pwc-archive/papers-with-abstracts huggingface dataset:
Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures
Authors: Valérie Poulin, François Théberge
Date: 2018-06-29 00:00:00

In this paper, we propose a family of graph partition similarity measures
that take the topology of the graph into account. These graph-aware measures
are alternatives to using set partition similarity measures that are not
specifically designed for graph partitions. The two types of measures,
graph-aware and set partition measures, are shown to have opposite behaviors
with respect to resolution issues and provide complementary information
necessary to assess that two graph partitions are similar.
Play Duration based User-Entity Affinity Modeling in Spoken Dialog System
Authors: Bo Xiao, Nicholas Monath, Shankar Ananthakrishnan, Abishek Ravi
Date: 2018-06-29 00:00:00
Tasks: Collaborative Filtering

Multimedia streaming services over spoken dialog systems have become
ubiquitous. User-entity affinity modeling is critical for the system to
understand and disambiguate user intents and personalize user experiences.
However, fully voice-based interaction demands quantification of novel
behavioral cues to determine user affinities. In this work, we propose using
play duration cues to learn a matrix factorization based collaborative
filtering model. We first binarize play durations to obtain implicit positive
and negative affinity labels. The Bayesian Personalized Ranking objective and
learning algorithm are employed in our low-rank matrix factorization approach.
To cope with uncertainties in the implicit affinity labels, we propose to apply
a weighting function that emphasizes the importance of high confidence samples.
Based on a large-scale database of Alexa music service records, we evaluate the
affinity models by computing Spearman correlation between play durations and
predicted affinities. Comparing different data utilizations and weighting
functions, we find that employing both positive and negative affinity samples
with a convex weighting function yields the best performance. Further analysis
demonstrates the model's effectiveness on individual entity level and provides
insights on the temporal dynamics of observed affinities.
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis
Authors: Deepa Gunashekar, Sailesh Conjeti, Abhijit Guha Roy, Nassir Navab, Kuangyu Shi
Date: 2018-06-29 00:00:00
Tasks: Image Generation, Image Segmentation, Image Super-Resolution, Semantic Segmentation, Super-Resolution

Cross modal image syntheses is gaining significant interests for its ability
to estimate target images of a different modality from a given set of source
images,like estimating MR to MR, MR to CT, CT to PET etc, without the need for
an actual acquisition.Though they show potential for applications in radiation
therapy planning,image super resolution, atlas construction, image segmentation
etc.The synthesis results are not as accurate as the actual acquisition.In this
paper,we address the problem of multi modal image synthesis by proposing a
fully convolutional deep learning architecture called the SynNet.We extend the
proposed architecture for various input output configurations. And finally, we
propose a structure preserving custom loss function for cross-modal image
synthesis.We validate the proposed SynNet and its extended framework on BRATS
dataset with comparisons against three state-of-the art methods.And the results
of the proposed custom loss function is validated against the traditional loss
function used by the state-of-the-art methods for cross modal image synthesis.
Simplified Active Calibration
Authors: Mehdi Faraji, Anup Basu
Date: 2018-06-29 00:00:00

We present a new mathematical formulation to estimate the intrinsic
parameters of a camera in active or robotic platforms. We show that the focal
lengths can be estimated using only one point correspondence that relates
images taken before and after a degenerate rotation of the camera. The
estimated focal lengths are then treated as known parameters to obtain a linear
set of equations to calculate the principal point. Assuming that the principal
point is close to the image center, the accuracy of the linear equations are
increased by integrating the image center into the formulation. We extensively
evaluate the formulations on a simulated camera, 3D scenes and real-world
images. Our error analysis over simulated and real images indicates that the
proposed Simplified Active Calibration method estimates the parameters of a
camera with low error rates that can be used as an initial guess for further
non-linear refinement procedures. Simplified Active Calibration can be employed
in real-time environments for automatic calibrations given the proposed
closed-form solutions.
Bayesian Deep Learning on a Quantum Computer
Authors: Zhikuan Zhao, Alejandro Pozas-Kerstjens, Patrick Rebentrost, Peter Wittek
Date: 2018-06-29 00:00:00
Tasks: Deep Learning, Gaussian Processes

Bayesian methods in machine learning, such as Gaussian processes, have great advantages com-pared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep architectures has remained a major challenge. Recent results connected deep feedforward neural networks with Gaussian processes, allowing training without backpropagation. This connection enables us to leverage a quantum algorithm designed for Gaussian processes and develop a new algorithm for Bayesian deep learning on quantum computers. The properties of the kernel matrix in the Gaussian process ensure the efficient execution of the core component of the protocol, quantum matrix inversion, providing an at least polynomial speedup over classical algorithms. Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.
Investigating Speech Features for Continuous Turn-Taking Prediction Using LSTMs
Authors: Matthew Roddy, Gabriel Skantze, Naomi Harte
Date: 2018-06-29 00:00:00

For spoken dialog systems to conduct fluid conversational interactions with
users, the systems must be sensitive to turn-taking cues produced by a user.
Models should be designed so that effective decisions can be made as to when it
is appropriate, or not, for the system to speak. Traditional end-of-turn
models, where decisions are made at utterance end-points, are limited in their
ability to model fast turn-switches and overlap. A more flexible approach is to
model turn-taking in a continuous manner using RNNs, where the system predicts
speech probability scores for discrete frames within a future window. The
continuous predictions represent generalized turn-taking behaviors observed in
the training data and can be applied to make decisions that are not just
limited to end-of-turn detection. In this paper, we investigate optimal
speech-related feature sets for making predictions at pauses and overlaps in
conversation. We find that while traditional acoustic features perform well,
part-of-speech features generally perform worse than word features. We show
that our current models outperform previously reported baselines.
MRFusion: A Deep Learning architecture to fuse PAN and MS imagery for land cover mapping
Authors: Raffaele Gaetano, Dino Ienco, Kenji Ose, Remi Cresson
Date: 2018-06-29 00:00:00
Tasks: Earth Observation, Pansharpening

Nowadays, Earth Observation systems provide a multitude of heterogeneous
remote sensing data. How to manage such richness leveraging its complementarity
is a crucial chal- lenge in modern remote sensing analysis. Data Fusion
techniques deal with this point proposing method to combine and exploit
complementarity among the different data sensors. Considering optical Very High
Spatial Resolution (VHSR) images, satellites obtain both Multi Spectral (MS)
and panchro- matic (PAN) images at different spatial resolution. VHSR images
are extensively exploited to produce land cover maps to deal with agricultural,
ecological, and socioeconomic issues as well as assessing ecosystem status,
monitoring biodiversity and provid- ing inputs to conceive food risk monitoring
systems. Common techniques to produce land cover maps from such VHSR images
typically opt for a prior pansharpening of the multi-resolution source for a
full resolution processing. Here, we propose a new deep learning architecture
to jointly use PAN and MS imagery for a direct classification without any prior
image fusion or resampling process. By managing the spectral information at its
native spatial resolution, our method, named MRFusion, aims at avoiding the
possible infor- mation loss induced by pansharpening or any other hand-crafted
preprocessing. Moreover, the proposed architecture is suitably designed to
learn non-linear transformations of the sources with the explicit aim of taking
as much as possible advantage of the complementarity of PAN and MS imagery.
Experiments are carried out on two-real world scenarios depicting large areas
with different land cover characteristics. The characteristics of the proposed
scenarios underline the applicability and the generality of our method in
operational settings.
Using General Adversarial Networks for Marketing: A Case Study of Airbnb
Authors: Richard Diehl Martinez, John Kaleialoha Kamalu
Date: 2018-06-29 00:00:00
Tasks: Marketing

In this paper, we examine the use case of general adversarial networks (GANs)
in the field of marketing. In particular, we analyze how GAN models can
replicate text patterns from successful product listings on Airbnb, a
peer-to-peer online market for short-term apartment rentals. To do so, we
define the Diehl-Martinez-Kamalu (DMK) loss function as a new class of
functions that forces the model's generated output to include a set of
user-defined keywords. This allows the general adversarial network to recommend
a way of rewording the phrasing of a listing description to increase the
likelihood that it is booked. Although we tailor our analysis to Airbnb data,
we believe this framework establishes a more general model for how generative
algorithms can be used to produce text samples for the purposes of marketing.
Towards real-time unsupervised monocular depth estimation on CPU
Authors: Matteo Poggi, Filippo Aleotti, Fabio Tosi, Stefano Mattoccia
Date: 2018-06-29 00:00:00
Tasks: Autonomous Navigation, CPU, Depth Estimation, Image Reconstruction, Monocular Depth Estimation, Raspberry Pi 3, Unsupervised Monocular Depth Estimation

Unsupervised depth estimation from a single image is a very attractive
technique with several implications in robotic, autonomous navigation,
augmented reality and so on. This topic represents a very challenging task and
the advent of deep learning enabled to tackle this problem with excellent
results. However, these architectures are extremely deep and complex. Thus,
real-time performance can be achieved only by leveraging power-hungry GPUs that
do not allow to infer depth maps in application fields characterized by
low-power constraints. To tackle this issue, in this paper we propose a novel
architecture capable to quickly infer an accurate depth map on a CPU, even of
an embedded system, using a pyramid of features extracted from a single input
image. Similarly to state-of-the-art, we train our network in an unsupervised
manner casting depth estimation as an image reconstruction problem. Extensive
experimental results on the KITTI dataset show that compared to the top
performing approach our network has similar accuracy but a much lower
complexity (about 6% of parameters) enabling to infer a depth map for a KITTI
image in about 1.7 s on the Raspberry Pi 3 and at more than 8 Hz on a standard
CPU. Moreover, by trading accuracy for efficiency, our network allows to infer
maps at about 2 Hz and 40 Hz respectively, still being more accurate than most
state-of-the-art slower methods. To the best of our knowledge, it is the first
method enabling such performance on CPUs paving the way for effective
deployment of unsupervised monocular depth estimation even on embedded systems.
Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering
Authors: Shuyang Ling, Thomas Strohmer
Date: 2018-06-29 00:00:00
Tasks: Clustering

Spectral clustering has become one of the most widely used clustering
techniques when the structure of the individual clusters is non-convex or
highly anisotropic. Yet, despite its immense popularity, there exists fairly
little theory about performance guarantees for spectral clustering. This issue
is partly due to the fact that spectral clustering typically involves two steps
which complicated its theoretical analysis: first, the eigenvectors of the
associated graph Laplacian are used to embed the dataset, and second, k-means
clustering algorithm is applied to the embedded dataset to get the labels. This
paper is devoted to the theoretical foundations of spectral clustering and
graph cuts. We consider a convex relaxation of graph cuts, namely ratio cuts
and normalized cuts, that makes the usual two-step approach of spectral
clustering obsolete and at the same time gives rise to a rigorous theoretical
analysis of graph cuts and spectral clustering. We derive deterministic bounds
for successful spectral clustering via a spectral proximity condition that
naturally depends on the algebraic connectivity of each cluster and the
inter-cluster connectivity. Moreover, we demonstrate by means of some popular
examples that our bounds can achieve near-optimality. Our findings are also
fundamental for the theoretical understanding of kernel k-means. Numerical
simulations confirm and complement our analysis.
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
Authors: Chandrakant Bothe, Sven Magg, Cornelius Weber, Stefan Wermter
Date: 2018-06-29 00:00:00
Tasks: Spoken Language Understanding

Spoken language understanding is one of the key factors in a dialogue system,
and a context in a conversation plays an important role to understand the
current utterance. In this work, we demonstrate the importance of context
within the dialogue for neural network models through an online web interface
live demo. We developed two different neural models: a model that does not use
context and a context-based model. The no-context model classifies dialogue
acts at an utterance-level whereas the context-based model takes some preceding
utterances into account. We make these trained neural models available as a
live demo called Discourse-Wizard using a modular server architecture. The live
demo provides an easy to use interface for conversational analysis and for
discovering deep discourse structures in a conversation.
Bounds on the Approximation Power of Feedforward Neural Networks
Authors: Mohammad Mehrabi, Aslan Tchamkerten, Mansoor I. Yousefi
Date: 2018-06-29 00:00:00
Proceeding: ICML 2018 7

The approximation power of general feedforward neural networks with piecewise
linear activation functions is investigated. First, lower bounds on the size of
a network are established in terms of the approximation error and network depth
and width. These bounds improve upon state-of-the-art bounds for certain
classes of functions, such as strongly convex functions. Second, an upper bound
is established on the difference of two neural networks with identical weights
but different activation functions.
WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architecture
Authors: Amit Kumar Mishra
Date: 2018-06-29 00:00:00

Neuroethology has been an active field of study for more than a century now.
Out of some of the most interesting species that has been studied so far,
weakly electric fish is a fascinating one. It performs communication,
echo-location and inter-species detection efficiently with an interesting
configuration of sensors, neu-rons and a simple brain. In this paper we propose
a cognitive architecture inspired by the way these fishes handle and process
information. We believe that it is eas-ier to understand and mimic the neural
architectures of a simpler species than that of human. Hence, the proposed
architecture is expected to both help research in cognitive robotics and also
help understand more complicated brains like that of human beings.
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Authors: Sebastijan Dumancic, Alberto Garcia-Duran, Mathias Niepert
Date: 2018-06-29 00:00:00
Tasks: Clustering, General Classification, Knowledge Base Completion, Knowledge Graphs, Relational Reasoning, Representation Learning

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches, recent methods in (deep) representation learning has shown promising results for specialized tasks such as knowledge base completion. These approaches abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare representation learning and relational learning on various relational classification and clustering tasks and analyse the complexity of the rules used implicitly by these approaches. Preliminary results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.
Convergence Problems with Generative Adversarial Networks (GANs)
Authors: Samuel A. Barnett
Date: 2018-06-29 00:00:00

Generative adversarial networks (GANs) are a novel approach to generative
modelling, a task whose goal it is to learn a distribution of real data points.
They have often proved difficult to train: GANs are unlike many techniques in
machine learning, in that they are best described as a two-player game between
a discriminator and generator. This has yielded both unreliability in the
training process, and a general lack of understanding as to how GANs converge,
and if so, to what. The purpose of this dissertation is to provide an account
of the theory of GANs suitable for the mathematician, highlighting both
positive and negative results. This involves identifying the problems when
training GANs, and how topological and game-theoretic perspectives of GANs have
contributed to our understanding and improved our techniques in recent years.
Theory IIIb: Generalization in Deep Networks
Authors: Tomaso Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary
Date: 2018-06-29 00:00:00
Tasks: Binary Classification

A main puzzle of deep neural networks (DNNs) revolves around the apparent
absence of "overfitting", defined in this paper as follows: the expected error
does not get worse when increasing the number of neurons or of iterations of
gradient descent. This is surprising because of the large capacity demonstrated
by DNNs to fit randomly labeled data and the absence of explicit
regularization. Recent results by Srebro et al. provide a satisfying solution
of the puzzle for linear networks used in binary classification. They prove
that minimization of loss functions such as the logistic, the cross-entropy and
the exp-loss yields asymptotic, "slow" convergence to the maximum margin
solution for linearly separable datasets, independently of the initial
conditions. Here we prove a similar result for nonlinear multilayer DNNs near
zero minima of the empirical loss. The result holds for exponential-type losses
but not for the square loss. In particular, we prove that the weight matrix at
each layer of a deep network converges to a minimum norm solution up to a scale
factor (in the separable case). Our analysis of the dynamical system
corresponding to gradient descent of a multilayer network suggests a simple
criterion for ranking the generalization performance of different zero
minimizers of the empirical loss.
Learning from graphs with structural variation
Authors: Rune Kok Nielsen, Andreas Nugaard Holm, Aasa Feragen
Date: 2018-06-29 00:00:00

We study the effect of structural variation in graph data on the predictive
performance of graph kernels. To this end, we introduce a novel, noise-robust
adaptation of the GraphHopper kernel and validate it on benchmark data,
obtaining modestly improved predictive performance on a range of datasets.
Next, we investigate the performance of the state-of-the-art Weisfeiler-Lehman
graph kernel under increasing synthetic structural errors and find that the
effect of introducing errors depends strongly on the dataset.
Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning
Authors: Benjamin Kellenberger, Diego Marcos, Devis Tuia
Date: 2018-06-29 00:00:00
Tasks: object-detection, Object Detection

Knowledge over the number of animals in large wildlife reserves is a vital
necessity for park rangers in their efforts to protect endangered species.
Manual animal censuses are dangerous and expensive, hence Unmanned Aerial
Vehicles (UAVs) with consumer level digital cameras are becoming a popular
alternative tool to estimate livestock. Several works have been proposed that
semi-automatically process UAV images to detect animals, of which some employ
Convolutional Neural Networks (CNNs), a recent family of deep learning
algorithms that proved very effective in object detection in large datasets
from computer vision. However, the majority of works related to wildlife
focuses only on small datasets (typically subsets of UAV campaigns), which
might be detrimental when presented with the sheer scale of real study areas
for large mammal census. Methods may yield thousands of false alarms in such
cases. In this paper, we study how to scale CNNs to large wildlife census tasks
and present a number of recommendations to train a CNN on a large UAV dataset.
We further introduce novel evaluation protocols that are tailored to censuses
and model suitability for subsequent human verification of detections. Using
our recommendations, we are able to train a CNN reducing the number of false
positives by an order of magnitude compared to previous state-of-the-art.
Setting the requirements at 90% recall, our CNN allows to reduce the amount of
data required for manual verification by three times, thus making it possible
for rangers to screen all the data acquired efficiently and to detect almost
all animals in the reserve automatically.
Ignition: An End-to-End Supervised Model for Training Simulated Self-Driving Vehicles
Authors: Rooz Mahdavian, Richard Diehl Martinez
Date: 2018-06-29 00:00:00

We introduce Ignition: an end-to-end neural network architecture for training
unconstrained self-driving vehicles in simulated environments. The model is a
ResNet-18 variant, which is fed in images from the front of a simulated F1 car,
and outputs optimal labels for steering, throttle, braking. Importantly, we
never explicitly train the model to detect road features like the outline of a
track or distance to other cars; instead, we illustrate that these latent
features can be automatically encapsulated by the network.
Measuring the quality of Synthetic data for use in competitions
Authors: James Jordon, Jinsung Yoon, Mihaela van der Schaar
Date: 2018-06-29 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning has the potential to assist many communities in using the
large datasets that are becoming more and more available. Unfortunately, much
of that potential is not being realized because it would require sharing data
in a way that compromises privacy. In order to overcome this hurdle, several
methods have been proposed that generate synthetic data while preserving the
privacy of the real data. In this paper we consider a key characteristic that
synthetic data should have in order to be useful for machine learning
researchers - the relative performance of two algorithms (trained and tested)
on the synthetic dataset should be the same as their relative performance (when
trained and tested) on the original dataset.