pwc-archive/papers-with-abstracts huggingface dataset:
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
Authors: Fabio Ferreira, Jonas Rothfuss, Eren Erdal Aksoy, You Zhou, Tamim Asfour
Date: 2018-07-02 00:00:00

We release two artificial datasets, Simulated Flying Shapes and Simulated
Planar Manipulator that allow to test the learning ability of video processing
systems. In particular, the dataset is meant as a tool which allows to easily
assess the sanity of deep neural network models that aim to encode, reconstruct
or predict video frame sequences. The datasets each consist of 90000 videos.
The Simulated Flying Shapes dataset comprises scenes showing two objects of
equal shape (rectangle, triangle and circle) and size in which one object
approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF
planar manipulator that executes a pick-and-place task in which it has to place
a size-varying circle on a squared platform. Different from other widely used
datasets such as moving MNIST [1], [2], the two presented datasets involve
goal-oriented tasks (e.g. the manipulator grasping an object and placing it on
a platform), rather than showing random movements. This makes our datasets more
suitable for testing prediction capabilities and the learning of sophisticated
motions by a machine learning model. This technical document aims at providing
an introduction into the usage of both datasets.
Learning to Personalize in Appearance-Based Gaze Tracking
Authors: Erik Lindén, Jonas Sjöstrand, Alexandre Proutiere
Date: 2018-07-02 00:00:00
Tasks: Gaze Estimation

Personal variations severely limit the performance of appearance-based gaze tracking. Adapting to these variations using standard neural network model adaptation methods is difficult. The problems range from overfitting, due to small amounts of training data, to underfitting, due to restrictive model architectures. We tackle these problems by introducing the SPatial Adaptive GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional latent parameter space, SPAZE provides just enough adaptability to capture the range of personal variations without being prone to overfitting. Calibrating SPAZE for a new person reduces to solving a small optimization problem. SPAZE achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze, improving on the state-of-the-art by 14 %. We contribute to gaze tracking research by empirically showing that personal variations are well-modeled as a 3-dimensional latent parameter space for each eye. We show that this low-dimensionality is expected by examining model-based approaches to gaze tracking. We also show that accurate head pose-free gaze tracking is possible.
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation
Authors: Mingyang Jiang, Yiran Wu, Tianqi Zhao, Zelin Zhao, Cewu Lu
Date: 2018-07-02 00:00:00
Tasks: Point Cloud Segmentation, Semantic Segmentation

Recently, 3D understanding research sheds light on extracting features from
point cloud directly, which requires effective shape pattern description of
point clouds. Inspired by the outstanding 2D shape descriptor SIFT, we design a
module called PointSIFT that encodes information of different orientations and
is adaptive to scale of shape. Specifically, an orientation-encoding unit is
designed to describe eight crucial orientations, and multi-scale representation
is achieved by stacking several orientation-encoding units. PointSIFT module
can be integrated into various PointNet-based architecture to improve the
representation ability. Extensive experiments show our PointSIFT-based
framework outperforms state-of-the-art method on standard benchmark datasets.
The code and trained model will be published accompanied by this paper.
A Neural Approach to Language Variety Translation
Authors: Marta R. Costa-jussà, Marcos Zampieri, Santanu Pal
Date: 2018-07-02 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Translation

In this paper we present the first neural-based machine translation system
trained to translate between standard national varieties of the same language.
We take the pair Brazilian - European Portuguese as an example and compare the
performance of this method to a phrase-based statistical machine translation
system. We report a performance improvement of 0.9 BLEU points in translating
from European to Brazilian Portuguese and 0.2 BLEU points when translating in
the opposite direction. We also carried out a human evaluation experiment with
native speakers of Brazilian Portuguese which indicates that humans prefer the
output produced by the neural-based system in comparison to the statistical
system.
Block-Value Symmetries in Probabilistic Graphical Models
Authors: Gagan Madan, Ankit Anand, Mausam, Parag Singla
Date: 2018-07-02 00:00:00

One popular way for lifted inference in probabilistic graphical models is to
first merge symmetric states into a single cluster (orbit) and then use these
for downstream inference, via variations of orbital MCMC [Niepert, 2012]. These
orbits are represented compactly using permutations over variables, and
variable-value (VV) pairs, but they can miss several state symmetries in a
domain.
  We define the notion of permutations over block-value (BV) pairs, where a
block is a set of variables. BV strictly generalizes VV symmetries, and can
compute many more symmetries for increasing block sizes. To operationalize use
of BV permutations in lifted inference, we describe 1) an algorithm to compute
BV permutations given a block partition of the variables, 2) BV-MCMC, an
extension of orbital MCMC that can sample from BV orbits, and 3) a heuristic to
suggest good block partitions. Our experiments show that BV-MCMC can mix much
faster compared to vanilla MCMC and orbital MCMC.
Mammography Dual View Mass Correspondence
Authors: Shaked Perek, Alon Hazan, Ella Barkan, Ayelet Akselrod-Ballin
Date: 2018-07-02 00:00:00
Tasks: Template Matching, Transfer Learning

Standard breast cancer screening involves the acquisition of two mammography
X-ray projections for each breast. Typically, a comparison of both views
supports the challenging task of tumor detection and localization. We introduce
a deep learning, patch-based Siamese network for lesion matching in dual-view
mammography. Our locally-fitted approach generates a joint patch pair
representation and comparison with a shared configuration between the two
views. We performed a comprehensive set of experiments with the network on
standard datasets, among them the large Digital Database for Screening
Mammography (DDSM). We analyzed the effect of transfer learning with the
network between different types of datasets and compared the network-based
matching to using Euclidean distance by template matching. Finally, we
evaluated the contribution of the matching network in a full detection
pipeline. Experimental results demonstrate the promise of improved detection
accuracy using our approach.
Adaptation to Easy Data in Prediction with Limited Advice
Authors: Tobias Sommer Thune, Yevgeny Seldin
Date: 2018-07-02 00:00:00
Proceeding: NeurIPS 2018 12

We derive an online learning algorithm with improved regret guarantees for `easy' loss sequences. We consider two types of `easiness': (a) stochastic loss sequences and (b) adversarial loss sequences with small effective range of the losses. While a number of algorithms have been proposed for exploiting small effective range in the full information setting, Gerchinovitz and Lattimore [2016] have shown the impossibility of regret scaling with the effective range of the losses in the bandit setting. We show that just one additional observation per round is sufficient to circumvent the impossibility result. The proposed Second Order Difference Adjustments (SODA) algorithm requires no prior knowledge of the effective range of the losses, $\varepsilon$, and achieves an $O(\varepsilon \sqrt{KT \ln K}) + \tilde{O}(\varepsilon K \sqrt[4]{T})$ expected regret guarantee, where $T$ is the time horizon and $K$ is the number of actions. The scaling with the effective loss range is achieved under significantly weaker assumptions than those made by Cesa-Bianchi and Shamir [2018] in an earlier attempt to circumvent the impossibility result. We also provide a regret lower bound of $\Omega(\varepsilon\sqrt{T K})$, which almost matches the upper bound. In addition, we show that in the stochastic setting SODA achieves an $O\left(\sum_{a:\Delta_a>0} \frac{K^3 \varepsilon^2}{\Delta_a}\right)$ pseudo-regret bound that holds simultaneously with the adversarial regret guarantee. In other words, SODA is safe against an unrestricted oblivious adversary and provides improved regret guarantees for at least two different types of `easiness' simultaneously.
Knowledge Compilation with Continuous Random Variables and its Application in Hybrid Probabilistic Logic Programming
Authors: Pedro Zuidberg Dos Martires, Anton Dries, Luc De Raedt
Date: 2018-07-02 00:00:00

In probabilistic reasoning, the traditionally discrete domain has been
elevated to the hybrid domain encompassing additionally continuous random
variables. Inference in the hybrid domain, however, usually necessitates to
condone trade-offs on either the inference on discrete or continuous random
variables. We introduce a novel approach based on weighted model integration
and algebraic model counting that circumvents these trade-offs. We then show
how it supports knowledge compilation and exact probabilistic inference.
Moreover, we introduce the hybrid probabilistic logic programming language
HAL-ProbLog, an extension of ProbLog, to which we apply our inference approach.
Multi-modal Egocentric Activity Recognition using Audio-Visual Features
Authors: Mehmet Ali Arabaci, Fatih Özkan, Elif Surer, Peter Jančovič, Alptekin Temizel
Date: 2018-07-02 00:00:00
Tasks: Activity Recognition, Egocentric Activity Recognition, Optical Flow Estimation

Egocentric activity recognition in first-person videos has an increasing importance with a variety of applications such as lifelogging, summarization, assisted-living and activity tracking. Existing methods for this task are based on interpretation of various sensor information using pre-determined weights for each feature. In this work, we propose a new framework for egocentric activity recognition problem based on combining audio-visual features with multi-kernel learning (MKL) and multi-kernel boosting (MKBoost). For that purpose, firstly grid optical-flow, virtual-inertia feature, log-covariance, cuboid are extracted from the video. The audio signal is characterized using a "supervector", obtained based on Gaussian mixture modelling of frame-level features, followed by a maximum a-posteriori adaptation. Then, the extracted multi-modal features are adaptively fused by MKL classifiers in which both the feature and kernel selection/weighing and recognition tasks are performed together. The proposed framework was evaluated on a number of egocentric datasets. The results showed that using multi-modal features with MKL outperforms the existing methods.
Crowd Counting using Deep Recurrent Spatial-Aware Network
Authors: Lingbo Liu, Hongjun Wang, Guanbin Li, Wanli Ouyang, Liang Lin
Date: 2018-07-02 00:00:00
Tasks: Crowd Counting, Management

Crowd counting from unconstrained scene images is a crucial task in many
real-world applications like urban surveillance and management, but it is
greatly challenged by the camera's perspective that causes huge appearance
variations in people's scales and rotations. Conventional methods address such
challenges by resorting to fixed multi-scale architectures that are often
unable to cover the largely varied scales while ignoring the rotation
variations. In this paper, we propose a unified neural network framework, named
Deep Recurrent Spatial-Aware Network, which adaptively addresses the two issues
in a learnable spatial transform module with a region-wise refinement process.
Specifically, our framework incorporates a Recurrent Spatial-Aware Refinement
(RSAR) module iteratively conducting two components: i) a Spatial Transformer
Network that dynamically locates an attentional region from the crowd density
map and transforms it to the suitable scale and rotation for optimal crowd
estimation; ii) a Local Refinement Network that refines the density map of the
attended region with residual learning. Extensive experiments on four
challenging benchmarks show the effectiveness of our approach. Specifically,
comparing with the existing best-performing methods, we achieve an improvement
of 12% on the largest dataset WorldExpo'10 and 22.8% on the most challenging
dataset UCF_CC_50.
A Pulmonary Nodule Detection Model Based on Progressive Resolution and Hierarchical Saliency
Authors: Jun-Jie Zhang, Yong Xia, Yanning Zhang
Date: 2018-07-02 00:00:00

Detection of pulmonary nodules on chest CT is an essential step in the early
diagnosis of lung cancer, which is critical for best patient care. Although a
number of computer-aided nodule detection methods have been published in the
literature, these methods still have two major drawbacks: missing out true
nodules during the detection of nodule candidates and less-accurate
identification of nodules from non-nodule. In this paper, we propose an
automated pulmonary nodule detection algorithm that jointly combines
progressive resolution and hierarchical saliency. Specifically, we design a 3D
progressive resolution-based densely dilated FCN, namely the progressive
resolution network (PRN), to detect nodule candidates inside the lung, and
construct a densely dilated 3D CNN with hierarchical saliency, namely the
hierarchical saliency network (HSN), to simultaneously identify genuine nodules
from those candidates and estimate the diameters of nodules. We evaluated our
algorithm on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and
achieved a state-of-the-art detection score. Our results suggest that the
proposed algorithm can effectively detect pulmonary nodules on chest CT and
accurately estimate their diameters.
Logical Explanations for Deep Relational Machines Using Relevance Information
Authors: Ashwin Srinivasan, Lovekesh Vig, Michael Bain
Date: 2018-07-02 00:00:00
Tasks: Inductive logic programming

Our interest in this paper is in the construction of symbolic explanations
for predictions made by a deep neural network. We will focus attention on deep
relational machines (DRMs, first proposed by H. Lodhi). A DRM is a deep network
in which the input layer consists of Boolean-valued functions (features) that
are defined in terms of relations provided as domain, or background, knowledge.
Our DRMs differ from those proposed by Lodhi, which use an Inductive Logic
Programming (ILP) engine to first select features (we use random selections
from a space of features that satisfies some approximate constraints on logical
relevance and non-redundancy). But why do the DRMs predict what they do? One
way of answering this is the LIME setting, in which readable proxies for a
black-box predictor. The proxies are intended only to model the predictions of
the black-box in local regions of the instance-space. But readability alone may
not enough: to be understandable, the local models must use relevant concepts
in an meaningful manner. We investigate the use of a Bayes-like approach to
identify logical proxies for local predictions of a DRM. We show: (a) DRM's
with our randomised propositionalization method achieve state-of-the-art
predictive performance; (b) Models in first-order logic can approximate the
DRM's prediction closely in a small local region; and (c) Expert-provided
relevance information can play the role of a prior to distinguish between
logical explanations that perform equivalently on prediction alone.
Lifted Marginal MAP Inference
Authors: Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, Parag Singla
Date: 2018-07-02 00:00:00

Lifted inference reduces the complexity of inference in relational
probabilistic models by identifying groups of constants (or atoms) which behave
symmetric to each other. A number of techniques have been proposed in the
literature for lifting marginal as well MAP inference. We present the first
application of lifting rules for marginal-MAP (MMAP), an important inference
problem in models having latent (random) variables. Our main contribution is
two fold: (1) we define a new equivalence class of (logical) variables, called
Single Occurrence for MAX (SOM), and show that solution lies at extreme with
respect to the SOM variables, i.e., predicate groundings differing only in the
instantiation of the SOM variables take the same truth value (2) we define a
sub-class {\em SOM-R} (SOM Reduce) and exploit properties of extreme
assignments to show that MMAP inference can be performed by reducing the domain
of SOM-R variables to a single constant.We refer to our lifting technique as
the {\em SOM-R} rule for lifted MMAP. Combined with existing rules such as
decomposer and binomial, this results in a powerful framework for lifted MMAP.
Experiments on three benchmark domains show significant gains in both time and
memory compared to ground inference as well as lifted approaches not using
SOM-R.
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
Authors: Jasper Linmans, Jim Winkens, Bastiaan S. Veeling, Taco S. Cohen, Max Welling
Date: 2018-07-02 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

We propose a semantic segmentation model that exploits rotation and
reflection symmetries. We demonstrate significant gains in sample efficiency
due to increased weight sharing, as well as improvements in robustness to
symmetry transformations. The group equivariant CNN framework is extended for
segmentation by introducing a new equivariant (G->Z2)-convolution that
transforms feature maps on a group to planar feature maps. Also, equivariant
transposed convolution is formulated for up-sampling in an encoder-decoder
network. To demonstrate improvements in sample efficiency we evaluate on
multiple data regimes of a rotation-equivariant segmentation task: cancer
metastases detection in histopathology images. We further show the
effectiveness of exploiting more symmetries by varying the size of the group.
Classifying neuromorphic data using a deep learning framework for image classification
Authors: Roshan Gopalakrishnan, Yansong Chua, Laxmi R. Iyer
Date: 2018-07-02 00:00:00
Tasks: Benchmarking, Deep Learning, General Classification, image-classification, Image Classification

In the field of artificial intelligence, neuromorphic computing has been
around for several decades. Deep learning has however made much recent progress
such that it consistently outperforms neuromorphic learning algorithms in
classification tasks in terms of accuracy. Specifically in the field of image
classification, neuromorphic computing has been traditionally using either the
temporal or rate code for encoding static images in datasets into spike trains.
It is only till recently, that neuromorphic vision sensors are widely used by
the neuromorphic research community, and provides an alternative to such
encoding methods. Since then, several neuromorphic datasets as obtained by
applying such sensors on image datasets (e.g. the neuromorphic CALTECH 101)
have been introduced. These data are encoded in spike trains and hence seem
ideal for benchmarking of neuromorphic learning algorithms. Specifically, we
train a deep learning framework used for image classification on the CALTECH
101 and a collapsed version of the neuromorphic CALTECH 101 datasets. We
obtained an accuracy of 91.66% and 78.01% for the CALTECH 101 and neuromorphic
CALTECH 101 datasets respectively. For CALTECH 101, our accuracy is close to
the best reported accuracy, while for neuromorphic CALTECH 101, it outperforms
the last best reported accuracy by over 10%. This raises the question of the
suitability of such datasets as benchmarks for neuromorphic learning
algorithms.
The Interplay between Lexical Resources and Natural Language Processing
Authors: Jose Camacho-Collados, Luis Espinosa-Anke, Mohammad Taher Pilehvar
Date: 2018-07-02 00:00:00
Proceeding: NAACL 2018 6
Tasks: Common Sense Reasoning

Incorporating linguistic, world and common sense knowledge into AI/NLP
systems is currently an important research area, with several open problems and
challenges. At the same time, processing and storing this knowledge in lexical
resources is not a straightforward task. This tutorial proposes to address
these complementary goals from two methodological perspectives: the use of NLP
methods to help the process of constructing and enriching lexical resources and
the use of lexical resources for improving NLP applications. Two main types of
audience can benefit from this tutorial: those working on language resources
who are interested in becoming acquainted with automatic NLP techniques, with
the end goal of speeding and/or easing up the process of resource curation; and
on the other hand, researchers in NLP who would like to benefit from the
knowledge of lexical resources to improve their systems and models. The slides
of the tutorial are available at https://bitbucket.org/luisespinosa/lr-nlp/
Inference, Learning, and Population Size: Projectivity for SRL Models
Authors: Manfred Jaeger, Oliver Schulte
Date: 2018-07-02 00:00:00

A subtle difference between propositional and relational data is that in many
relational models, marginal probabilities depend on the population or domain
size. This paper connects the dependence on population size to the classic
notion of projectivity from statistical theory: Projectivity implies that
relational predictions are robust with respect to changes in domain size. We
discuss projectivity for a number of common SRL systems, and identify syntactic
fragments that are guaranteed to yield projective models. The syntactic
conditions are restrictive, which suggests that projectivity is difficult to
achieve in SRL, and care must be taken when working with different domain
sizes.
Weight-importance sparse training in keyword spotting
Authors: Sihao Xue, Zhenyi Ying, Fan Mo, Min Wang, Jue Sun
Date: 2018-07-02 00:00:00
Tasks: Keyword Spotting, speech-recognition, Speech Recognition

Large size models are implemented in recently ASR system to deal with complex
speech recognition problems. The num- ber of parameters in these models makes
them hard to deploy, especially on some resource-short devices such as car
tablet. Besides this, at most of time, ASR system is used to deal with
real-time problem such as keyword spotting (KWS). It is contradictory to the
fact that large model requires long com- putation time. To deal with this
problem, we apply some sparse algo- rithms to reduces number of parameters in
some widely used models, Deep Neural Network (DNN) KWS, which requires real
short computation time. We can prune more than 90 % even 95% of parameters in
the model with tiny effect decline. And the sparse model performs better than
baseline models which has same order number of parameters. Besides this, sparse
algorithm can lead us to find rational model size au- tomatically for certain
problem without concerning choosing an original model size.