pwc-archive/papers-with-abstracts huggingface dataset:
Qualitätsmaße binärer Klassifikationen im Bereich kriminalprognostischer Instrumente der vierten Generation
Authors: Tobias D. Krafft
Date: 2018-04-04 00:00:00
Tasks: Decision Making

This master's thesis discusses an important issue regarding how algorithmic
decision making (ADM) is used in crime forecasting. In America forecasting
tools are widely used by judiciary systems for making decisions about risk
offenders based on criminal justice for risk offenders. By making use of such
tools, the judiciary relies on ADM in order to make error free judgement on
offenders. For this purpose, one of the quality measures for machine learning
techniques which is widly used, the $AUC$ (area under curve), is compared to
and contrasted for results with the $PPV_k$ (positive predictive value).
Keeping in view the criticality of judgement along with a high dependency on
tools offering ADM, it is necessary to evaluate risk tools that aid in decision
making based on algorithms. In this methodology, such an evaluation is
conducted by implementing a common machine learning approach called binary
classifier, as it determines the binary outcome of the underlying juristic
question. This thesis showed that the $PPV_k$ (positive predictive value)
technique models the decision of judges much better than the $AUC$. Therefore,
this research has investigated whether there exists a classifier for which the
$PPV_k$ deviates from $AUC$ by a large proportion. It could be shown that the
deviation can rise up to 0.75. In order to test this deviation on an already in
used Classifier, data from the fourth generation risk assement tool COMPAS was
used. The result were were quite alarming as the two measures derivate from
each other by 0.48. In this study, the risk assessment evaluation of the
forecasting tools was successfully conducted, carefully reviewed and examined.
Additionally, it is also discussed whether such systems used for the purpose of
making decisions should be socially accepted or not.
Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
Authors: David Novotny, Samuel Albanie, Diane Larlus, Andrea Vedaldi
Date: 2018-04-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification, Self-Supervised Learning, Unsupervised Landmark Detection

Self-supervision can dramatically cut back the amount of manually-labelled
data required to train deep neural networks. While self-supervision has usually
been considered for tasks such as image classification, in this paper we aim at
extending it to geometry-oriented tasks such as semantic matching and part
detection. We do so by building on several recent ideas in unsupervised
landmark detection. Our approach learns dense distinctive visual descriptors
from an unlabelled dataset of images using synthetic image transformations. It
does so by means of a robust probabilistic formulation that can introspectively
determine which image regions are likely to result in stable image matching. We
show empirically that a network pre-trained in this manner requires
significantly less supervision to learn semantic object parts compared to
numerous pre-training alternatives. We also show that the pre-trained
representation is excellent for semantic object matching.
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Authors: Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, Fredrik Kahl, Tomas Pajdla
Date: 2017-07-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Autonomous Vehicles, Benchmarking, Camera Pose Estimation, Navigate, Pose Estimation, Visual Localization

Visual localization enables autonomous vehicles to navigate in their
surroundings and augmented reality applications to link virtual to real worlds.
Practical visual localization approaches need to be robust to a wide variety of
viewing condition, including day-night changes, as well as weather and seasonal
variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera
pose estimates. In this paper, we introduce the first benchmark datasets
specifically designed for analyzing the impact of such factors on visual
localization. Using carefully created ground truth poses for query images taken
under a wide variety of conditions, we evaluate the impact of various factors
on 6DOF camera pose estimation accuracy through extensive experiments with
state-of-the-art localization approaches. Based on our results, we draw
conclusions about the difficulty of different conditions, showing that
long-term localization is far from solved, and propose promising avenues for
future work, including sequence-based localization approaches and the need for
better local features. Our benchmark is available at visuallocalization.net.
On the Computation of Kantorovich-Wasserstein Distances between 2D-Histograms by Uncapacitated Minimum Cost Flows
Authors: Federico Bassetti, Stefano Gualandi, Marco Veneroni
Date: 2018-04-02 00:00:00

In this work, we present a method to compute the Kantorovich-Wasserstein distance of order one between a pair of two-dimensional histograms. Recent works in Computer Vision and Machine Learning have shown the benefits of measuring Wasserstein distances of order one between histograms with $n$ bins, by solving a classical transportation problem on very large complete bipartite graphs with $n$ nodes and $n^2$ edges. The main contribution of our work is to approximate the original transportation problem by an uncapacitated min cost flow problem on a reduced flow network of size $O(n)$ that exploits the geometric structure of the cost function. More precisely, when the distance among the bin centers is measured with the 1-norm or the $\infty$-norm, our approach provides an optimal solution. When the distance among bins is measured with the 2-norm: (i) we derive a quantitative estimate on the error between optimal and approximate solution; (ii) given the error, we construct a reduced flow network of size $O(n)$. We numerically show the benefits of our approach by computing Wasserstein distances of order one on a set of grey scale images used as benchmark in the literature. We show how our approach scales with the size of the images with 1-norm, 2-norm and $\infty$-norm ground distances, and we compare it with other two methods which are largely used in the literature.
Stochastic Adversarial Video Prediction
Authors: Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine
Date: 2018-04-04 00:00:00
Proceeding: ICLR 2019 5
Tasks: Prediction, Representation Learning, Video Generation, Video Prediction

Being able to predict what may happen in the future requires an in-depth
understanding of the physical and causal rules that govern the world. A model
that is able to do so has a number of appealing applications, from robotic
planning to representation learning. However, learning to predict raw future
observations, such as frames in a video, is exceedingly challenging -- the
ambiguous nature of the problem can cause a naively designed model to average
together possible futures into a single, blurry prediction. Recently, this has
been addressed by two distinct approaches: (a) latent variational variable
models that explicitly model underlying stochasticity and (b)
adversarially-trained models that aim to produce naturalistic images. However,
a standard latent variable model can struggle to produce realistic results, and
a standard adversarially-trained model underutilizes latent variables and fails
to produce diverse predictions. We show that these distinct methods are in fact
complementary. Combining the two produces predictions that look more realistic
to human raters and better cover the range of possible futures. Our method
outperforms prior and concurrent work in these aspects.
Universal Planning Networks
Authors: Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn
Date: 2018-04-02 00:00:00
Tasks: Imitation Learning, Reinforcement Learning, Representation Learning, Transfer Reinforcement Learning

A key challenge in complex visuomotor control is learning abstract
representations that are effective for specifying goals, planning, and
generalization. To this end, we introduce universal planning networks (UPN).
UPNs embed differentiable planning within a goal-directed policy. This planning
computation unrolls a forward model in a latent space and infers an optimal
action plan through gradient descent trajectory optimization. The
plan-by-gradient-descent process and its underlying representations are learned
end-to-end to directly optimize a supervised imitation learning objective. We
find that the representations learned are not only effective for goal-directed
visual imitation via gradient-based trajectory optimization, but can also
provide a metric for specifying goals using images. The learned representations
can be leveraged to specify distance-based rewards to reach new target states
for model-free reinforcement learning, resulting in substantially more
effective learning when solving new tasks described via image-based goals. We
were able to achieve successful transfer of visuomotor planning strategies
across robots with significantly different morphologies and actuation
capabilities.
Stochastic Dynamic Programming Heuristics for Influence Maximization-Revenue Optimization
Authors: Trisha Lawrence
Date: 2018-02-28 00:00:00
Tasks: Decision Making, Marketing

The well-known Influence Maximization (IM) problem has been actively studied
by researchers over the past decade, with emphasis on marketing and social
networks. Existing research have obtained solutions to the IM problem by
obtaining the influence spread and utilizing the property of submodularity.
This paper is based on a novel approach to the IM problem geared towards
optimizing clicks and consequently revenue within anOnline Social Network
(OSN). Our approach diverts from existing approaches by adopting a novel,
decision-making perspective through implementing Stochastic Dynamic Programming
(SDP). Thus, we define a new problem Influence Maximization-Revenue
Optimization (IM-RO) and propose SDP as a method in which this problem can be
solved. The SDP method has lucrative gains for an advertiser in terms of
optimizing clicks and generating revenue however, one drawback to the method is
its associated "curse of dimensionality" particularly for problems involving a
large state space. Thus, we introduce the Lawrence Degree Heuristic (LDH),
Adaptive Hill-Climbing (AHC) and Multistage Particle Swarm Optimization (MPSO)
heuristics as methods which are orders of magnitude faster than the SDP method
whilst achieving near-optimal results. Through a comparative analysis on
various synthetic and real-world networks we present the AHC and LDH as
heuristics well suited to to the IM-RO problem in terms of their accuracy,
running times and scalability under ideal model parameters. In this paper we
present a compelling survey on the SDP method as a practical and lucrative
method for spreading information and optimizing revenue within the context of
OSNs.
Density Adaptive Point Set Registration
Authors: Felix Järemo Lawin, Martin Danelljan, Fahad Shahbaz Khan, Per-Erik Forssén, Michael Felsberg
Date: 2018-04-04 00:00:00
Proceeding: CVPR 2018 6

Probabilistic methods for point set registration have demonstrated
competitive results in recent years. These techniques estimate a probability
distribution model of the point clouds. While such a representation has shown
promise, it is highly sensitive to variations in the density of 3D points. This
fundamental problem is primarily caused by changes in the sensor location
across point sets. We revisit the foundations of the probabilistic registration
paradigm. Contrary to previous works, we model the underlying structure of the
scene as a latent probability distribution, and thereby induce invariance to
point set density changes. Both the probabilistic model of the scene and the
registration parameters are inferred by minimizing the Kullback-Leibler
divergence in an Expectation Maximization based framework. Our density-adaptive
registration successfully handles severe density variations commonly
encountered in terrestrial Lidar applications. We perform extensive experiments
on several challenging real-world Lidar datasets. The results demonstrate that
our approach outperforms state-of-the-art probabilistic methods for multi-view
registration, without the need of re-sampling. Code is available at
https://github.com/felja633/DARE.
Online Multi-Label Classification: A Label Compression Method
Authors: Zahra Ahmadi, Stefan Kramer
Date: 2018-04-04 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Text Categorization

Many modern applications deal with multi-label data, such as functional
categorizations of genes, image labeling and text categorization.
Classification of such data with a large number of labels and latent
dependencies among them is a challenging task, and it becomes even more
challenging when the data is received online and in chunks. Many of the current
multi-label classification methods require a lot of time and memory, which make
them infeasible for practical real-world applications. In this paper, we
propose a fast linear label space dimension reduction method that transforms
the labels into a reduced encoded space and trains models on the obtained
pseudo labels. Additionally, it provides an analytical method to update the
decoding matrix which maps the labels into the original space and is used
during the test phase. Experimental results show the effectiveness of this
approach in terms of running times and the prediction performance over
different measures.
Object Referring in Videos with Language and Human Gaze
Authors: Arun Balajee Vasudevan, Dengxin Dai, Luc van Gool
Date: 2018-01-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Referring Expression

We investigate the problem of object referring (OR) i.e. to localize a target
object in a visual scene coming with a language description. Humans perceive
the world more as continued video snippets than as static images, and describe
objects not only by their appearance, but also by their spatio-temporal context
and motion features. Humans also gaze at the object when they issue a referring
expression. Existing works for OR mostly focus on static images only, which
fall short in providing many such cues. This paper addresses OR in videos with
language and human gaze. To that end, we present a new video dataset for OR,
with 30, 000 objects over 5, 000 stereo video sequences annotated for their
descriptions and gaze. We further propose a novel network model for OR in
videos, by integrating appearance, motion, gaze, and spatio-temporal context
into one network. Experimental results show that our method effectively
utilizes motion cues, human gaze, and spatio-temporal context. Our method
outperforms previousOR methods. For dataset and code, please refer
https://people.ee.ethz.ch/~arunv/ORGaze.html.
Marian: Fast Neural Machine Translation in C++
Authors: Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, Alexandra Birch
Date: 2018-04-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Machine Translation, Translation

We present Marian, an efficient and self-contained Neural Machine Translation
framework with an integrated automatic differentiation engine based on dynamic
computation graphs. Marian is written entirely in C++. We describe the design
of the encoder-decoder framework and demonstrate that a research-friendly
toolkit can achieve high training and translation speed.
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data
Authors: William Herlands, Edward McFowland III, Andrew Gordon Wilson, Daniel B. Neill
Date: 2018-04-04 00:00:00
Tasks: Gaussian Processes

Identifying anomalous patterns in real-world data is essential for
understanding where, when, and how systems deviate from their expected
dynamics. Yet methods that separately consider the anomalousness of each
individual data point have low detection power for subtle, emerging
irregularities. Additionally, recent detection techniques based on subset
scanning make strong independence assumptions and suffer degraded performance
in correlated data. We introduce methods for identifying anomalous patterns in
non-iid data by combining Gaussian processes with novel log-likelihood ratio
statistic and subset scanning techniques. Our approaches are powerful,
interpretable, and can integrate information across multiple data streams. We
illustrate their performance on numeric simulations and three open source
spatiotemporal datasets of opioid overdose deaths, 311 calls, and storm
reports.
Framework for evaluation of sound event detection in web videos
Authors: Rohan Badlani, Ankit Shah, Benjamin Elizalde, Anurag Kumar, Bhiksha Raj
Date: 2017-11-02 00:00:00
Tasks: Event Detection, Sound Event Detection

The largest source of sound events is web videos. Most videos lack sound
event labels at segment level, however, a significant number of them do respond
to text queries, from a match found using metadata by search engines. In this
paper we explore the extent to which a search query can be used as the true
label for detection of sound events in videos. We present a framework for
large-scale sound event recognition on web videos. The framework crawls videos
using search queries corresponding to 78 sound event labels drawn from three
datasets. The datasets are used to train three classifiers, and we obtain a
prediction on 3.7 million web video segments. We evaluated performance using
the search query as true label and compare it with human labeling. Both types
of ground truth exhibited close performance, to within 10%, and similar
performance trend with increasing number of evaluated segments. Hence, our
experiments show potential for using search query as a preliminary true label
for sound event recognition in web videos.
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input
Authors: David Harwath, Adrià Recasens, Dídac Surís, Galen Chuang, Antonio Torralba, James Glass
Date: 2018-04-04 00:00:00
Proceeding: ECCV 2018 9
Tasks: Retrieval, Sound Prompted Semantic Segmentation, Speech Prompted Semantic Segmentation

In this paper, we explore neural network models that learn to associate
segments of spoken audio captions with the semantically relevant portions of
natural images that they refer to. We demonstrate that these audio-visual
associative localizations emerge from network-internal representations learned
as a by-product of training to perform an image-audio retrieval task. Our
models operate directly on the image pixels and speech waveform, and do not
rely on any conventional supervision in the form of labels, segmentations, or
alignments between the modalities during training. We perform analysis using
the Places 205 and ADE20k datasets demonstrating that our models implicitly
learn semantically-coupled object and word detectors.
Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
Authors: Alvaro Ulloa, Sergey Plis, Vince Calhoun
Date: 2018-04-04 00:00:00
Tasks: General Classification

The wide variety of brain imaging technologies allows us to exploit
information inherent to different data modalities. The richness of multimodal
datasets may increase predictive power and reveal latent variables that
otherwise would have not been found. However, the analysis of multimodal data
is often conducted by assuming linear interactions which impact the accuracy of
the results. We propose the use of a multimodal multi-layer perceptron model to
enhance the predictive power of structural and functional magnetic resonance
imaging (sMRI and fMRI) combined.
  We also use a synthetic data generator to pre-train each modality input
layers, alleviating the effects of the small sample size that is often the case
for brain imaging modalities. The proposed model improved the average and
uncertainty of the area under the ROC curve to 0.850+-0.051 compared to the
best results on individual modalities (0.741+-0.075 for sMRI, and 0.833+-0.050
for fMRI).
Layout-induced Video Representation for Recognizing Agent-in-Place Actions
Authors: Ruichi Yu, Hongcheng Wang, Ang Li, Jingxiao Zheng, Vlad I. Morariu, Larry S. Davis
Date: 2018-04-04 00:00:00
Proceeding: ICCV 2019 10

We address the recognition of agent-in-place actions, which are associated
with agents who perform them and places where they occur, in the context of
outdoor home surveillance. We introduce a representation of the geometry and
topology of scene layouts so that a network can generalize from the layouts
observed in the training set to unseen layouts in the test set. This
Layout-Induced Video Representation (LIVR) abstracts away low-level appearance
variance and encodes geometric and topological relationships of places in a
specific scene layout. LIVR partitions the semantic features of a video clip
into different places to force the network to learn place-based feature
descriptions; to predict the confidence of each action, LIVR aggregates
features from the place associated with an action and its adjacent places on
the scene layout. We introduce the Agent-in-Place Action dataset to show that
our method allows neural network models to generalize significantly better to
unseen scenes.
Learning Filterbanks from Raw Speech for Phone Recognition
Authors: Neil Zeghidour, Nicolas Usunier, Iasonas Kokkinos, Thomas Schatz, Gabriel Synnaeve, Emmanuel Dupoux
Date: 2017-11-03 00:00:00

We train a bank of complex filters that operates on the raw waveform and is
fed into a convolutional neural network for end-to-end phone recognition. These
time-domain filterbanks (TD-filterbanks) are initialized as an approximation of
mel-filterbanks, and then fine-tuned jointly with the remaining convolutional
architecture. We perform phone recognition experiments on TIMIT and show that
for several architectures, models trained on TD-filterbanks consistently
outperform their counterparts trained on comparable mel-filterbanks. We get our
best performance by learning all front-end steps, from pre-emphasis up to
averaging. Finally, we observe that the filters at convergence have an
asymmetric impulse response, and that some of them remain almost analytic.
R2RML Mappings in OBDA Systems: Enabling Comparison among OBDA Tools
Authors: Manuel Namici
Date: 2018-04-04 00:00:00

In today's large enterprises there is a significant increasing trend in the
amount of data that has to be stored and processed. To complicate this scenario
the complexity of organizing and managing a large collection of data,
structured according to a single, unified schema, makes so that there is almost
never a single place where to look to satisfy an information need.
  The Ontology-Based Data Access (OBDA) paradigm aims at mitigating this
phenomenon by providing to the users of the system a unified and shared
conceptual view of the domain of interest (ontology), while still enabling the
data to be stored in different data sources, which are managed by a relational
database. In an OBDA system the link between the data stored at the sources and
the ontology is provided through a declarative specification given in terms of
a set of mappings.
  In this work we focus on comparing two of the available systems for OBDA,
namely, Mastro and Ontop, by adopting OBDA specifications based on W3C
recommendations. We first show how support for R2RML mappings has been
integrated in Mastro, which was the last feature missing in order to enable the
system to use specifications based solely on W3C recommendations relevant to
OBDA. We then proceed in performing a comparison between these systems over two
OBDA specifications, the NPD Benchmark and the ACI specification.