pwc-archive/papers-with-abstracts huggingface dataset:
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent
Authors: Xingwen Zhang, Jeff Clune, Kenneth O. Stanley
Date: 2017-12-18 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Because stochastic gradient descent (SGD) has shown promise optimizing neural
networks with millions of parameters and few if any alternatives are known to
exist, it has moved to the heart of leading approaches to reinforcement
learning (RL). For that reason, the recent result from OpenAI showing that a
particular kind of evolution strategy (ES) can rival the performance of
SGD-based deep RL methods with large neural networks provoked surprise. This
result is difficult to interpret in part because of the lingering ambiguity on
how ES actually relates to SGD. The aim of this paper is to significantly
reduce this ambiguity through a series of MNIST-based experiments designed to
uncover their relationship. As a simple supervised problem without domain noise
(unlike in most RL), MNIST makes it possible (1) to measure the correlation
between gradients computed by ES and SGD and (2) then to develop an SGD-based
proxy that accurately predicts the performance of different ES population
sizes. These innovations give a new level of insight into the real capabilities
of ES, and lead also to some unconventional means for applying ES to supervised
problems that shed further light on its differences from SGD. Incorporating
these lessons, the paper concludes by demonstrating that ES can achieve 99%
accuracy on MNIST, a number higher than any previously published result for any
evolutionary method. While not by any means suggesting that ES should
substitute for SGD in supervised learning, the suite of experiments herein
enables more informed decisions on the application of ES within RL and other
paradigms.
Nonparametric Inference for Auto-Encoding Variational Bayes
Authors: Erik Bodin, Iman Malik, Carl Henrik Ek, Neill D. F. Campbell
Date: 2017-12-18 00:00:00

We would like to learn latent representations that are low-dimensional and
highly interpretable. A model that has these characteristics is the Gaussian
Process Latent Variable Model. The benefits and negative of the GP-LVM are
complementary to the Variational Autoencoder, the former provides interpretable
low-dimensional latent representations while the latter is able to handle large
amounts of data and can use non-Gaussian likelihoods. Our inspiration for this
paper is to marry these two approaches and reap the benefits of both. In order
to do so we will introduce a novel approximate inference scheme inspired by the
GP-LVM and the VAE. We show experimentally that the approximation allows the
capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large
without losing a highly interpretable representation, allowing reconstruction
quality to be unlimited by Z at the same time as a low-dimensional space can be
used to perform ancestral sampling from as well as a means to reason about the
embedded data.
Deep generative models of genetic variation capture mutation effects
Authors: Adam J. Riesselman, John B. Ingraham, Debora S. Marks
Date: 2017-12-18 00:00:00

The functions of proteins and RNAs are determined by a myriad of interactions
between their constituent residues, but most quantitative models of how
molecular phenotype depends on genotype must approximate this by simple
additive effects. While recent models have relaxed this constraint to also
account for pairwise interactions, these approaches do not provide a tractable
path towards modeling higher-order dependencies. Here, we show how latent
variable models with nonlinear dependencies can be applied to capture
beyond-pairwise constraints in biomolecules. We present a new probabilistic
model for sequence families, DeepSequence, that can predict the effects of
mutations across a variety of deep mutational scanning experiments
significantly better than site independent or pairwise models that are based on
the same evolutionary data. The model, learned in an unsupervised manner solely
from sequence information, is grounded with biologically motivated priors,
reveals latent organization of sequence families, and can be used to
extrapolate to new parts of sequence space
IMU2Face: Real-time Gesture-driven Facial Reenactment
Authors: Justus Thies, Michael Zollhöfer, Matthias Nießner
Date: 2017-12-18 00:00:00

We present IMU2Face, a gesture-driven facial reenactment system. To this end,
we combine recent advances in facial motion capture and inertial measurement
units (IMUs) to control the facial expressions of a person in a target video
based on intuitive hand gestures. IMUs are omnipresent, since modern
smart-phones, smart-watches and drones integrate such sensors, e.g., for
changing the orientation of the screen content, counting steps, or for flight
stabilization. Face tracking and reenactment is based on the state-of-the-art
real-time Face2Face facial reenactment system. Instead of transferring facial
expressions from a source to a target actor, we employ an IMU to track the hand
gestures of a source actor and use its orientation to modify the target actor's
expressions.
Guiding human gaze with convolutional neural networks
Authors: Leon A. Gatys, Matthias Kümmerer, Thomas S. A. Wallis, Matthias Bethge
Date: 2017-12-18 00:00:00

The eye fixation patterns of human observers are a fundamental indicator of
the aspects of an image to which humans attend. Thus, manipulating fixation
patterns to guide human attention is an exciting challenge in digital image
processing. Here, we present a new model for manipulating images to change the
distribution of human fixations in a controlled fashion. We use the
state-of-the-art model for fixation prediction to train a convolutional neural
network to transform images so that they satisfy a given fixation distribution.
For network training, we carefully design a loss function to achieve a
perceptual effect while preserving naturalness of the transformed images.
Finally, we evaluate the success of our model by measuring human fixations for
a set of manipulated images. On our test images we can in-/decrease the
probability to fixate on selected objects on average by 43/22% but show that
the effectiveness of the model depends on the semantic content of the
manipulated images.
Multi-modal Face Pose Estimation with Multi-task Manifold Deep Learning
Authors: Chaoqun Hong, Jun Yu
Date: 2017-12-18 00:00:00
Tasks: Deep Learning, Descriptive, Multi-Task Learning, Pose Estimation, Saliency Detection

Human face pose estimation aims at estimating the gazing direction or head
postures with 2D images. It gives some very important information such as
communicative gestures, saliency detection and so on, which attracts plenty of
attention recently. However, it is challenging because of complex background,
various orientations and face appearance visibility. Therefore, a descriptive
representation of face images and mapping it to poses are critical. In this
paper, we make use of multi-modal data and propose a novel face pose estimation
method that uses a novel deep learning framework named Multi-task Manifold Deep
Learning $M^2DL$. It is based on feature extraction with improved deep neural
networks and multi-modal mapping relationship with multi-task learning. In the
proposed deep learning based framework, Manifold Regularized Convolutional
Layers (MRCL) improve traditional convolutional layers by learning the
relationship among outputs of neurons. Besides, in the proposed mapping
relationship learning method, different modals of face representations are
naturally combined to learn the mapping function from face images to poses. In
this way, the computed mapping model with multiple tasks is improved.
Experimental results on three challenging benchmark datasets DPOSE, HPID and
BKHPD demonstrate the outstanding performance of $M^2DL$.
Super-Resolution with Deep Adaptive Image Resampling
Authors: Xu Jia, Hong Chang, Tinne Tuytelaars
Date: 2017-12-18 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep learning based methods have recently pushed the state-of-the-art on the
problem of Single Image Super-Resolution (SISR). In this work, we revisit the
more traditional interpolation-based methods, that were popular before, now
with the help of deep learning. In particular, we propose to use a
Convolutional Neural Network (CNN) to estimate spatially variant interpolation
kernels and apply the estimated kernels adaptively to each position in the
image. The whole model is trained in an end-to-end manner. We explore two ways
to improve the results for the case of large upscaling factors, and propose a
recursive extension of our basic model. This achieves results that are on par
with state-of-the-art methods. We visualize the estimated adaptive
interpolation kernels to gain more insight on the effectiveness of the proposed
method. We also extend the method to the task of joint image filtering and
again achieve state-of-the-art performance.
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalising neural network
Authors: Ester Bonmati, Yipeng Hu, Nikhil Sindhwani, Hans Peter Dietz, Jan D'hooge, Dean Barratt, Jan Deprest, Tom Vercauteren
Date: 2017-12-18 00:00:00

Segmentation of the levator hiatus in ultrasound allows to extract biometrics
which are of importance for pelvic floor disorder assessment. In this work, we
present a fully automatic method using a convolutional neural network (CNN) to
outline the levator hiatus in a 2D image extracted from a 3D ultrasound volume.
In particular, our method uses a recently developed scaled exponential linear
unit (SELU) as a nonlinear self-normalising activation function, which for the
first time has been applied in medical imaging with CNN. SELU has important
advantages such as being parameter-free and mini-batch independent, which may
help to overcome memory constraints during training. A dataset with 91 images
from 35 patients during Valsalva, contraction and rest, all labelled by three
operators, is used for training and evaluation in a leave-one-patient-out
cross-validation. Results show a median Dice similarity coefficient of 0.90
with an interquartile range of 0.08, with equivalent performance to the three
operators (with a Williams' index of 1.03), and outperforming a U-Net
architecture without the need for batch normalisation. We conclude that the
proposed fully automatic method achieved equivalent accuracy in segmenting the
pelvic floor levator hiatus compared to a previous semi-automatic approach.
A Shapelet Transform for Multivariate Time Series Classification
Authors: Aaron Bostrom, Anthony Bagnall
Date: 2017-12-18 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Shapelets are phase independent subsequences designed for time series
classification. We propose three adaptations to the Shapelet Transform (ST) to
capture multivariate features in multivariate time series classification. We
create a unified set of data to benchmark our work on, and compare with three
other algorithms. We demonstrate that multivariate shapelets are not
significantly worse than other state-of-the-art algorithms.
Coresets for Kinematic Data: From Theorems to Real-Time Systems
Authors: Soliman Nasser, Ibrahim Jubran, Dan Feldman
Date: 2015-11-30 00:00:00
Tasks: Pose Estimation, Semantic Compression

A coreset (or core-set) of a dataset is its semantic compression with respect
to a set of queries, such that querying the (small) coreset provably yields an
approximate answer to querying the original (full) dataset. In the last decade,
coresets provided breakthroughs in theoretical computer science for
approximation algorithms, and more recently, in the machine learning community
for learning "Big data". However, we are not aware of real-time systems that
compute coresets in a rate of dozens of frames per second. In this paper we
suggest a framework to turn theorems to such systems using coresets. We begin
with a proof of independent interest, that any set of $n$ matrices in
$\mathbb{R}^{d\times d}$ whose sum is $S$, has a positively weighted subset
whose sum has the same center of mass (mean) and orientation (left+right
singular vectors) as $S$, and consists of $O(dr)$ matrices (independent of
$n$), where $r\leq d$ is the rank of $S$. We provide an algorithm that computes
this (core) set in one pass over possibly infinite stream of matrices in
$d^{O(1)}$ time per matrix insertion. By maintaining such a coreset for
kinematic (moving) set of $n$ points, we can run pose-estimation algorithms,
such as Kabsch or PnP, on the small coresets, instead of the $n$ points, in
real-time using weak devices, while obtaining the same results. This enabled us
to implement a low-cost ($<\$100$) IoT wireless system that tracks a toy (and
harmless) quadcopter which guides guests to a desired room (in a hospital,
mall, hotel, museum, etc.) with no help of additional human or remote
controller. We hope that our framework will encourage researchers outside the
theoretical community to design and use coresets in future systems and papers.
To this end, we provide extensive experimental results on both synthetic and
real data, as well as a link to the open code of our system and algorithms.
On the Effectiveness of Least Squares Generative Adversarial Networks
Authors: Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, Zhen Wang, Stephen Paul Smolley
Date: 2017-12-18 00:00:00

Unsupervised learning with generative adversarial networks (GANs) has proven
to be hugely successful. Regular GANs hypothesize the discriminator as a
classifier with the sigmoid cross entropy loss function. However, we found that
this loss function may lead to the vanishing gradients problem during the
learning process. To overcome such a problem, we propose in this paper the
Least Squares Generative Adversarial Networks (LSGANs) which adopt the least
squares loss for both the discriminator and the generator. We show that
minimizing the objective function of LSGAN yields minimizing the Pearson
$\chi^2$ divergence. We also show that the derived objective function that
yields minimizing the Pearson $\chi^2$ divergence performs better than the
classical one of using least squares for classification. There are two benefits
of LSGANs over regular GANs. First, LSGANs are able to generate higher quality
images than regular GANs. Second, LSGANs perform more stably during the
learning process. For evaluating the image quality, we conduct both qualitative
and quantitative experiments, and the experimental results show that LSGANs can
generate higher quality images than regular GANs. Furthermore, we evaluate the
stability of LSGANs in two groups. One is to compare between LSGANs and regular
GANs without gradient penalty. We conduct three experiments, including Gaussian
mixture distribution, difficult architectures, and a newly proposed method ---
datasets with small variability, to illustrate the stability of LSGANs. The
other one is to compare between LSGANs with gradient penalty (LSGANs-GP) and
WGANs with gradient penalty (WGANs-GP). The experimental results show that
LSGANs-GP succeed in training for all the difficult architectures used in
WGANs-GP, including 101-layer ResNet.
The empirical Christoffel function with applications in data analysis
Authors: Jean-Bernard Lasserre, Edouard Pauwels
Date: 2017-01-11 00:00:00
Tasks: BIG-bench Machine Learning, Novelty Detection

We illustrate the potential applications in machine learning of the
Christoffel function, or more precisely, its empirical counterpart associated
with a counting measure uniformly supported on a finite set of points. Firstly,
we provide a thresholding scheme which allows to approximate the support of a
measure from a finite subset of its moments with strong asymptotic guaranties.
Secondly, we provide a consistency result which relates the empirical
Christoffel function and its population counterpart in the limit of large
samples. Finally, we illustrate the relevance of our results on simulated and
real world datasets for several applications in statistics and machine
learning: (a) density and support estimation from finite samples, (b) outlier
and novelty detection and (c) affine matching.
Taming Adversarial Domain Transfer with Structural Constraints for Image Enhancement
Authors: Elias Vansteenkiste, Patrick Kern
Date: 2017-12-02 00:00:00
Tasks: Image Enhancement

The goal of this work is to improve images of traffic scenes that are
degraded by natural causes such as fog, rain and limited visibility during the
night. For these applications, it is next to impossible to get pixel perfect
pairs of the same scene, with and without the degrading conditions. This makes
it unsuitable for conventional supervised learning approaches, however, it is
easy to collect unpaired images of the scenes in a perfect and in a degraded
condition. To enhance the images taken in a poor visibility condition, domain
transfer models can be trained to transform an image from the degraded to the
clear domain. A well-known concept for unsupervised domain transfer are
cycle-consistent generative adversarial models. Unfortunately, the resulting
generators often change the structure of the scene. This causes an undesirable
change in the semantics. We propose three ways to cope with this problem
depending on the type of degradation.
When is Network Lasso Accurate?
Authors: Alexander Jung, Nguyen Tran Quang, Alexandru Mara
Date: 2017-04-07 00:00:00
Tasks: compressed sensing

The "least absolute shrinkage and selection operator" (Lasso) method has been
adapted recently for networkstructured datasets. In particular, this network
Lasso method allows to learn graph signals from a small number of noisy signal
samples by using the total variation of a graph signal for regularization.
While efficient and scalable implementations of the network Lasso are
available, only little is known about the conditions on the underlying network
structure which ensure network Lasso to be accurate. By leveraging concepts of
compressed sensing, we address this gap and derive precise conditions on the
underlying network topology and sampling set which guarantee the network Lasso
for a particular loss function to deliver an accurate estimate of the entire
underlying graph signal. We also quantify the error incurred by network Lasso
in terms of two constants which reflect the connectivity of the sampled nodes.
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things
Authors: Dohyung Kim, Hyochang Yang, Minki Chung, Sungzoon Cho
Date: 2017-12-18 00:00:00
Tasks: Anomaly Detection, Edge-computing, Time Series, Time Series Analysis, Unsupervised Anomaly Detection

In this paper, we propose Squeezed Convolutional Variational AutoEncoder
(SCVAE) for anomaly detection in time series data for Edge Computing in
Industrial Internet of Things (IIoT). The proposed model is applied to labeled
time series data from UCI datasets for exact performance evaluation, and
applied to real world data for indirect model performance comparison. In
addition, by comparing the models before and after applying Fire Modules from
SqueezeNet, we show that model size and inference times are reduced while
similar levels of performance is maintained.
Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
Authors: Santiago Pascual, Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn
Date: 2017-12-18 00:00:00
Tasks: Generative Adversarial Network, Speech Enhancement

Speech enhancement deep learning systems usually require large amounts of
training data to operate in broad conditions or real applications. This makes
the adaptability of those systems into new, low resource environments an
important topic. In this work, we present the results of adapting a speech
enhancement generative adversarial network by finetuning the generator with
small amounts of data. We investigate the minimum requirements to obtain a
stable behavior in terms of several objective metrics in two very different
languages: Catalan and Korean. We also study the variability of test
performance to unseen noise as a function of the amount of different types of
noise available for training. Results show that adapting a pre-trained English
model with 10 min of data already achieves a comparable performance to having
two orders of magnitude more data. They also demonstrate the relative stability
in test performance with respect to the number of training noise types.
Minimax Error of Interpolation and Optimal Design of Experiments for Variable Fidelity Data
Authors: Alexey Zaytsev, Evgeny Burnaev
Date: 2016-10-21 00:00:00
Tasks: regression

Engineering problems often involve data sources of variable fidelity with
different costs of obtaining an observation. In particular, one can use both a
cheap low fidelity function (e.g. a computational experiment with a CFD code)
and an expensive high fidelity function (e.g. a wind tunnel experiment) to
generate a data sample in order to construct a regression model of a high
fidelity function. The key question in this setting is how the sizes of the
high and low fidelity data samples should be selected in order to stay within a
given computational budget and maximize accuracy of the regression model prior
to committing resources on data acquisition.
  In this paper we obtain minimax interpolation errors for single and variable
fidelity scenarios for a multivariate Gaussian process regression. Evaluation
of the minimax errors allows us to identify cases when the variable fidelity
data provides better interpolation accuracy than the exclusively high fidelity
data for the same computational budget.
  These results allow us to calculate the optimal shares of variable fidelity
data samples under the given computational budget constraint. Real and
synthetic data experiments suggest that using the obtained optimal shares often
outperforms natural heuristics in terms of the regression accuracy.
A polynomial time algorithm for the Lambek calculus with brackets of bounded order
Authors: Max Kanovich, Stepan Kuznetsov, Glyn Morrill, Andre Scedrov
Date: 2017-05-01 00:00:00

Lambek calculus is a logical foundation of categorial grammar, a linguistic
paradigm of grammar as logic and parsing as deduction. Pentus (2010) gave a
polynomial-time algorithm for determ- ining provability of bounded depth
formulas in the Lambek calculus with empty antecedents allowed. Pentus'
algorithm is based on tabularisation of proof nets. Lambek calculus with
brackets is a conservative extension of Lambek calculus with bracket
modalities, suitable for the modeling of syntactical domains. In this paper we
give an algorithm for provability the Lambek calculus with brackets allowing
empty antecedents. Our algorithm runs in polynomial time when both the formula
depth and the bracket nesting depth are bounded. It combines a Pentus-style
tabularisation of proof nets with an automata-theoretic treatment of
bracketing.