pwc-archive/papers-with-abstracts huggingface dataset:
An asymptotic analysis of distributed nonparametric methods
Authors: Botond Szabo, Harry van Zanten
Date: 2017-11-08 00:00:00
Tasks: Uncertainty Quantification

We investigate and compare the fundamental performance of several distributed
learning methods that have been proposed recently. We do this in the context of
a distributed version of the classical signal-in-Gaussian-white-noise model,
which serves as a benchmark model for studying performance in this setting. The
results show how the design and tuning of a distributed method can have great
impact on convergence rates and validity of uncertainty quantification.
Moreover, we highlight the difficulty of designing nonparametric distributed
procedures that automatically adapt to smoothness.
Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication
Authors: Ahmed Elgammal, Yan Kang, Milko Den Leeuw
Date: 2017-11-08 00:00:00

This paper proposes a computational approach for analysis of strokes in line
drawings by artists. We aim at developing an AI methodology that facilitates
attribution of drawings of unknown authors in a way that is not easy to be
deceived by forged art. The methodology used is based on quantifying the
characteristics of individual strokes in drawings. We propose a novel algorithm
for segmenting individual strokes. We designed and compared different
hand-crafted and learned features for the task of quantifying stroke
characteristics. We also propose and compare different classification methods
at the drawing level. We experimented with a dataset of 300 digitized drawings
with over 80 thousands strokes. The collection mainly consisted of drawings of
Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of
representative works of other artists. The experiments shows that the proposed
methodology can classify individual strokes with accuracy 70%-90%, and
aggregate over drawings with accuracy above 80%, while being robust to be
deceived by fakes (with accuracy 100% for detecting fakes in most settings).
On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications
Authors: Clemente Rubio-Manzano, Martin Pereira-Fariña
Date: 2017-11-08 00:00:00

In this paper we analyse the benefits of incorporating interval-valued fuzzy
sets into the Bousi-Prolog system. A syntax, declarative semantics and im-
plementation for this extension is presented and formalised. We show, by using
potential applications, that fuzzy logic programming frameworks enhanced with
them can correctly work together with lexical resources and ontologies in order
to improve their capabilities for knowledge representation and reasoning.
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks
Authors: Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, Dana Kulić
Date: 2017-06-02 00:00:00
Tasks: Classification, Data Augmentation, General Classification

While convolutional neural networks (CNNs) have been successfully applied to
many challenging classification applications, they typically require large
datasets for training. When the availability of labeled data is limited, data
augmentation is a critical preprocessing step for CNNs. However, data
augmentation for wearable sensor data has not been deeply investigated yet.
  In this paper, various data augmentation methods for wearable sensor data are
proposed. The proposed methods and CNNs are applied to the classification of
the motor state of Parkinson's Disease patients, which is challenging due to
small dataset size, noisy labels, and large intra-class variability.
Appropriate augmentation improves the classification performance from 77.54\%
to 86.88\%.
EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures
Authors: Gus Kristiansen, Xavi Gonzalvo
Date: 2017-11-08 00:00:00

We present E NERGY N ET , a new framework for analyzing and building
artificial neural network architectures. Our approach adaptively learns the
structure of the networks in an unsupervised manner. The methodology is based
upon the theoretical guarantees of the energy function of restricted Boltzmann
machines (RBM) of infinite number of nodes. We present experimental results to
show that the final network adapts to the complexity of a given problem.
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Authors: Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, William T. Freeman, Joshua B. Tenenbaum
Date: 2017-11-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: 3D Object Reconstruction, 3D Object Reconstruction From A Single Image, 3D Reconstruction, 3D Shape Reconstruction, Domain Adaptation, Object, Object Reconstruction

3D object reconstruction from a single image is a highly under-determined
problem, requiring strong prior knowledge of plausible 3D shapes. This
introduces challenges for learning-based approaches, as 3D object annotations
are scarce in real images. Previous work chose to train on synthetic data with
ground truth 3D information, but suffered from domain adaptation when tested on
real data. In this work, we propose MarrNet, an end-to-end trainable model that
sequentially estimates 2.5D sketches and 3D object shape. Our disentangled,
two-step formulation has three advantages. First, compared to full 3D shape,
2.5D sketches are much easier to be recovered from a 2D image; models that
recover 2.5D sketches are also more likely to transfer from synthetic to real
data. Second, for 3D reconstruction from 2.5D sketches, systems can learn
purely from synthetic data. This is because we can easily render realistic 2.5D
sketches without modeling object appearance variations in real images,
including lighting, texture, etc. This further relieves the domain adaptation
problem. Third, we derive differentiable projective functions from 3D shape to
2.5D sketches; the framework is therefore end-to-end trainable on real images,
requiring no human annotations. Our model achieves state-of-the-art performance
on 3D shape reconstruction.
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
Authors: Daniel George, E. A. Huerta
Date: 2017-11-08 00:00:00
Tasks: Gravitational Wave Detection, parameter estimation, Time Series, Time Series Analysis

The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent field of
science, we pioneered the use of deep learning with convolutional neural
networks, that take time-series inputs, for rapid detection and
characterization of gravitational wave signals. This approach, Deep Filtering,
was initially demonstrated using simulated LIGO noise. In this article, we
present the extension of Deep Filtering using real data from LIGO, for both
detection and parameter estimation of gravitational waves from binary black
hole mergers using continuous data streams from multiple LIGO detectors. We
demonstrate for the first time that machine learning can detect and estimate
the true parameters of real events observed by LIGO. Our results show that Deep
Filtering achieves similar sensitivities and lower errors compared to
matched-filtering while being far more computationally efficient and more
resilient to glitches, allowing real-time processing of weak time-series
signals in non-stationary non-Gaussian noise with minimal resources, and also
enables the detection of new classes of gravitational wave sources that may go
unnoticed with existing detection algorithms. This unified framework for data
analysis is ideally suited to enable coincident detection campaigns of
gravitational waves and their multimessenger counterparts in real-time.
Network-size independent covering number bounds for deep networks
Authors: Mayank Kabra, Kristin Branson
Date: 2017-11-02 00:00:00

We give a covering number bound for deep learning networks that is
independent of the size of the network. The key for the simple analysis is that
for linear classifiers, rotating the data doesn't affect the covering number.
Thus, we can ignore the rotation part of each layer's linear transformation,
and get the covering number bound by concentrating on the scaling part.
Exploration in NetHack With Secret Discovery
Authors: Jonathan C. Campbell, Clark Verbrugge
Date: 2017-11-08 00:00:00
Tasks: NetHack

Roguelike games generally feature exploration problems as a critical, yet
often repetitive element of gameplay. Automated approaches, however, face
challenges in terms of optimality, as well as due to incomplete information,
such as from the presence of secret doors. This paper presents an algorithmic
approach to exploration of roguelike dungeon environments. Our design aims to
minimize exploration time, balancing coverage and discovery of secret areas
with resource cost. Our algorithm is based on the concept of occupancy maps
popular in robotics, adapted to encourage efficient discovery of secret access
points. Through extensive experimentation on NetHack maps we show that this
technique is significantly more efficient than simpler greedy approaches and an
existing automated player. We further investigate optimized parameterization
for the algorithm through a comprehensive data analysis. These results point
towards better automation for players as well as heuristics applicable to fully
automated gameplay.
Offline signature authenticity verification through unambiguously connected skeleton segments
Authors: Jugurta Montalvão, Luiz Miranda, Jânio Canuto
Date: 2017-11-08 00:00:00
Tasks: Dynamic Time Warping

A method for offline signature verification is presented in this paper. It is
based on the segmentation of the signature skeleton (through standard image
skeletonization) into unambiguous sequences of points, or unambiguously
connected skeleton segments corresponding to vectorial representations of
signature portions. These segments are assumed to be the fundamental carriers
of useful information for authenticity verification, and are compactly encoded
as sets of 9 scalars (4 sampled coordinates and 1 length measure). Thus
signature authenticity is inferred through Euclidean distance based comparisons
between pairs of such compact representations. The average performance of this
method is evaluated through experiments with offline versions of signatures
from the MCYT-100 database. For comparison purposes, three other approaches are
applied to the same set of signatures, namely: (1) a straightforward approach
based on Dynamic Time Warping distances between segments, (2) a published
method by [shanker2007], also based on DTW, and (3) the average human
performance under equivalent experimental protocol. Results suggest that if
human performance is taken as a goal for automatic verification, then we should
discard signature shape details to approach this goal. Moreover, our best
result -- close to human performance -- was obtained by the simplest strategy,
where equal weights were given to segment shape and length.
Head Detection with Depth Images in the Wild
Authors: Diego Ballotta, Guido Borghi, Roberto Vezzani, Rita Cucchiara
Date: 2017-07-21 00:00:00
Tasks: Head Detection

Head detection and localization is a demanding task and a key element for
many computer vision applications, like video surveillance, Human Computer
Interaction and face analysis. The stunning amount of work done for detecting
faces on RGB images, together with the availability of huge face datasets,
allowed to setup very effective systems on that domain. However, due to
illumination issues, infrared or depth cameras may be required in real
applications. In this paper, we introduce a novel method for head detection on
depth images that exploits the classification ability of deep learning
approaches. In addition to reduce the dependency on the external illumination,
depth images implicitly embed useful information to deal with the scale of the
target objects. Two public datasets have been exploited: the first one, called
Pandora, is used to train a deep binary classifier with face and non-face
images. The second one, collected by Cornell University, is used to perform a
cross-dataset test during daily activities in unconstrained environments.
Experimental results show that the proposed method overcomes the performance of
state-of-art methods working on depth images.
Bayesian GAN
Authors: Yunus Saatchi, Andrew Gordon Wilson
Date: 2017-05-26 00:00:00
Proceeding: NeurIPS 2017

Generative adversarial networks (GANs) can implicitly learn rich
distributions over images, audio, and data which are hard to model with an
explicit likelihood. We present a practical Bayesian formulation for
unsupervised and semi-supervised learning with GANs. Within this framework, we
use stochastic gradient Hamiltonian Monte Carlo to marginalize the weights of
the generator and discriminator networks. The resulting approach is
straightforward and obtains good performance without any standard interventions
such as feature matching, or mini-batch discrimination. By exploring an
expressive posterior over the parameters of the generator, the Bayesian GAN
avoids mode-collapse, produces interpretable and diverse candidate samples, and
provides state-of-the-art quantitative results for semi-supervised learning on
benchmarks including SVHN, CelebA, and CIFAR-10, outperforming DCGAN,
Wasserstein GANs, and DCGAN ensembles.
Deep Kernels for Optimizing Locomotion Controllers
Authors: Rika Antonova, Akshara Rai, Christopher G. Atkeson
Date: 2017-07-27 00:00:00
Tasks: Bayesian Optimization

Sample efficiency is important when optimizing parameters of locomotion
controllers, since hardware experiments are time consuming and expensive.
Bayesian Optimization, a sample-efficient optimization framework, has recently
been widely applied to address this problem, but further improvements in sample
efficiency are needed for practical applicability to real-world robots and
high-dimensional controllers. To address this, prior work has proposed using
domain expertise for constructing custom distance metrics for locomotion. In
this work we show how to learn such a distance metric automatically. We use a
neural network to learn an informed distance metric from data obtained in
high-fidelity simulations. We conduct experiments on two different controllers
and robot architectures. First, we demonstrate improvement in sample efficiency
when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We
then conduct simulation experiments to optimize a 16-dimensional controller for
a 7-link robot model and obtain significant improvements even when optimizing
in perturbed environments. This demonstrates that our approach is able to
enhance sample efficiency for two different controllers, hence is a fitting
candidate for further experiments on hardware in the future.
Multi-Period Flexibility Forecast for Low Voltage Prosumers
Authors: Rui Pinto, Ricardo Bessa, Manuel Matos
Date: 2017-03-26 00:00:00
Tasks: energy management, Management

Near-future electric distribution grids operation will have to rely on
demand-side flexibility, both by implementation of demand response strategies
and by taking advantage of the intelligent management of increasingly common
small-scale energy storage. The Home energy management system (HEMS), installed
at low voltage residential clients, will play a crucial role on the flexibility
provision to both system operators and market players like aggregators.
Modeling and forecasting multi-period flexibility from residential prosumers,
such as battery storage and electric water heater, while complying with
internal constraints (comfort levels, data privacy) and uncertainty is a
complex task. This papers describes a computational method that is capable of
efficiently learn and define the feasibility flexibility space from
controllable resources connected to a HEMS. An Evolutionary Particle Swarm
Optimization (EPSO) algorithm is adopted and reshaped to derive a set of
feasible temporal trajectories for the residential net-load, considering
storage, flexible appliances, and predefined costumer preferences, as well as
load and photovoltaic (PV) forecast uncertainty. A support vector data
description (SVDD) algorithm is used to build models capable of classifying
feasible and non-feasible HEMS operating trajectories upon request from an
optimization/control algorithm operated by a DSO or market player.
Data Fusion and Machine Learning Integration for Transformer Loss of Life Estimation
Authors: Mohsen Mahoor, Amin Khodaei
Date: 2017-11-08 00:00:00
Tasks: Asset Management, BIG-bench Machine Learning, Management

Rapid growth of machine learning methodologies and their applications offer
new opportunity for improved transformer asset management. Accordingly, power
system operators are currently looking for data-driven methods to make
better-informed decisions in terms of network management. In this paper,
machine learning and data fusion techniques are integrated to estimate
transformer loss of life. Using IEEE Std. C57.91-2011, a data synthesis process
is proposed based on hourly transformer loading and ambient temperature values.
This synthesized data is employed to estimate transformer loss of life by using
Adaptive Network-Based Fuzzy Inference System (ANFIS) and Radial Basis Function
(RBF) network, which are further fused together with the objective of improving
the estimation accuracy. Among various data fusion techniques, Ordered Weighted
Averaging (OWA) and sequential Kalman filter are selected to fuse the output
results of the estimated ANFIS and RBF. Simulation results demonstrate the
merit and the effectiveness of the proposed method.
Recency-weighted Markovian inference
Authors: Kristjan Kalm
Date: 2017-11-08 00:00:00

We describe a Markov latent state space (MLSS) model, where the latent state
distribution is a decaying mixture over multiple past states. We present a
simple sampling algorithm that allows to approximate such high-order MLSS with
fixed time and memory costs.
Accelerating Neural Architecture Search using Performance Prediction
Authors: Bowen Baker, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
Date: 2017-05-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Hyperparameter Optimization, Language Modeling, Language Modelling, Neural Architecture Search, Prediction, Reinforcement Learning, Time Series, Time Series Analysis

Methods for neural network hyperparameter optimization and meta-modeling are
computationally expensive due to the need to train a large number of model
configurations. In this paper, we show that standard frequentist regression
models can predict the final performance of partially trained model
configurations using features based on network architectures, hyperparameters,
and time-series validation performance data. We empirically show that our
performance prediction models are much more effective than prominent Bayesian
counterparts, are simpler to implement, and are faster to train. Our models can
predict final performance in both visual classification and language modeling
domains, are effective for predicting performance of drastically varying model
architectures, and can even generalize between model classes. Using these
prediction models, we also propose an early stopping method for hyperparameter
optimization and meta-modeling, which obtains a speedup of a factor up to 6x in
both hyperparameter optimization and meta-modeling. Finally, we empirically
show that our early stopping method can be seamlessly incorporated into both
reinforcement learning-based architecture selection algorithms and bandit based
search methods. Through extensive experimentation, we empirically show our
performance prediction models and early stopping algorithm are state-of-the-art
in terms of prediction accuracy and speedup achieved while still identifying
the optimal model configurations.
Intelligent Fault Analysis in Electrical Power Grids
Authors: Biswarup Bhattacharya, Abhishek Sinha
Date: 2017-11-08 00:00:00

Power grids are one of the most important components of infrastructure in
today's world. Every nation is dependent on the security and stability of its
own power grid to provide electricity to the households and industries. A
malfunction of even a small part of a power grid can cause loss of
productivity, revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid and take
protective measures accordingly even before a serious anomaly takes place. To
achieve this objective, we have set out to create an artificially intelligent
system which can analyze the grid information at any given time and determine
the health of the grid through the usage of sophisticated formal models and
novel machine learning techniques like recurrent neural networks. Our system
simulates grid conditions including stimuli like faults, generator output
fluctuations, load fluctuations using Siemens PSS/E software and this data is
trained using various classifiers like SVM, LSTM and subsequently tested. The
results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid
architectures.
Information Theoretic Analysis of DNN-HMM Acoustic Modeling
Authors: Pranay Dighe, Afsaneh Asaei, Hervé Bourlard
Date: 2017-08-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose an information theoretic framework for quantitative assessment of
acoustic modeling for hidden Markov model (HMM) based automatic speech
recognition (ASR). Acoustic modeling yields the probabilities of HMM sub-word
states for a short temporal window of speech acoustic features. We cast ASR as
a communication channel where the input sub-word probabilities convey the
information about the output HMM state sequence. The quality of the acoustic
model is thus quantified in terms of the information transmitted through this
channel. The process of inferring the most likely HMM state sequence from the
sub-word probabilities is known as decoding. HMM based decoding assumes that an
acoustic model yields accurate state-level probabilities and the data
distribution given the underlying hidden state is independent of any other
state in the sequence. We quantify 1) the acoustic model accuracy and 2) its
robustness to mismatch between data and the HMM conditional independence
assumption in terms of some mutual information quantities. In this context,
exploiting deep neural network (DNN) posterior probabilities leads to a simple
and straightforward analysis framework to assess shortcomings of the acoustic
model for HMM based decoding. This analysis enables us to evaluate the Gaussian
mixture acoustic model (GMM) and the importance of many hidden layers in DNNs
without any need of explicit speech recognition. In addition, it sheds light on
the contribution of low-dimensional models to enhance acoustic modeling for
better compliance with the HMM based decoding requirements.