a Granger-causal objective to
learn to jointly produce accurate predictions as well as estimates of feature
importance in a single model. Our experiments show (i) that the feature
importance estimates provided by AMEs compare favourably to those provided by
state-of-the-art methods, (ii) that AMEs are significantly faster at estimating
feature importance than existing methods, and (iii) that the associations
discovered by AMEs are consistent with those reported by domain experts.
Versatile Auxiliary Regressor with Generative Adversarial network (VAR+GAN)
Authors: Shabab Bazrafkan, Peter Corcoran
Date: 2018-05-28 00:00:00
Tasks: Face Generation, Generative Adversarial Network, regression

Being able to generate constrained samples is one of the most appealing
applications of the deep generators. Conditional generators are one of the
successful implementations of such models wherein the created samples are
constrained to a specific class. In this work, the application of these
networks is extended to regression problems wherein the conditional generator
is restrained to any continuous aspect of the data. A new loss function is
presented for the regression network and also implementations for generating
faces with any particular set of landmarks is provided.
Distributed Weight Consolidation: A Brain Segmentation Case Study
Authors: Patrick McClure, Charles Y. Zheng, Jakub R. Kaczmarzyk, John A. Lee, Satrajit S. Ghosh, Dylan Nielson, Peter Bandettini, Francisco Pereira
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Brain Segmentation, Continual Learning

Collecting the large datasets needed to train deep neural networks can be
very difficult, particularly for the many applications for which sharing and
pooling data is complicated by practical, ethical, or legal concerns. However,
it may be the case that derivative datasets or predictive models developed
within individual sites can be shared and combined with fewer restrictions.
Training on distributed data and combining the resulting networks is often
viewed as continual learning, but these methods require networks to be trained
sequentially. In this paper, we introduce distributed weight consolidation
(DWC), a continual learning method to consolidate the weights of separate
neural networks, each trained on an independent dataset. We evaluated DWC with
a brain segmentation case study, where we consolidated dilated convolutional
neural networks trained on independent structural magnetic resonance imaging
(sMRI) datasets from different sites. We found that DWC led to increased
performance on test sets from the different sites, while maintaining
generalization performance for a very large and completely independent
multi-site dataset, compared to an ensemble baseline.
A non-invertible cancelable fingerprint template generation based on ridge feature transformation
Authors: Rudresh Dwivedi, Somnath Dey
Date: 2018-05-28 00:00:00

In a biometric verification system, leakage of biometric data leads to
permanent identity loss since original biometric data is inherently linked to a
user. Further, various types of attacks on a biometric system may reveal the
original template and utility in other applications. To address these security
and privacy concerns cancelable biometric has been introduced. Cancelable
biometric constructs a protected template from the original biometric template
using transformation functions and performs the comparison between templates in
the transformed domain. Recent approaches towards cancelable fingerprint
generation either rely on aligning minutiae points with respect to singular
points (core/delta) or utilize the absolute coordinate positions of minutiae
points. In this paper, we propose a novel non-invertible ridge feature
transformation method to protect the original fingerprint template information.
The proposed method partitions the fingerprint region into a number of sectors
with reference to each minutia point employing a ridge-based co-ordinate
system. The nearest neighbor minutiae in each sector are identified, and
ridge-based features are computed. Further, a cancelable template is generated
by applying the Cantor pairing function followed by random projection. We have
evaluated our method with FVC2002, FVC2004 and FVC2006 databases. It is evident
from the experimental results that the proposed method outperforms existing
methods in the literature. Moreover, the security analysis demonstrates that
the proposed method fulfills the necessary requirements of non-invertibility,
revocability, and diversity with a minor performance degradation caused due to
cancelable transformation.
A Pragmatic AI Approach to Creating Artistic Visual Variations by Neural Style Transfer
Authors: Chaehan So
Date: 2018-05-28 00:00:00
Tasks: Style Transfer

On a constant quest for inspiration, designers can become more effective with
tools that facilitate their creative process and let them overcome design
fixation. This paper explores the practicality of applying neural style
transfer as an emerging design tool for generating creative digital content. To
this aim, the present work explores a well-documented neural style transfer
algorithm (Johnson 2016) in four experiments on four relevant visual
parameters: number of iterations, learning rate, total variation, content vs.
style weight. The results allow a pragmatic recommendation of parameter
configuration (number of iterations: 200 to 300, learning rate: 2e-1 to 4e-1,
total variation: 1e-4 to 1e-8, content weights vs. style weights: 50:100 to
200:100) that saves extensive experimentation time and lowers the technical
entry barrier. With this rule-of-thumb insight, visual designers can
effectively apply deep learning to create artistic visual variations of digital
content. This could enable designers to leverage AI for creating design works
as state-of-the-art.
Inducing Grammars with and for Neural Machine Translation
Authors: Ke Tran, Yonatan Bisk
Date: 2018-05-28 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Machine Translation, NMT, Translation

Machine translation systems require semantic knowledge and grammatical
understanding. Neural machine translation (NMT) systems often assume this
information is captured by an attention mechanism and a decoder that ensures
fluency. Recent work has shown that incorporating explicit syntax alleviates
the burden of modeling both types of knowledge. However, requiring parses is
expensive and does not explore the question of what syntax a model needs during
translation. To address both of these issues we introduce a model that
simultaneously translates while inducing dependency trees. In this way, we
leverage the benefits of structure while investigating what syntax NMT must
induce to maximize performance. We show that our dependency trees are 1.
language pair dependent and 2. improve translation quality.
A Stochastic Decoder for Neural Machine Translation
Authors: Philip Schulz, Wilker Aziz, Trevor Cohn
Date: 2018-05-28 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Machine Translation, Sentence, Translation, valid, Variational Inference

The process of translation is ambiguous, in that there are typically many
valid trans- lations for a given sentence. This gives rise to significant
variation in parallel cor- pora, however, most current models of machine
translation do not account for this variation, instead treating the prob- lem
as a deterministic process. To this end, we present a deep generative model of
machine translation which incorporates a chain of latent variables, in order to
ac- count for local lexical and syntactic varia- tion in parallel corpora. We
provide an in- depth analysis of the pitfalls encountered in variational
inference for training deep generative models. Experiments on sev- eral
different language pairs demonstrate that the model consistently improves over
strong baselines.
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
Authors: Christoph Reinders, Hanno Ackermann, Michael Ying Yang, Bodo Rosenhahn
Date: 2017-09-18 00:00:00
Tasks: Object Recognition, Transfer Learning

In recent years, data-driven methods have shown great success for extracting
information about the infrastructure in urban areas. These algorithms are
usually trained on large datasets consisting of thousands or millions of
labeled training examples. While large datasets have been published regarding
cars, for cyclists very few labeled data is available although appearance,
point of view, and positioning of even relevant objects differ. Unfortunately,
labeling data is costly and requires a huge amount of work. In this paper, we
thus address the problem of learning with very few labels. The aim is to
recognize particular traffic signs in crowdsourced data to collect information
which is of interest to cyclists. We propose a system for object recognition
that is trained with only 15 examples per class on average. To achieve this, we
combine the advantages of convolutional neural networks and random forests to
learn a patch-wise classifier. In the next step, we map the random forest to a
neural network and transform the classifier to a fully convolutional network.
Thereby, the processing of full images is significantly accelerated and
bounding boxes can be predicted. Finally, we integrate data of the Global
Positioning System (GPS) to localize the predictions on the map. In comparison
to Faster R-CNN and other networks for object recognition or algorithms for
transfer learning, we considerably reduce the required amount of labeled data.
We demonstrate good performance on the recognition of traffic signs for
cyclists as well as their localization in maps.
Approximating Real-Time Recurrent Learning with Random Kronecker Factors
Authors: Asier Mujika, Florian Meier, Angelika Steger
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Memorization

Despite all the impressive advances of recurrent neural networks, sequential
data is still in need of better modelling. Truncated backpropagation through
time (TBPTT), the learning algorithm most widely used in practice, suffers from
the truncation bias, which drastically limits its ability to learn long-term
dependencies.The Real-Time Recurrent Learning algorithm (RTRL) addresses this
issue, but its high computational requirements make it infeasible in practice.
The Unbiased Online Recurrent Optimization algorithm (UORO) approximates RTRL
with a smaller runtime and memory cost, but with the disadvantage of obtaining
noisy gradients that also limit its practical applicability. In this paper we
propose the Kronecker Factored RTRL (KF-RTRL) algorithm that uses a Kronecker
product decomposition to approximate the gradients for a large class of RNNs.
We show that KF-RTRL is an unbiased and memory efficient online learning
algorithm. Our theoretical analysis shows that, under reasonable assumptions,
the noise introduced by our algorithm is not only stable over time but also
asymptotically much smaller than the one of the UORO algorithm. We also confirm
these theoretical results experimentally. Further, we show empirically that the
KF-RTRL algorithm captures long-term dependencies and almost matches the
performance of TBPTT on real world tasks by training Recurrent Highway Networks
on a synthetic string memorization task and on the Penn TreeBank task,
respectively. These results indicate that RTRL based approaches might be a
promising future alternative to TBPTT.
Bayesian Learning with Wasserstein Barycenters
Authors: Julio Backhoff-Veraguas, Joaquin Fontbona, Gonzalo Rios, Felipe Tobar
Date: 2018-05-28 00:00:00
Tasks: Model Selection

We introduce and study a novel model-selection strategy for Bayesian learning, based on optimal transport, along with its associated predictive posterior law: the Wasserstein population barycenter of the posterior law over models. We first show how this estimator, termed Bayesian Wasserstein barycenter (BWB), arises naturally in a general, parameter-free Bayesian model-selection framework, when the considered Bayesian risk is the Wasserstein distance. Examples are given, illustrating how the BWB extends some classic parametric and non-parametric selection strategies. Furthermore, we also provide explicit conditions granting the existence and statistical consistency of the BWB, and discuss some of its general and specific properties, providing insights into its advantages compared to usual choices, such as the model average estimator. Finally, we illustrate how this estimator can be computed using the stochastic gradient descent (SGD) algorithm in Wasserstein space introduced in a companion paper arXiv:2201.04232v2 [math.OC], and provide a numerical example for experimental validation of the proposed method.
Sigsoftmax: Reanalysis of the Softmax Bottleneck
Authors: Sekitoshi Kanai, Yasuhiro Fujiwara, Yuki Yamanaka, Shuichi Adachi
Date: 2018-05-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Language Modeling, Language Modelling

Softmax is an output activation function for modeling categorical probability
distributions in many applications of deep learning. However, a recent study
revealed that softmax can be a bottleneck of representational capacity of
neural networks in language modeling (the softmax bottleneck). In this paper,
we propose an output activation function for breaking the softmax bottleneck
without additional parameters. We re-analyze the softmax bottleneck from the
perspective of the output set of log-softmax and identify the cause of the
softmax bottleneck. On the basis of this analysis, we propose sigsoftmax, which
is composed of a multiplication of an exponential function and sigmoid
function. Sigsoftmax can break the softmax bottleneck. The experiments on
language modeling demonstrate that sigsoftmax and mixture of sigsoftmax
outperform softmax and mixture of softmax, respectively.
UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish
Authors: Marloes Kuijper, Mike van Lenthe, Rik van Noord
Date: 2018-05-28 00:00:00
Proceeding: SEMEVAL 2018 6

The present study describes our submission to SemEval 2018 Task 1: Affect in
Tweets. Our Spanish-only approach aimed to demonstrate that it is beneficial to
automatically generate additional training data by (i) translating training
data from other languages and (ii) applying a semi-supervised learning method.
We find strong support for both approaches, with those models outperforming our
regular models in all subtasks. However, creating a stepwise ensemble of
different models as opposed to simply averaging did not result in an increase
in performance. We placed second (EI-Reg), second (EI-Oc), fourth (V-Reg) and
fifth (V-Oc) in the four Spanish subtasks we participated in.
Quadrature-based features for kernel approximation
Authors: Marina Munkhoeva, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
Date: 2018-02-11 00:00:00
Proceeding: ICLR 2018 1
Tasks: Numerical Integration

We consider the problem of improving kernel approximation via randomized
feature maps. These maps arise as Monte Carlo approximation to integral
representations of kernel functions and scale up kernel methods for larger
datasets. Based on an efficient numerical integration technique, we propose a
unifying approach that reinterprets the previous random features methods and
extends to better estimates of the kernel approximation. We derive the
convergence behaviour and conduct an extensive empirical study that supports
our hypothesis.