pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Unbalanced Optimal Transport using Generative Adversarial Networks
Authors: Karren D. Yang, Caroline Uhler
Date: 2018-10-26 00:00:00
Proceeding: ICLR 2019 5

Generative adversarial networks (GANs) are an expressive class of neural generative models with tremendous success in modeling high-dimensional continuous measures. In this paper, we present a scalable method for unbalanced optimal transport (OT) based on the generative-adversarial framework. We formulate unbalanced OT as a problem of simultaneously learning a transport map and a scaling factor that push a source measure to a target measure in a cost-optimal manner. In addition, we propose an algorithm for solving this problem based on stochastic alternating gradient updates, similar in practice to GANs. We also provide theoretical justification for this formulation, showing that it is closely related to an existing static formulation by Liero et al. (2018), and perform numerical experiments demonstrating how this methodology can be applied to population modeling.
American Sign Language fingerspelling recognition in the wild
Authors: Bowen Shi, Aurora Martinez Del Rio, Jonathan Keane, Jonathan Michaux, Diane Brentari, Greg Shakhnarovich, Karen Livescu
Date: 2018-10-26 00:00:00
Tasks: Sign Language Recognition

We address the problem of American Sign Language fingerspelling recognition
in the wild, using videos collected from websites. We introduce the largest
data set available so far for the problem of fingerspelling recognition, and
the first using naturally occurring video data. Using this data set, we present
the first attempt to recognize fingerspelling sequences in this challenging
setting. Unlike prior work, our video data is extremely challenging due to low
frame rates and visual variability. To tackle the visual challenges, we train a
special-purpose signing hand detector using a small subset of our data. Given
the hand detector output, a sequence model decodes the hypothesized
fingerspelled letter sequence. For the sequence model, we explore
attention-based recurrent encoder-decoders and CTC-based approaches. As the
first attempt at fingerspelling recognition in the wild, this work is intended
to serve as a baseline for future work on sign language recognition in
realistic conditions. We find that, as expected, letter error rates are much
higher than in previous work on more controlled data, and we analyze the
sources of error and effects of model variants.
Resampled Priors for Variational Autoencoders
Authors: Matthias Bauer, andriy mnih
Date: 2018-10-26 00:00:00

We propose Learned Accept/Reject Sampling (LARS), a method for constructing
richer priors using rejection sampling with a learned acceptance function. This
work is motivated by recent analyses of the VAE objective, which pointed out
that commonly used simple priors can lead to underfitting. As the distribution
induced by LARS involves an intractable normalizing constant, we show how to
estimate it and its gradients efficiently. We demonstrate that LARS priors
improve VAE performance on several standard datasets both when they are learned
jointly with the rest of the model and when they are fitted to a pretrained
model. Finally, we show that LARS can be combined with existing methods for
defining flexible priors for an additional boost in performance.
Anytime Stereo Image Depth Estimation on Mobile Devices
Authors: Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger
Date: 2018-10-26 00:00:00
Tasks: Depth Estimation, Stereo Depth Estimation

Many applications of stereo depth estimation in robotics require the
generation of accurate disparity maps in real time under significant
computational constraints. Current state-of-the-art algorithms force a choice
between either generating accurate mappings at a slow pace, or quickly
generating inaccurate ones, and additionally these methods typically require
far too many parameters to be usable on power- or memory-constrained devices.
Motivated by these shortcomings, we propose a novel approach for disparity
prediction in the anytime setting. In contrast to prior work, our end-to-end
learned approach can trade off computation and accuracy at inference time.
Depth estimation is performed in stages, during which the model can be queried
at any time to output its current best estimate. Our final model can process
1242$ \times $375 resolution images within a range of 10-35 FPS on an NVIDIA
Jetson TX2 module with only marginal increases in error -- using two orders of
magnitude fewer parameters than the most competitive baseline. The source code
is available at https://github.com/mileyan/AnyNet .
Dendritic cortical microcircuits approximate the backpropagation algorithm
Authors: João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
Date: 2018-10-26 00:00:00
Proceeding: NeurIPS 2018 12

Deep learning has seen remarkable developments over the last years, many of
them inspired by neuroscience. However, the main learning mechanism behind
these advances - error backpropagation - appears to be at odds with
neurobiology. Here, we introduce a multilayer neuronal network model with
simplified dendritic compartments in which error-driven synaptic plasticity
adapts the network towards a global desired output. In contrast to previous
work our model does not require separate phases and synaptic learning is driven
by local dendritic prediction errors continuously in time. Such errors
originate at apical dendrites and occur due to a mismatch between predictive
input from lateral interneurons and activity from actual top-down feedback.
Through the use of simple dendritic compartments and different cell-types our
model can represent both error and normal activity within a pyramidal neuron.
We demonstrate the learning capabilities of the model in regression and
classification tasks, and show analytically that it approximates the error
backpropagation algorithm. Moreover, our framework is consistent with recent
observations of learning between brain areas and the architecture of cortical
microcircuits. Overall, we introduce a novel view of learning on dendritic
cortical circuits and on how the brain may solve the long-standing synaptic
credit assignment problem.
Finding dissimilar explanations in Bayesian networks: Complexity results
Authors: Johan Kwisthout
Date: 2018-10-26 00:00:00

Finding the most probable explanation for observed variables in a Bayesian
network is a notoriously intractable problem, particularly if there are hidden
variables in the network. In this paper we examine the complexity of a related
problem, that is, the problem of finding a set of sufficiently dissimilar, yet
all plausible, explanations. Applications of this problem are, e.g., in search
query results (you won't want 10 results that all link to the same website) or
in decision support systems. We show that the problem of finding a 'good
enough' explanation that differs in structure from the best explanation is at
least as hard as finding the best explanation itself.
Deep Intrinsically Motivated Continuous Actor-Critic for Efficient Robotic Visuomotor Skill Learning
Authors: Muhammad Burhan Hafez, Cornelius Weber, Matthias Kerzel, Stefan Wermter
Date: 2018-10-26 00:00:00
Tasks: continuous-control, Continuous Control

In this paper, we present a new intrinsically motivated actor-critic
algorithm for learning continuous motor skills directly from raw visual input.
Our neural architecture is composed of a critic and an actor network. Both
networks receive the hidden representation of a deep convolutional autoencoder
which is trained to reconstruct the visual input, while the centre-most hidden
representation is also optimized to estimate the state value. Separately, an
ensemble of predictive world models generates, based on its learning progress,
an intrinsic reward signal which is combined with the extrinsic reward to guide
the exploration of the actor-critic learner. Our approach is more
data-efficient and inherently more stable than the existing actor-critic
methods for continuous control from pixel data. We evaluate our algorithm for
the task of learning robotic reaching and grasping skills on a realistic
physics simulator and on a humanoid robot. The results show that the control
policies learned with our approach can achieve better performance than the
compared state-of-the-art and baseline algorithms in both dense-reward and
challenging sparse-reward settings.
Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization
Authors: Jaime Roquero Gimenez, James Zou
Date: 2018-10-26 00:00:00
Tasks: feature selection

The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees. The advantage of knockoff is that if we have a good model of the features X, then we can identify salient features without knowing anything about how the outcome Y depends on X. An important drawback of knockoffs is its instability: running the procedure twice can result in very different selected features, potentially leading to different conclusions. Addressing this instability is critical for obtaining reproducible and robust results. Here we present a generalization of the knockoff procedure that we call simultaneous multi-knockoffs. We show that multi-knockoff guarantees false discovery rate (FDR) control, and is substantially more stable and powerful compared to the standard (single) knockoff. Moreover we propose a new algorithm based on entropy maximization for generating Gaussian multi-knockoffs. We validate the improved stability and power of multi-knockoffs in systematic experiments. We also illustrate how multi-knockoffs can improve the accuracy of detecting genetic mutations that are causally linked to phenotypes.
Static and Dynamic Vector Semantics for Lambda Calculus Models of Natural Language
Authors: Mehrnoosh Sadrzadeh, Reinhard Muskens
Date: 2018-10-26 00:00:00
Tasks: Sentence

Vector models of language are based on the contextual aspects of language,
the distributions of words and how they co-occur in text. Truth conditional
models focus on the logical aspects of language, compositional properties of
words and how they compose to form sentences. In the truth conditional
approach, the denotation of a sentence determines its truth conditions, which
can be taken to be a truth value, a set of possible worlds, a context change
potential, or similar. In the vector models, the degree of co-occurrence of
words in context determines how similar the meanings of words are. In this
paper, we put these two models together and develop a vector semantics for
language based on the simply typed lambda calculus models of natural language.
We provide two types of vector semantics: a static one that uses techniques
familiar from the truth conditional tradition and a dynamic one based on a form
of dynamic interpretation inspired by Heim's context change potentials. We show
how the dynamic model can be applied to entailment between a corpus and a
sentence and we provide examples.
Security Event Recognition for Visual Surveillance
Authors: Michael Ying Yang, Wentong Liao, Chun Yang, Yanpeng Cao, Bodo Rosenhahn
Date: 2018-10-26 00:00:00

With rapidly increasing deployment of surveillance cameras, the reliable
methods for automatically analyzing the surveillance video and recognizing
special events are demanded by different practical applications. This paper
proposes a novel effective framework for security event analysis in
surveillance videos. First, convolutional neural network (CNN) framework is
used to detect objects of interest in the given videos. Second, the owners of
the objects are recognized and monitored in real-time as well. If anyone moves
any object, this person will be verified whether he/she is its owner. If not,
this event will be further analyzed and distinguished between two different
scenes: moving the object away or stealing it. To validate the proposed
approach, a new video dataset consisting of various scenarios is constructed
for more complex tasks. For comparison purpose, the experiments are also
carried out on the benchmark databases related to the task on abandoned luggage
detection. The experimental results show that the proposed approach outperforms
the state-of-the-art methods and effective in recognizing complex security
events.
Generating equilibrium molecules with deep neural networks
Authors: Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Date: 2018-10-26 00:00:00

Discovery of atomistic systems with desirable properties is a major challenge
in chemistry and material science. Here we introduce a novel, autoregressive,
convolutional deep neural network architecture that generates molecular
equilibrium structures by sequentially placing atoms in three-dimensional
space. The model estimates the joint probability over molecular configurations
with tractable conditional probabilities which only depend on distances between
atoms and their nuclear charges. It combines concepts from state-of-the-art
atomistic neural networks with auto-regressive generative models for images and
speech. We demonstrate that the architecture is capable of generating molecules
close to equilibrium for constitutional isomers of C$_7$O$_2$H$_{10}$.
Benefits of over-parameterization with EM
Authors: Ji Xu, Daniel Hsu, Arian Maleki
Date: 2018-10-26 00:00:00
Proceeding: NeurIPS 2018 12

Expectation Maximization (EM) is among the most popular algorithms for
maximum likelihood estimation, but it is generally only guaranteed to find its
stationary points of the log-likelihood objective. The goal of this article is
to present theoretical and empirical evidence that over-parameterization can
help EM avoid spurious local optima in the log-likelihood. We consider the
problem of estimating the mean vectors of a Gaussian mixture model in a
scenario where the mixing weights are known. Our study shows that the global
behavior of EM, when one uses an over-parameterized model in which the mixing
weights are treated as unknown, is better than that when one uses the (correct)
model with the mixing weights fixed to the known values. For symmetric
Gaussians mixtures with two components, we prove that introducing the
(statistically redundant) weight parameters enables EM to find the global
maximizer of the log-likelihood starting from almost any initial mean
parameters, whereas EM without this over-parameterization may very often fail.
For other Gaussian mixtures, we provide empirical evidence that shows similar
behavior. Our results corroborate the value of over-parameterization in solving
non-convex optimization problems, previously observed in other domains.
Outlier Detection using Generative Models with Theoretical Performance Guarantees
Authors: Jirong Yi, Anh Duc Le, Tianming Wang, Xiaodong Wu, Weiyu Xu
Date: 2018-10-26 00:00:00
Tasks: Outlier Detection

This paper considers the problem of recovering signals from compressed
measurements contaminated with sparse outliers, which has arisen in many
applications. In this paper, we propose a generative model neural network
approach for reconstructing the ground truth signals under sparse outliers. We
propose an iterative alternating direction method of multipliers (ADMM)
algorithm for solving the outlier detection problem via $\ell_1$ norm
minimization, and a gradient descent algorithm for solving the outlier
detection problem via squared $\ell_1$ norm minimization. We establish the
recovery guarantees for reconstruction of signals using generative models in
the presence of outliers, and give an upper bound on the number of outliers
allowed for recovery. Our results are applicable to both the linear generator
neural network and the nonlinear generator neural network with an arbitrary
number of layers. We conduct extensive experiments using variational
auto-encoder and deep convolutional generative adversarial networks, and the
experimental results show that the signals can be successfully reconstructed
under outliers using our approach. Our approach outperforms the traditional
Lasso and $\ell_2$ minimization approach.
Comparing Multilayer Perceptron and Multiple Regression Models for Predicting Energy Use in the Balkans
Authors: Radmila Janković, Alessia Amelio
Date: 2018-10-26 00:00:00
Tasks: regression

Global demographic and economic changes have a critical impact on the total
energy consumption, which is why demographic and economic parameters have to be
taken into account when making predictions about the energy consumption. This
research is based on the application of a multiple linear regression model and
a neural network model, in particular multilayer perceptron, for predicting the
energy consumption. Data from five Balkan countries has been considered in the
analysis for the period 1995-2014. Gross domestic product, total number of
population, and CO2 emission were taken as predictor variables, while the
energy consumption was used as the dependent variable. The analyses showed that
CO2 emissions have the highest impact on the energy consumption, followed by
the gross domestic product, while the population number has the lowest impact.
The results from both analyses are then used for making predictions on the same
data, after which the obtained values were compared with the real values. It
was observed that the multilayer perceptron model predicts better the energy
consumption than the regression model.
Real-time Context-aware Learning System for IoT Applications
Authors: Bhaskar Das, Jalal Almhana
Date: 2018-10-26 00:00:00
Tasks: BIG-bench Machine Learning

We propose a real-time context-aware learning system along with the
architecture that runs on the mobile devices, provide services to the user and
manage the IoT devices. In this system, an application running on mobile
devices collected data from the sensors, learned about the user-defined
context, made predictions in real-time and manage IoT devices accordingly.
However, the computational power of the mobile devices makes it challenging to
run machine learning algorithms with acceptable accuracy. To solve this issue,
some authors have run machine learning algorithms on the server and transmitted
the results to the mobile devices. Although the context-aware predictions made
by the server are more accurate than their mobile counterpart, it heavily
depends on the network connection for the delivery of the results to the
devices, which negatively affects real-time context-learning. Therefore, in
this work, we describe a context-learning algorithm for mobile devices which is
less demanding on the computational resources and maintains the accuracy of the
prediction by updating itself from the learning parameters obtained from the
server periodically. Experimental results show that the proposed light-weight
context-learning algorithm can achieve mean accuracy up to 97.51% while mean
execution time requires only 11ms.
Interruptible Algorithms for Multiproblem Solving
Authors: Spyros Angelopoulos, Alejandro Lopez-Ortiz
Date: 2018-10-26 00:00:00
Tasks: Scheduling

In this paper we address the problem of designing an interruptible system in
a setting in which $n$ problem instances, all equally important, must be solved
concurrently. The system involves scheduling executions of contract algorithms
(which offer a trade-off between allowable computation time and quality of the
solution) in m identical parallel processors. When an interruption occurs, the
system must report a solution to each of the $n$ problem instances. The quality
of this output is then compared to the best-possible algorithm that has
foreknowledge of the interruption time and must, likewise, produce solutions to
all $n$ problem instances. This extends the well-studied setting in which only
one problem instance is queried at interruption time.
  In this work we first introduce new measures for evaluating the performance
of interruptible systems in this setting. In particular, we propose the
deficiency of a schedule as a performance measure that meets the requirements
of the problem at hand. We then present a schedule whose performance we prove
that is within a small factor from optimal in the general, multiprocessor
setting. We also show several lower bounds on the deficiency of schedules on a
single processor. More precisely, we prove a general lower bound of (n+1)/n, an
improved lower bound for the two-problem setting (n=2), and a tight lower bound
for the class of round-robin schedules. Our techniques can also yield a
simpler, alternative proof of the main result of [Bernstein et al, IJCAI 2003]
concerning the performance of cyclic schedules in multiprocessor environments.
Texture variation adaptive image denoising with nonlocal PCA
Authors: Wenzhao Zhao, Qiegen Liu, Yisong Lv, Binjie Qin
Date: 2018-10-26 00:00:00
Tasks: Clustering, Denoising, Image Denoising

Image textures, as a kind of local variations, provide important information
for human visual system. Many image textures, especially the small-scale or
stochastic textures are rich in high-frequency variations, and are difficult to
be preserved. Current state-of-the-art denoising algorithms typically adopt a
nonlocal approach consisting of image patch grouping and group-wise denoising
filtering. To achieve a better image denoising while preserving the variations
in texture, we first adaptively group high correlated image patches with the
same kinds of texture elements (texels) via an adaptive clustering method. This
adaptive clustering method is applied in an
over-clustering-and-iterative-merging approach, where its noise robustness is
improved with a custom merging threshold relating to the noise level and
cluster size. For texture-preserving denoising of each cluster, considering
that the variations in texture are captured and wrapped in not only the
between-dimension energy variations but also the within-dimension variations of
PCA transform coefficients, we further propose a PCA-transform-domain variation
adaptive filtering method to preserve the local variations in textures.
Experiments on natural images show the superiority of the proposed
transform-domain variation adaptive filtering to traditional PCA-based hard or
soft threshold filtering. As a whole, the proposed denoising method achieves a
favorable texture preserving performance both quantitatively and visually,
especially for stochastic textures, which is further verified in camera raw
image denoising.
Multi-Stage Reinforcement Learning For Object Detection
Authors: Jonas Koenig, Simon Malberg, Martin Martens, Sebastian Niehaus, Artus Krohn-Grimberghe, Arunselvan Ramaswamy
Date: 2018-10-15 00:00:00
Tasks: Object, object-detection, Object Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a reinforcement learning approach for detecting objects within an
image. Our approach performs a step-wise deformation of a bounding box with the
goal of tightly framing the object. It uses a hierarchical tree-like
representation of predefined region candidates, which the agent can zoom in on.
This reduces the number of region candidates that must be evaluated so that the
agent can afford to compute new feature maps before each step to enhance
detection quality. We compare an approach that is based purely on zoom actions
with one that is extended by a second refinement stage to fine-tune the
bounding box after each zoom step. We also improve the fitting ability by
allowing for different aspect ratios of the bounding box. Finally, we propose
different reward functions to lead to a better guidance of the agent while
following its search trajectories. Experiments indicate that each of these
extensions leads to more correct detections. The best performing approach
comprises a zoom stage and a refinement stage, uses aspect-ratio modifying
actions and is trained using a combination of three different reward metrics.