pwc-archive/papers-with-abstracts huggingface dataset:
Markerless Visual Robot Programming by Demonstration
Authors: Raphael Memmesheimer, Ivanna Mykhalchyshyna, Viktor Seib, Nick Theisen, Dietrich Paulus
Date: 2018-07-30 00:00:00
Tasks: Object, Pose Estimation

In this paper we present an approach for learning to imitate human behavior
on a semantic level by markerless visual observation. We analyze a set of
spatial constraints on human pose data extracted using convolutional pose
machines and object informations extracted from 2D image sequences. A scene
analysis, based on an ontology of objects and affordances, is combined with
continuous human pose estimation and spatial object relations. Using a set of
constraints we associate the observed human actions with a set of executable
robot commands. We demonstrate our approach in a kitchen task, where the robot
learns to prepare a meal.
Estimating Failure in Brittle Materials using Graph Theory
Authors: M. K. Mudunuru, N. Panda, S. Karra, G. Srinivasan, V. T. Chau, E. Rougier, A. Hunter, H. S. Viswanathan
Date: 2018-07-30 00:00:00
Tasks: Uncertainty Quantification

In brittle fracture applications, failure paths, regions where the failure
occurs and damage statistics, are some of the key quantities of interest (QoI).
High-fidelity models for brittle failure that accurately predict these QoI
exist but are highly computationally intensive, making them infeasible to
incorporate in upscaling and uncertainty quantification frameworks. The goal of
this paper is to provide a fast heuristic to reasonably estimate quantities
such as failure path and damage in the process of brittle failure. Towards this
goal, we first present a method to predict failure paths under tensile loading
conditions and low-strain rates. The method uses a $k$-nearest neighbors
algorithm built on fracture process zone theory, and identifies the set of all
possible pre-existing cracks that are likely to join early to form a large
crack. The method then identifies zone of failure and failure paths using
weighted graphs algorithms. We compare these failure paths to those computed
with a high-fidelity model called the Hybrid Optimization Software Simulation
Suite (HOSS). A probabilistic evolution model for average damage in a system is
also developed that is trained using 150 HOSS simulations and tested on 40
simulations. A non-parametric approach based on confidence intervals is used to
determine the damage evolution over time along the dominant failure path. For
upscaling, damage is the key QoI needed as an input by the continuum models.
This needs to be informed accurately by the surrogate models for calculating
effective modulii at continuum-scale. We show that for the proposed average
damage evolution model, the prediction accuracy on the test data is more than
90\%. In terms of the computational time, the proposed models are $\approx
\mathcal{O}(10^6)$ times faster compared to high-fidelity HOSS.
News Article Teaser Tweets and How to Generate Them
Authors: Sanjeev Kumar Karn, Mark Buckley, Ulli Waltinger, Hinrich Schütze
Date: 2018-07-30 00:00:00
Proceeding: NAACL 2019 6

In this work, we define the task of teaser generation and provide an
evaluation benchmark and baseline systems for the process of generating
teasers. A teaser is a short reading suggestion for an article that is
illustrative and includes curiosity-arousing elements to entice potential
readers to read particular news items. Teasers are one of the main vehicles for
transmitting news to social media users. We compile a novel dataset of teasers
by systematically accumulating tweets and selecting those that conform to the
teaser definition. We have compared a number of neural abstractive
architectures on the task of teaser generation and the overall best performing
system is See et al.(2017)'s seq2seq with pointer network.
A Restricted-Domain Dual Formulation for Two-Phase Image Segmentation
Authors: Jack Spencer
Date: 2018-07-30 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Vocal Bursts Valence Prediction

In two-phase image segmentation, convex relaxation has allowed global
minimisers to be computed for a variety of data fitting terms. Many efficient
approaches exist to compute a solution quickly. However, we consider whether
the nature of the data fitting in this formulation allows for reasonable
assumptions to be made about the solution that can improve the computational
performance further. In particular, we employ a well known dual formulation of
this problem and solve the corresponding equations in a restricted domain. We
present experimental results that explore the dependence of the solution on
this restriction and quantify imrovements in the computational performance.
This approach can be extended to analogous methods simply and could provide an
efficient alternative for problems of this type.
Efficiency, Sequenceability and Deal-Optimality in Fair Division of Indivisible Goods
Authors: Aurélie Beynier, Sylvain Bouveret, Michel Lemaître, Nicolas Maudet, Simon Rey
Date: 2018-07-28 00:00:00
Tasks: Fairness

In fair division of indivisible goods, using sequences of sincere choices (or
picking sequences) is a natural way to allocate the objects. The idea is as
follows: at each stage, a designated agent picks one object among those that
remain. Another intuitive way to obtain an allocation is to give objects to
agents in the first place, and to let agents exchange them as long as such
"deals" are beneficial. This paper investigates these notions, when agents have
additive preferences over objects, and unveils surprising connections between
them, and with other efficiency and fairness notions. In particular, we show
that an allocation is sequenceable iff it is optimal for a certain type of
deals, namely cycle deals involving a single object. Furthermore, any
Pareto-optimal allocation is sequenceable, but not the converse. Regarding
fairness, we show that an allocation can be envy-free and non-sequenceable, but
that every competitive equilibrium with equal incomes is sequenceable. To
complete the picture, we show how some domain restrictions may affect the
relations between these notions. Finally, we experimentally explore the links
between the scales of efficiency and fairness.
MaskConnect: Connectivity Learning by Gradient Descent
Authors: Karim Ahmed, Lorenzo Torresani
Date: 2018-07-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: image-classification, Image Classification, Neural Architecture Search

Although deep networks have recently emerged as the model of choice for many
computer vision problems, in order to yield good results they often require
time-consuming architecture search. To combat the complexity of design choices,
prior work has adopted the principle of modularized design which consists in
defining the network in terms of a composition of topologically identical or
similar building blocks (a.k.a. modules). This reduces architecture search to
the problem of determining the number of modules to compose and how to connect
such modules. Again, for reasons of design complexity and training cost,
previous approaches have relied on simple rules of connectivity, e.g.,
connecting each module to only the immediately preceding module or perhaps to
all of the previous ones. Such simple connectivity rules are unlikely to yield
the optimal architecture for the given problem.
  In this work we remove these predefined choices and propose an algorithm to
learn the connections between modules in the network. Instead of being chosen a
priori by the human designer, the connectivity is learned simultaneously with
the weights of the network by optimizing the loss function of the end task
using a modified version of gradient descent. We demonstrate our connectivity
learning method on the problem of multi-class image classification using two
popular architectures: ResNet and ResNeXt. Experiments on four different
datasets show that connectivity learning using our approach yields consistently
higher accuracy compared to relying on traditional predefined rules of
connectivity. Furthermore, in certain settings it leads to significant savings
in number of parameters.
Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems
Authors: Seonghan Ryu, Seokhwan Kim, Junhwi Choi, Hwanjo Yu, Gary Geunbae Lee
Date: 2018-07-27 00:00:00
Tasks: Sentence, Sentence Embedding, Sentence-Embedding

To ensure satisfactory user experience, dialog systems must be able to
determine whether an input sentence is in-domain (ID) or out-of-domain (OOD).
We assume that only ID sentences are available as training data because
collecting enough OOD sentences in an unbiased way is a laborious and
time-consuming job. This paper proposes a novel neural sentence embedding
method that represents sentences in a low-dimensional continuous vector space
that emphasizes aspects that distinguish ID cases from OOD cases. We first used
a large set of unlabeled text to pre-train word representations that are used
to initialize neural sentence embedding. Then we used domain-category analysis
as an auxiliary task to train neural sentence embedding for OOD sentence
detection. After the sentence representations were learned, we used them to
train an autoencoder aimed at OOD sentence detection. We evaluated our method
by experimentally comparing it to the state-of-the-art methods in an
eight-domain dialog system; our proposed method achieved the highest accuracy
in all tests.
A Hierarchical Approach to Neural Context-Aware Modeling
Authors: Patrick Huber, Jan Niehues, Alex Waibel
Date: 2018-07-27 00:00:00
Tasks: Binary Classification, Language Modeling, Language Modelling, Sentence

We present a new recurrent neural network topology to enhance
state-of-the-art machine learning systems by incorporating a broader context.
Our approach overcomes recent limitations with extended narratives through a
multi-layered computational approach to generate an abstract context
representation. Therefore, the developed system captures the narrative on
word-level, sentence-level, and context-level. Through the hierarchical set-up,
our proposed model summarizes the most salient information on each level and
creates an abstract representation of the extended context. We subsequently use
this representation to enhance neural language processing systems on the task
of semantic error detection. To show the potential of the newly introduced
topology, we compare the approach against a context-agnostic set-up including a
standard neural language model and a supervised binary classification network.
The performance measures on the error detection task show the advantage of the
hierarchical context-aware topologies, improving the baseline by 12.75%
relative for unsupervised models and 20.37% relative for supervised models.
Efficient Gauss-Newton-Krylov momentum conservation constrained PDE-LDDMM using the band-limited vector field parameterization
Authors: Monica Hernandez
Date: 2018-07-27 00:00:00

The class of non-rigid registration methods proposed in the framework of
PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a
particularly interesting family of physically meaningful diffeomorphic
registration methods. PDE-constrained LDDMM methods are formulated as
constrained variational problems, where the different physical models are
imposed using the associated partial differential equations as hard
constraints. Inexact Newton-Krylov optimization has shown an excellent
numerical accuracy and an extraordinarily fast convergence rate in this
framework. However, the Galerkin representation of the non-stationary velocity
fields does not provide proper geodesic paths. In a previous work, we proposed
a method for PDE-constrained LDDMM parameterized in the space of initial
velocity fields under the EPDiff equation. The proposed method provided
geodesics in the framework of PDE-constrained LDDMM, and it showed performance
competitive to benchmark PDE-constrained LDDMM and EPDiff-LDDMM methods.
However, the major drawback of this method was the large memory load inherent
to PDE-constrained LDDMM methods and the increased computational time with
respect to the benchmark methods. In this work we optimize the computational
complexity of the method using the band-limited vector field parameterization
closing the loop with our previous works.
Learning Blind Video Temporal Consistency
Authors: Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, Ming-Hsuan Yang
Date: 2018-08-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Colorization, Image-to-Image Translation, Intrinsic Image Decomposition, Optical Flow Estimation, Style Transfer, Video Temporal Consistency

Applying image processing algorithms independently to each frame of a video
often leads to undesired inconsistent results over time. Developing temporally
consistent video-based extensions, however, requires domain knowledge for
individual tasks and is unable to generalize to other applications. In this
paper, we present an efficient end-to-end approach based on deep recurrent
network for enforcing temporal consistency in a video. Our method takes the
original unprocessed and per-frame processed videos as inputs to produce a
temporally consistent video. Consequently, our approach is agnostic to specific
image processing algorithms applied on the original video. We train the
proposed network by minimizing both short-term and long-term temporal losses as
well as the perceptual loss to strike a balance between temporal stability and
perceptual similarity with the processed frames. At test time, our model does
not require computing optical flow and thus achieves real-time speed even for
high-resolution videos. We show that our single model can handle multiple and
unseen tasks, including but not limited to artistic style transfer,
enhancement, colorization, image-to-image translation and intrinsic image
decomposition. Extensive objective evaluation and subject study demonstrate
that the proposed approach performs favorably against the state-of-the-art
methods on various types of videos.
Towards a Semantic Perceptual Image Metric
Authors: Troy Chinen, Johannes Ballé, Chunhui Gu, Sung Jin Hwang, Sergey Ioffe, Nick Johnston, Thomas Leung, David Minnen, Sean O'Malley, Charles Rosenberg, George Toderici
Date: 2018-08-01 00:00:00
Tasks: Image Quality Assessment

We present a full reference, perceptual image metric based on VGG-16, an
artificial neural network trained on object classification. We fit the metric
to a new database based on 140k unique images annotated with ground truth by
human raters who received minimal instruction. The resulting metric shows
competitive performance on TID 2013, a database widely used to assess image
quality assessments methods. More interestingly, it shows strong responses to
objects potentially carrying semantic relevance such as faces and text, which
we demonstrate using a visualization technique and ablation experiments. In
effect, the metric appears to model a higher influence of semantic context on
judgments, which we observe particularly in untrained raters. As the vast
majority of users of image processing systems are unfamiliar with Image Quality
Assessment (IQA) tasks, these findings may have significant impact on
real-world applications of perceptual metrics.
Matrix completion and extrapolation via kernel regression
Authors: Pere Giménez-Febrer, Alba Pagès-Zamora, Georgios B. Giannakis
Date: 2018-08-01 00:00:00
Tasks: Matrix Completion, regression

Matrix completion and extrapolation (MCEX) are dealt with here over
reproducing kernel Hilbert spaces (RKHSs) in order to account for prior
information present in the available data. Aiming at a faster and
low-complexity solver, the task is formulated as a kernel ridge regression. The
resultant MCEX algorithm can also afford online implementation, while the class
of kernel functions also encompasses several existing approaches to MC with
prior information. Numerical tests on synthetic and real datasets show that the
novel approach performs faster than widespread methods such as alternating
least squares (ALS) or stochastic gradient descent (SGD), and that the recovery
error is reduced, especially when dealing with noisy data.
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate
Authors: Sheng Chen, Jia Guo, Yang Liu, Xiang Gao, Zhen Han
Date: 2018-08-01 00:00:00
Tasks: Face Recognition, Robust Face Recognition

In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block,
which reweights local features in a convolutional neural network (CNN)
adaptively according to their L2 norms and outputs a global feature vector with
a global average pooling layer. Our GNAP block is designed to give dynamic
weights to local features in different spatial positions without losing spatial
symmetry. We use a GNAP block in a face feature embedding CNN to produce
discriminative face feature vectors for pose-robust face recognition. The GNAP
block is of very cheap computational cost, but it is very powerful for
frontal-profile face recognition. Under the CFP frontal-profile protocol, the
GNAP block can not only reduce EER dramatically but also boost TPR@FPR=0.1%
(TPR i.e. True Positive Rate, FPR i.e. False Positive Rate) substantially. Our
experiments show that the GNAP block greatly promotes pose-robust face
recognition over the base model especially at low false positive rate.
Seq2Seq and Multi-Task Learning for joint intent and content extraction for domain specific interpreters
Authors: Marc Velay, Fabrice Daniel
Date: 2018-08-01 00:00:00
Tasks: Chatbot, Multi-Task Learning

This study evaluates the performances of an LSTM network for detecting and
extracting the intent and content of com- mands for a financial chatbot. It
presents two techniques, sequence to sequence learning and Multi-Task Learning,
which might improve on the previous task.
Stock Chart Pattern recognition with Deep Learning
Authors: Marc Velay, Fabrice Daniel
Date: 2018-08-01 00:00:00
Tasks: Deep Learning

This study evaluates the performances of CNN and LSTM for recognizing common
charts patterns in a stock historical data. It presents two common patterns,
the method used to build the training set, the neural networks architectures
and the accuracies obtained.
Debugging Non-Ground ASP Programs: Technique and Graphical Tools
Authors: Carmine Dodaro, Philip Gasteiger, Kristian Reale, Francesco Ricca, Konstantin Schekotihin
Date: 2018-08-01 00:00:00

Answer Set Programming (ASP) is one of the major declarative programming
paradigms in the area of logic programming and non-monotonic reasoning. Despite
that ASP features a simple syntax and an intuitive semantics, errors are common
during the development of ASP programs. In this paper we propose a novel
debugging approach allowing for interactive localization of bugs in non-ground
programs. The new approach points the user directly to a set of non-ground
rules involved in the bug, which might be refined (up to the point in which the
bug is easily identified) by asking the programmer a sequence of questions on
an expected answer set. The approach has been implemented on top of the ASP
solver WASP. The resulting debugger has been complemented by a user-friendly
graphical interface, and integrated in ASPIDE, a rich IDE for answer set
programs. In addition, an empirical analysis shows that the new debugger is not
affected by the grounding blowup limiting the application of previous
approaches based on meta-programming. Under consideration in Theory and
Practice of Logic Programming (TPLP).
Geometry of energy landscapes and the optimizability of deep neural networks
Authors: Simon Becker, Yao Zhang, Alpha A. Lee
Date: 2018-08-01 00:00:00
Tasks: BIG-bench Machine Learning

Deep neural networks are workhorse models in machine learning with multiple
layers of non-linear functions composed in series. Their loss function is
highly non-convex, yet empirically even gradient descent minimisation is
sufficient to arrive at accurate and predictive models. It is hitherto unknown
why are deep neural networks easily optimizable. We analyze the energy
landscape of a spin glass model of deep neural networks using random matrix
theory and algebraic geometry. We analytically show that the multilayered
structure holds the key to optimizability: Fixing the number of parameters and
increasing network depth, the number of stationary points in the loss function
decreases, minima become more clustered in parameter space, and the tradeoff
between the depth and width of minima becomes less severe. Our analytical
results are numerically verified through comparison with neural networks
trained on a set of classical benchmark datasets. Our model uncovers generic
design principles of machine learning models.
Efficient Progressive Neural Architecture Search
Authors: Juan-Manuel Perez-Rua, Moez Baccouche, Stephane Pateux
Date: 2018-08-01 00:00:00
Tasks: General Classification, image-classification, Image Classification, Neural Architecture Search

This paper addresses the difficult problem of finding an optimal neural
architecture design for a given image classification task. We propose a method
that aggregates two main results of the previous state-of-the-art in neural
architecture search. These are, appealing to the strong sampling efficiency of
a search scheme based on sequential model-based optimization (SMBO), and
increasing training efficiency by sharing weights among sampled architectures.
Sequential search has previously demonstrated its capabilities to find
state-of-the-art neural architectures for image classification. However, its
computational cost remains high, even unreachable under modest computational
settings. Affording SMBO with weight-sharing alleviates this problem. On the
other hand, progressive search with SMBO is inherently greedy, as it leverages
a learned surrogate function to predict the validation error of neural
architectures. This prediction is directly used to rank the sampled neural
architectures. We propose to attenuate the greediness of the original SMBO
method by relaxing the role of the surrogate function so it predicts
architecture sampling probability instead. We demonstrate with experiments on
the CIFAR-10 dataset that our method, denominated Efficient progressive neural
architecture search (EPNAS), leads to increased search efficiency, while
retaining competitiveness of found architectures.
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize
Authors: Tengyuan Liang, Alexander Rakhlin
Date: 2018-08-01 00:00:00
Tasks: regression

In the absence of explicit regularization, Kernel "Ridgeless" Regression with
nonlinear kernels has the potential to fit the training data perfectly. It has
been observed empirically, however, that such interpolated solutions can still
generalize well on test data. We isolate a phenomenon of implicit
regularization for minimum-norm interpolated solutions which is due to a
combination of high dimensionality of the input data, curvature of the kernel
function, and favorable geometric properties of the data such as an eigenvalue
decay of the empirical covariance and kernel matrices. In addition to deriving
a data-dependent upper bound on the out-of-sample error, we present
experimental evidence suggesting that the phenomenon occurs in the MNIST
dataset.