pwc-archive/papers-with-abstracts huggingface dataset:
Avoiding Latent Variable Collapse With Generative Skip Models
Authors: Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei
Date: 2018-07-12 00:00:00

Variational autoencoders learn distributions of high-dimensional data. They
model data with a deep latent-variable model and then fit the model by
maximizing a lower bound of the log marginal likelihood. VAEs can capture
complex distributions, but they can also suffer from an issue known as "latent
variable collapse," especially if the likelihood model is powerful.
Specifically, the lower bound involves an approximate posterior of the latent
variables; this posterior "collapses" when it is set equal to the prior, i.e.,
when the approximate posterior is independent of the data. While VAEs learn
good generative models, latent variable collapse prevents them from learning
useful representations. In this paper, we propose a simple new way to avoid
latent variable collapse by including skip connections in our generative model;
these connections enforce strong links between the latent variables and the
likelihood function. We study generative skip models both theoretically and
empirically. Theoretically, we prove that skip models increase the mutual
information between the observations and the inferred latent variables.
Empirically, we study images (MNIST and Omniglot) and text (Yahoo). Compared to
existing VAE architectures, we show that generative skip models maintain
similar predictive performance but lead to less collapse and provide more
meaningful representations of the data.
Hybrid Temporal Situation Calculus
Authors: Vitaliy Batusov, Giuseppe De Giacomo, Mikhail Soutchanski
Date: 2018-07-12 00:00:00

The ability to model continuous change in Reiter's temporal situation
calculus action theories has attracted a lot of interest. In this paper, we
propose a new development of his approach, which is directly inspired by hybrid
systems in control theory. Specifically, while keeping the foundations of
Reiter's axiomatization, we propose an elegant extension of his approach by
adding a time argument to all fluents that represent continuous change.
Thereby, we insure that change can happen not only because of actions, but also
due to the passage of time. We present a systematic methodology to derive, from
simple premises, a new group of axioms which specify how continuous fluents
change over time within a situation. We study regression for our new temporal
basic action theories and demonstrate what reasoning problems can be solved.
Finally, we formally show that our temporal basic action theories indeed
capture hybrid automata.
CADDY Underwater Stereo-Vision Dataset for Human-Robot Interaction (HRI) in the Context of Diver Activities
Authors: Arturo Gomez Chavez, Andrea Ranieri, Davide Chiarella, Enrica Zereik, Anja Babić, Andreas Birk
Date: 2018-07-12 00:00:00
Tasks: General Classification, object-detection, Object Detection, Pose Estimation

In this article we present a novel underwater dataset collected from several
field trials within the EU FP7 project "Cognitive autonomous diving buddy
(CADDY)", where an Autonomous Underwater Vehicle (AUV) was used to interact
with divers and monitor their activities. To our knowledge, this is one of the
first efforts to collect a large dataset in underwater environments targeting
object classification, segmentation and human pose estimation tasks. The first
part of the dataset contains stereo camera recordings (~10K) of divers
performing hand gestures to communicate and interact with an AUV in different
environmental conditions. These gestures samples serve to test the robustness
of object detection and classification algorithms against underwater image
distortions i.e., color attenuation and light backscatter. The second part
includes stereo footage (~12.7K) of divers free-swimming in front of the AUV,
along with synchronized IMUs measurements located throughout the diver's suit
(DiverNet) which serve as ground-truth for human pose and tracking methods. In
both cases, these rectified images allow investigation of 3D representation and
reasoning pipelines from low-texture targets commonly present in underwater
scenarios. In this paper we describe our recording platform, sensor calibration
procedure plus the data format and the utilities provided to use the dataset.
A feature agnostic approach for glaucoma detection in OCT volumes
Authors: Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa, Gadi Wollstein, Joel S. Schuman, Rahil Garnavi
Date: 2018-07-12 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly used for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have utilized segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.
A game-theoretic approach to timeline-based planning with uncertainty
Authors: Nicola Gigante, Angelo Montanari, Marta Cialdea Mayer, Andrea Orlandini, Mark Reynolds
Date: 2018-07-12 00:00:00

In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints. A distinguishing feature of timeline-based planning systems is the ability to integrate planning with execution by synthesising control strategies for flexible plans. However, flexible plans can only represent temporal uncertainty, while more complex forms of nondeterminism are needed to deal with a wider range of realistic problems. In this paper, we propose a novel game-theoretic approach to timeline-based planning problems, generalising the state of the art while uniformly handling temporal uncertainty and nondeterminism. We define a general concept of timeline-based game and we show that the notion of winning strategy for these games is strictly more general than that of control strategy for dynamically controllable flexible plans. Moreover, we show that the problem of establishing the existence of such winning strategies is decidable using a doubly exponential amount of space.
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
Authors: Yandong Wen, Mahmoud  Al Ismail, Weiyang Liu, Bhiksha Raj, Rita Singh
Date: 2018-07-12 00:00:00
Proceeding: ICLR 2019 5

We propose a novel framework, called Disjoint Mapping Network (DIMNet), for
cross-modal biometric matching, in particular of voices and faces. Different
from the existing methods, DIMNet does not explicitly learn the joint
relationship between the modalities. Instead, DIMNet learns a shared
representation for different modalities by mapping them individually to their
common covariates. These shared representations can then be used to find the
correspondences between the modalities. We show empirically that DIMNet is able
to achieve better performance than other current methods, with the additional
benefits of being conceptually simpler and less data-intensive.
Optimal Strategies for Matching and Retrieval Problems by Comparing Covariates
Authors: Yandong Wen, Mahmoud  Al Ismail, Bhiksha Raj, Rita Singh
Date: 2018-07-12 00:00:00
Tasks: Retrieval

In many retrieval problems, where we must retrieve one or more entries from a
gallery in response to a probe, it is common practice to learn to do by
directly comparing the probe and gallery entries to one another. In many
situations the gallery and probe have common covariates -- external variables
that are common to both. In principle it is possible to perform the retrieval
based merely on these covariates. The process, however, becomes gated by our
ability to recognize the covariates for the probe and gallery entries
correctly.
  In this paper we analyze optimal strategies for retrieval based only on
matching covariates, when the recognition of the covariates is itself
inaccurate. We investigate multiple problems: recovering one item from a
gallery of $N$ entries, matching pairs of instances, and retrieval from large
collections. We verify our analytical formulae through experiments to verify
their correctness in practical settings.
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Authors: Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell
Date: 2018-07-12 00:00:00

We present a non-parametric Bayesian latent variable model capable of
learning dependency structures across dimensions in a multivariate setting. Our
approach is based on flexible Gaussian process priors for the generative
mappings and interchangeable Dirichlet process priors to learn the structure.
The introduction of the Dirichlet process as a specific structural prior allows
our model to circumvent issues associated with previous Gaussian process latent
variable models. Inference is performed by deriving an efficient variational
bound on the marginal log-likelihood on the model.
CTAP: Complementary Temporal Action Proposal Generation
Authors: Jiyang Gao, Kan Chen, Ram Nevatia
Date: 2018-07-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Temporal Action Proposal Generation

Temporal action proposal generation is an important task, akin to object
proposals, temporal action proposals are intended to capture "clips" or
temporal intervals in videos that are likely to contain an action. Previous
methods can be divided to two groups: sliding window ranking and actionness
score grouping. Sliding windows uniformly cover all segments in videos, but the
temporal boundaries are imprecise; grouping based method may have more precise
boundaries but it may omit some proposals when the quality of actionness score
is low. Based on the complementary characteristics of these two methods, we
propose a novel Complementary Temporal Action Proposal (CTAP) generator.
Specifically, we apply a Proposal-level Actionness Trustworthiness Estimator
(PATE) on the sliding windows proposals to generate the probabilities
indicating whether the actions can be correctly detected by actionness scores,
the windows with high scores are collected. The collected sliding windows and
actionness proposals are then processed by a temporal convolutional neural
network for proposal ranking and boundary adjustment. CTAP outperforms
state-of-the-art methods on average recall (AR) by a large margin on THUMOS-14
and ActivityNet 1.3 datasets. We further apply CTAP as a proposal generation
method in an existing action detector, and show consistent significant
improvements.
Latent Transformations for Object View Points Synthesis
Authors: Sangpil Kim, Nick Winovich, Guang Lin, Karthik Ramani
Date: 2018-07-12 00:00:00
Tasks: Decoder, Object

We propose a fully-convolutional conditional generative model, the latent
transformation neural network (LTNN), capable of view synthesis using a
light-weight neural network suited for real-time applications. In contrast to
existing conditional generative models which incorporate conditioning
information via concatenation, we introduce a dedicated network component, the
conditional transformation unit (CTU), designed to learn the latent space
transformations corresponding to specified target views. In addition, a
consistency loss term is defined to guide the network toward learning the
desired latent space mappings, a task-divided decoder is constructed to refine
the quality of generated views, and an adaptive discriminator is introduced to
improve the adversarial training process. The generality of the proposed
methodology is demonstrated on a collection of three diverse tasks: multi-view
reconstruction on real hand depth images, view synthesis of real and synthetic
faces, and the rotation of rigid objects. The proposed model is shown to exceed
state-of-the-art results in each category while simultaneously achieving a
reduction in the computational demand required for inference by 30% on average.
Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation
Authors: Allen Lu, Nripesh Parajuli, Maria Zontak, John Stendahl, Kevinminh Ta, Zhao Liu, Nabil Boutagy, Geng-Shi Jeng, Imran Alkhalil, Lawrence H. Staib, Matthew O'Donnell, Albert J. Sinusas, James S. Duncan
Date: 2018-07-12 00:00:00
Tasks: Domain Adaptation, Motion Estimation

Reliable motion estimation and strain analysis using 3D+time echocardiography
(4DE) for localization and characterization of myocardial injury is valuable
for early detection and targeted interventions. However, motion estimation is
difficult due to the low-SNR that stems from the inherent image properties of
4DE, and intelligent regularization is critical for producing reliable motion
estimates. In this work, we incorporated the notion of domain adaptation into a
supervised neural network regularization framework. We first propose an
unsupervised autoencoder network with biomechanical constraints for learning a
latent representation that is shown to have more physiologically plausible
displacements. We extended this framework to include a supervised loss term on
synthetic data and showed the effects of biomechanical constraints on the
network's ability for domain adaptation. We validated both the autoencoder and
semi-supervised regularization method on in vivo data with implanted
sonomicrometers. Finally, we showed the ability of our semi-supervised learning
regularization approach to identify infarcted regions using estimated regional
strain maps with good agreement to manually traced infarct regions from
postmortem excised hearts.
Practical Obstacles to Deploying Active Learning
Authors: David Lowell, Zachary C. Lipton, Byron C. Wallace
Date: 2018-07-12 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Active Learning, Text Classification

Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.
Feature Selection for Gender Classification in TUIK Life Satisfaction Survey
Authors: Adil Çoban, Ilhan Tarımer
Date: 2018-07-12 00:00:00
Tasks: Attribute, Classification, feature selection, Gender Classification, General Classification, Survey

As known, attribute selection is a method that is used before the
classification of data mining. In this study, a new data set has been created
by using attributes expressing overall satisfaction in Turkey Statistical
Institute (TSI) Life Satisfaction Survey dataset. Attributes are sorted by
Ranking search method using attribute selection algorithms in a data mining
application. These selected attributes were subjected to a classification test
with Naive Bayes and Random Forest from machine learning algorithms. The
feature selection algorithms are compared according to the number of attributes
selected and the classification accuracy rates achievable with them. In this
study, which is aimed at reducing the dataset volume, the best classification
result comes up with 3 attributes selected by the Chi2 algorithm. The best
classification rate was 73% with the Random Forest classification algorithm.
Hydranet: Data Augmentation for Regression Neural Networks
Authors: Florian Dubost, Gerda Bortsova, Hieab Adams, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
Date: 2018-07-12 00:00:00
Tasks: Data Augmentation, Medical Image Analysis, regression

Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scarce. We propose a novel data-augmentation method to regularize neural network regressors that learn from a single global label per image. The principle of the method is to create new samples by recombining existing ones. We demonstrate the performance of our algorithm on two tasks: estimation of the number of enlarged perivascular spaces in the basal ganglia, and estimation of white matter hyperintensities volume. We show that the proposed method improves the performance over more basic data augmentation. The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0.73 on the first task and 0.84 on the second task, only using between 25 and 30 scans with a single global label per scan for training. With the same number of training scans, more conventional data augmentation methods could only reach intraclass correlation coefficients of 0.68 on the first task, and 0.79 on the second task.
Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate
Authors: Christo Kirov, Ryan Cotterell
Date: 2018-07-12 00:00:00
Proceeding: TACL 2018 1
Tasks: Decoder

Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter, Pinker & Prince (1988) presented a comprehensive rebuttal of many of Rumelhart and McClelland's claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland (1986) model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince's criticisms without requiring any simplication of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a re-examination of their utility in linguistic and cognitive modeling.
Improving on Q & A Recurrent Neural Networks Using Noun-Tagging
Authors: Erik Partridge, Jack Sklar, Omar El-lakany
Date: 2018-07-12 00:00:00
Tasks: Prediction, Relation, Relation Prediction

Often, more time is spent on finding a model that works well, rather than
tuning the model and working directly with the dataset. Our research began as
an attempt to improve upon a simple Recurrent Neural Network for answering
"simple" first-order questions (QA-RNN), developed by Ferhan Ture and Oliver
Jojic, from Comcast Labs, using the SimpleQuestions dataset. Their baseline
model, a bidirectional, 2-layer LSTM RNN and a GRU RNN, have accuracies of 0.94
and 0.90, for entity detection and relation prediction, respectively. We fine
tuned these models by doing substantial hyper-parameter tuning, getting
resulting accuracies of 0.70 and 0.80, for entity detection and relation
prediction, respectively. An accuracy of 0.984 was obtained on entity detection
using a 1-layer LSTM, where preprocessing was done by removing all words not
part of a noun chunk from the question. 100% of the dataset was available for
relation prediction, but only 20% of the dataset, was available for entity
detection, which we believe to be much of the reason for our initial
difficulties in replicating their result, despite the fact we were able to
improve on their entity detection results.
Metalearning with Hebbian Fast Weights
Authors: Tsendsuren Munkhdalai, Adam Trischler
Date: 2018-07-12 00:00:00
Tasks: One-Shot Learning

We unify recent neural approaches to one-shot learning with older ideas of
associative memory in a model for metalearning. Our model learns jointly to
represent data and to bind class labels to representations in a single shot. It
builds representations via slow weights, learned across tasks through SGD,
while fast weights constructed by a Hebbian learning rule implement one-shot
binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank
one-shot learning benchmarks, our model achieves state-of-the-art results.
Unsupervised nonparametric detection of unknown objects in noisy images based on percolation theory
Authors: Mikhail A. Langovoy, Olaf Wittich, Patrick Laurie Davies
Date: 2011-02-24 00:00:00

We develop an unsupervised, nonparametric, and scalable statistical learning
method for detection of unknown objects in noisy images. The method uses
results from percolation theory and random graph theory. We present an
algorithm that allows to detect objects of unknown shapes and sizes in the
presence of nonparametric noise of unknown level. The noise density is assumed
to be unknown and can be very irregular. The algorithm has linear complexity
and exponential accuracy and is appropriate for real-time systems. We prove
strong consistency and scalability of our method in this setup with minimal
assumptions.
Fast Estimation of Causal Interactions using Wold Processes
Authors: Flavio Figueiredo, Guilherme Borges, Pedro O. S. Vaz de Melo, Renato M. Assunção
Date: 2018-07-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Point Processes

We here focus on the task of learning Granger causality matrices for
multivariate point processes. In order to accomplish this task, our work is the
first to explore the use of Wold processes. By doing so, we are able to develop
asymptotically fast MCMC learning algorithms. With $N$ being the total number
of events and $K$ the number of processes, our learning algorithm has a
$O(N(\,\log(N)\,+\,\log(K)))$ cost per iteration. This is much faster than the
$O(N^3\,K^2)$ or $O(K^3)$ for the state of the art. Our approach, called
GrangerBusca, is validated on nine datasets. This is an advance in relation to
most prior efforts which focus mostly on subsets of the Memetracker data.
Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10)
than the state of the art for the commonly explored subsets Memetracker. Due to
GrangerBusca's much lower training complexity, our approach is the only one
able to train models for larger, full, sets of data.