pwc-archive/papers-with-abstracts huggingface dataset:
Solving Equations of Random Convex Functions via Anchored Regression
Authors: Sohail Bahmani, Justin Romberg
Date: 2017-02-17 00:00:00
Tasks: regression

We consider the question of estimating a solution to a system of equations
that involve convex nonlinearities, a problem that is common in machine
learning and signal processing. Because of these nonlinearities, conventional
estimators based on empirical risk minimization generally involve solving a
non-convex optimization program. We propose anchored regression, a new approach
based on convex programming that amounts to maximizing a linear functional
(perhaps augmented by a regularizer) over a convex set. The proposed convex
program is formulated in the natural space of the problem, and avoids the
introduction of auxiliary variables, making it computationally favorable.
Working in the native space also provides great flexibility as structural
priors (e.g., sparsity) can be seamlessly incorporated.
  For our analysis, we model the equations as being drawn from a fixed set
according to a probability law. Our main results provide guarantees on the
accuracy of the estimator in terms of the number of equations we are solving,
the amount of noise present, a measure of statistical complexity of the random
equations, and the geometry of the regularizer at the true solution. We also
provide recipes for constructing the anchor vector (that determines the linear
functional to maximize) directly from the observed data.
Computer Assisted Composition with Recurrent Neural Networks
Authors: Christian Walder, Dongwoo Kim
Date: 2016-12-01 00:00:00

Sequence modeling with neural networks has lead to powerful models of
symbolic music data. We address the problem of exploiting these models to reach
creative musical goals, by combining with human input. To this end we
generalise previous work, which sampled Markovian sequence models under the
constraint that the sequence belong to the language of a given finite state
machine provided by the human. We consider more expressive non-Markov models,
thereby requiring approximate sampling which we provide in the form of an
efficient sequential Monte Carlo method. In addition we provide and compare
with a beam search strategy for conditional probability maximisation.
  Our algorithms are capable of convincingly re-harmonising famous musical
works. To demonstrate this we provide visualisations, quantitative experiments,
a human listening test and audio examples. We find both the sampling and
optimisation procedures to be effective, yet complementary in character. For
the case of highly permissive constraint sets, we find that sampling is to be
preferred due to the overly regular nature of the optimisation based results.
The generality of our algorithms permits countless other creative applications.
CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion
Authors: Vidyasagar Sadhu, Dario Pompili, Saman Zonouz, Vincent Sritapan
Date: 2017-09-29 00:00:00
Tasks: Privacy Preserving

Mobile phones provide an excellent opportunity for building context-aware
applications. In particular, location-based services are important
context-aware services that are more and more used for enforcing security
policies, for supporting indoor room navigation, and for providing personalized
assistance. However, a major problem still remains unaddressed---the lack of
solutions that work across buildings while not using additional infrastructure
and also accounting for privacy and reliability needs. In this paper, a
privacy-preserving, multi-modal, cross-building, collaborative localization
platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular
RSSI, sound and light levels, that enables room-level localization as main
application (though sub room level granularity is possible). The privacy is
inherently built into the solution based on onion routing, and
perturbation/randomization techniques, and exploits the idea of weighted
collaboration to increase the reliability as well as to limit the effect of
noisy devices (due to sensor noise/privacy). The proposed solution has been
analyzed in terms of privacy, accuracy, optimum parameters, and other overheads
on location data collected at multiple indoor and outdoor locations using an
Android app.
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case
Authors: Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh, Mike A. Nalls, Saurabh Sinha, Roy H. Campbell
Date: 2017-09-29 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Dimensionality Reduction, General Classification, regression

In an effort to overcome the data deluge in computational biology and
bioinformatics and to facilitate bioinformatics research in the era of big
data, we identify some of the most influential algorithms that have been widely
used in the bioinformatics community. These top data mining and machine
learning algorithms cover classification, clustering, regression, graphical
model-based learning, and dimensionality reduction. The goal of this study is
to guide the focus of scalable computing experts in the endeavor of applying
new storage and scalable computation designs to bioinformatics algorithms that
merit their attention most, following the engineering maxim of "optimize the
common case".
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
Authors: Viraj Shah, Mohammadreza Soltani, Chinmay Hegde
Date: 2017-09-29 00:00:00
Tasks: Compressive Sensing

We consider the problem of reconstructing signals and images from periodic
nonlinearities. For such problems, we design a measurement scheme that supports
efficient reconstruction; moreover, our method can be adapted to extend to
compressive sensing-based signal and image acquisition systems. Our techniques
can be potentially useful for reducing the measurement complexity of high
dynamic range (HDR) imaging systems, with little loss in reconstruction
quality. Several numerical experiments on real data demonstrate the
effectiveness of our approach.
Language-depedent I-Vectors for LRE15
Authors: Niko Brümmer, Albert Swart
Date: 2017-09-29 00:00:00

A standard recipe for spoken language recognition is to apply a Gaussian
back-end to i-vectors. This ignores the uncertainty in the i-vector extraction,
which could be important especially for short utterances. A recent paper by
Cumani, Plchot and Fer proposes a solution to propagate that uncertainty into
the backend. We propose an alternative method of propagating the uncertainty.
Model-free prediction of noisy chaotic time series by deep learning
Authors: Kyongmin Yeo
Date: 2017-09-29 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

We present a deep neural network for a model-free prediction of a chaotic
dynamical system from noisy observations. The proposed deep learning model aims
to predict the conditional probability distribution of a state variable. The
Long Short-Term Memory network (LSTM) is employed to model the nonlinear
dynamics and a softmax layer is used to approximate a probability distribution.
The LSTM model is trained by minimizing a regularized cross-entropy function.
The LSTM model is validated against delay-time chaotic dynamical systems,
Mackey-Glass and Ikeda equations. It is shown that the present LSTM makes a
good prediction of the nonlinear dynamics by effectively filtering out the
noise. It is found that the prediction uncertainty of a multiple-step forecast
of the LSTM model is not a monotonic function of time; the predicted standard
deviation may increase or decrease dynamically in time.
Learning the Exact Topology of Undirected Consensus Networks
Authors: Saurav Talukdar, Deepjyoti Deka, Sandeep Attree, Donatello Materassi, Murti V. Salapaka
Date: 2017-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

In this article, we present a method to learn the interaction topology of a
network of agents undergoing linear consensus updates in a non invasive manner.
Our approach is based on multivariate Wiener filtering, which is known to
recover spurious edges apart from the true edges in the topology. The main
contribution of this work is to show that in the case of undirected consensus
networks, all spurious links obtained using Wiener filtering can be identified
using frequency response of the Wiener filters. Thus, the exact interaction
topology of the agents is unveiled. The method presented requires time series
measurements of the state of the agents and does not require any knowledge of
link weights. To the best of our knowledge this is the first approach that
provably reconstructs the structure of undirected consensus networks with
correlated noise. We illustrate the effectiveness of the method developed
through numerical simulations as well as experiments on a five node network of
Raspberry Pis.
Efficient acquisition rules for model-based approximate Bayesian computation
Authors: Marko Järvenpää, Michael U. Gutmann, Arijus Pleska, Aki Vehtari, Pekka Marttinen
Date: 2017-04-03 00:00:00
Tasks: Bayesian Inference, Bayesian Optimisation, Gaussian Processes

Approximate Bayesian computation (ABC) is a method for Bayesian inference
when the likelihood is unavailable but simulating from the model is possible.
However, many ABC algorithms require a large number of simulations, which can
be costly. To reduce the computational cost, Bayesian optimisation (BO) and
surrogate models such as Gaussian processes have been proposed. Bayesian
optimisation enables one to intelligently decide where to evaluate the model
next but common BO strategies are not designed for the goal of estimating the
posterior distribution. Our paper addresses this gap in the literature. We
propose to compute the uncertainty in the ABC posterior density, which is due
to a lack of simulations to estimate this quantity accurately, and define a
loss function that measures this uncertainty. We then propose to select the
next evaluation location to minimise the expected loss. Experiments show that
the proposed method often produces the most accurate approximations as compared
to common BO strategies.
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies
Authors: Seppo Enarvi, Peter Smit, Sami Virpioja, Mikko Kurimo
Date: 2017-07-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Today, the vocabulary size for language models in large vocabulary speech
recognition is typically several hundreds of thousands of words. While this is
already sufficient in some applications, the out-of-vocabulary words are still
limiting the usability in others. In agglutinative languages the vocabulary for
conversational speech should include millions of word forms to cover the
spelling variations due to colloquial pronunciations, in addition to the word
compounding and inflections. Very large vocabularies are also needed, for
example, when the recognition of rare proper names is important.
Unsupervised Domain Adaptation with Copula Models
Authors: Cuong D. Tran, Ognjen Rudovic, Vladimir Pavlovic
Date: 2017-09-29 00:00:00
Tasks: Domain Adaptation, regression, Unsupervised Domain Adaptation

We study the task of unsupervised domain adaptation, where no labeled data
from the target domain is provided during training time. To deal with the
potential discrepancy between the source and target distributions, both in
features and labels, we exploit a copula-based regression framework. The
benefits of this approach are two-fold: (a) it allows us to model a broader
range of conditional predictive densities beyond the common exponential family,
(b) we show how to leverage Sklar's theorem, the essence of the copula
formulation relating the joint density to the copula dependency functions, to
find effective feature mappings that mitigate the domain mismatch. By
transforming the data to a copula domain, we show on a number of benchmark
datasets (including human emotion estimation), and using different regression
models for prediction, that we can achieve a more robust and accurate
estimation of target labels, compared to recently proposed feature
transformation (adaptation) methods.
Hierarchical modeling of molecular energies using a deep neural network
Authors: Nicholas Lubbers, Justin S. Smith, Kipton Barros
Date: 2017-09-29 00:00:00
Tasks: Drug Discovery, Formation Energy

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN)
to model molecular properties from datasets of quantum calculations. Inspired
by a many-body expansion, HIP-NN decomposes properties, such as energy, as a
sum over hierarchical terms. These terms are generated from a neural network--a
composition of many nonlinear transformations--acting on a representation of
the molecule. HIP-NN achieves state-of-the-art performance on a dataset of 131k
ground state organic molecules, and predicts energies with 0.26 kcal/mol mean
absolute error. With minimal tuning, our model is also competitive on a dataset
of molecular dynamics trajectories. In addition to enabling accurate energy
predictions, the hierarchical structure of HIP-NN helps to identify regions of
model uncertainty.
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts
Authors: Corneliu T. C. Arsene, Stephen Church, Mark Dickinson
Date: 2016-12-19 00:00:00
Tasks: Clustering, Dimensionality Reduction

Multispectral imaging is an important technique for improving the readability
of written or printed text where the letters have faded, either due to
deliberate erasing or simply due to the ravages of time. Often the text can be
read simply by looking at individual wavelengths, but in some cases the images
need further enhancement to maximise the chances of reading the text. There are
many possible enhancement techniques and this paper assesses and compares an
extended set of dimensionality reduction methods for image processing. We
assess 15 dimensionality reduction methods in two different manuscripts. This
assessment was performed both subjectively by asking the opinions of scholars
who were experts in the languages used in the manuscripts which of the
techniques they preferred and also by using the Davies-Bouldin and Dunn indexes
for assessing the quality of the resulted image clusters. We found that the
Canonical Variates Analysis (CVA) method which was using a Matlab
implementation and we have used previously to enhance multispectral images, it
was indeed superior to all the other tested methods. However it is very likely
that other approaches will be more suitable in specific circumstance so we
would still recommend that a range of these techniques are tried. In
particular, CVA is a supervised clustering technique so it requires
considerably more user time and effort than a non-supervised technique such as
the much more commonly used Principle Component Analysis Approach (PCA). If the
results from PCA are adequate to allow a text to be read then the added effort
required for CVA may not be justified. For the purposes of comparing the
computational times and the image results, a CVA method is also implemented in
C programming language and using the GNU (GNUs Not Unix) Scientific Library
(GSL) and the OpenCV (OPEN source Computer Vision) computer vision programming
library.
Vision-based deep execution monitoring
Authors: Francesco Puja, Simone Grazioso, Antonio Tammaro, Valsmis Ntouskos, Marta Sanzari, Fiora Pirri
Date: 2017-09-29 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Execution monitor of high-level robot actions can be effectively improved by
visual monitoring the state of the world in terms of preconditions and
postconditions that hold before and after the execution of an action.
Furthermore a policy for searching where to look at, either for verifying the
relations that specify the pre and postconditions or to refocus in case of a
failure, can tremendously improve the robot execution in an uncharted
environment. It is now possible to strongly rely on visual perception in order
to make the assumption that the environment is observable, by the amazing
results of deep learning. In this work we present visual execution monitoring
for a robot executing tasks in an uncharted Lab environment. The execution
monitor interacts with the environment via a visual stream that uses two DCNN
for recognizing the objects the robot has to deal with and manipulate, and a
non-parametric Bayes estimation to discover the relations out of the DCNN
features. To recover from lack of focus and failures due to missed objects we
resort to visual search policies via deep reinforcement learning.
Symbol, Conversational, and Societal Grounding with a Toy Robot
Authors: Casey Kennington, Sarah Plane
Date: 2017-09-29 00:00:00

Essential to meaningful interaction is grounding at the symbolic,
conversational, and societal levels. We present ongoing work with Anki's Cozmo
toy robot as a research platform where we leverage the recent
words-as-classifiers model of lexical semantics in interactive reference
resolution tasks for language grounding.
Assumption-Based Approaches to Reasoning with Priorities
Authors: Jesse Heyninck, Christian Straßer, Pere Pardo
Date: 2017-09-21 00:00:00
Tasks: Relation

This paper maps out the relation between different approaches for handling
preferences in argumentation with strict rules and defeasible assumptions by
offering translations between them. The systems we compare are: non-prioritized
defeats i.e. attacks, preference-based defeats, and preference-based defeats
extended with reverse defeat.
Dynamic Assortment Personalization in High Dimensions
Authors: Nathan Kallus, Madeleine Udell
Date: 2016-10-18 00:00:00
Tasks: Management, Vocal Bursts Intensity Prediction

We study the problem of dynamic assortment personalization with large, heterogeneous populations and wide arrays of products, and demonstrate the importance of structural priors for effective, efficient large-scale personalization. Assortment personalization is the problem of choosing, for each individual (type), a best assortment of products, ads, or other offerings (items) so as to maximize revenue. This problem is central to revenue management in e-commerce and online advertising where both items and types can number in the millions. We formulate the dynamic assortment personalization problem as a discrete-contextual bandit with $m$ contexts (types) and exponentially many arms (assortments of the $n$ items). We assume that each type's preferences follow a simple parametric model with $n$ parameters. In all, there are $mn$ parameters, and existing literature suggests that order optimal regret scales as $mn$. However, the data required to estimate so many parameters is orders of magnitude larger than the data available in most revenue management applications; and the optimal regret under these models is unacceptably high. In this paper, we impose a natural structure on the problem -- a small latent dimension, or low rank. In the static setting, we show that this model can be efficiently learned from surprisingly few interactions, using a time- and memory-efficient optimization algorithm that converges globally whenever the model is learnable. In the dynamic setting, we show that structure-aware dynamic assortment personalization can have regret that is an order of magnitude smaller than structure-ignorant approaches. We validate our theoretical results empirically.
Improving image generative models with human interactions
Authors: Andrew Kyle Lampinen, David So, Douglas Eck, Fred Bertsch
Date: 2017-09-29 00:00:00
Proceeding: ICLR 2018 1

GANs provide a framework for training generative models which mimic a data
distribution. However, in many cases we wish to train these generative models
to optimize some auxiliary objective function within the data it generates,
such as making more aesthetically pleasing images. In some cases, these
objective functions are difficult to evaluate, e.g. they may require human
interaction. Here, we develop a system for efficiently improving a GAN to
target an objective involving human interaction, specifically generating images
that increase rates of positive user interactions. To improve the generative
model, we build a model of human behavior in the targeted domain from a
relatively small set of interactions, and then use this behavioral model as an
auxiliary loss function to improve the generative model. We show that this
system is successful at improving positive interaction rates, at least on
simulated data, and characterize some of the factors that affect its
performance.
Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models
Authors: Jakub Repicky, Lukas Bajer, Zbynek Pitra, Martin Holena
Date: 2017-09-29 00:00:00
Tasks: Gaussian Processes

The interest in accelerating black-box optimizers has resulted in several
surrogate model-assisted version of the Covariance Matrix Adaptation Evolution
Strategy, a state-of-the-art continuous black-box optimizer. The version called
Surrogate CMA-ES uses Gaussian processes or random forests surrogate models
with a generation-based evolution control. This paper presents an adaptive
improvement for S-CMA-ES based on a general procedure introduced with the
s*ACM-ES algorithm, in which the number of generations using the surrogate
model before retraining is adjusted depending on the performance of the last
instance of the surrogate. Three algorithms that differ in the measure of the
surrogate model's performance are evaluated on the COCO/BBOB framework. The
results show a minor improvement on S-CMA-ES with constant model lifelengths,
especially when larger lifelengths are considered.
Optimisation of photometric stereo methods by non-convex variational minimisation
Authors: Georg Radow, Laurent Hoeltgen, Yvain Quéau, Michael Breuß
Date: 2017-09-29 00:00:00

Estimating shape and appearance of a three dimensional object from a given
set of images is a classic research topic that is still actively pursued. Among
the various techniques available, PS is distinguished by the assumption that
the underlying input images are taken from the same point of view but under
different lighting conditions. The most common techniques provide the shape
information in terms of surface normals. In this work, we instead propose to
minimise a much more natural objective function, namely the reprojection error
in terms of depth. Minimising the resulting non-trivial variational model for
PS allows to recover the depth of the photographed scene directly. As a solving
strategy, we follow an approach based on a recently published optimisation
scheme for non-convex and non-smooth cost functions.
  The main contributions of our paper are of theoretical nature. A technical
novelty in our framework is the usage of matrix differential calculus. We
supplement our approach by a detailed convergence analysis of the resulting
optimisation algorithm and discuss possibilities to ease the computational
complexity. At hand of an experimental evaluation we discuss important
properties of the method. Overall, our strategy achieves more accurate results
than competing approaches. The experiments also highlights some practical
aspects of the underlying optimisation algorithm that may be of interest in a
more general context.