pwc-archive/papers-with-abstracts huggingface dataset:
On the role of ML estimation and Bregman divergences in sparse representation of covariance and precision matrices
Authors: Branko Brkljač, Željen Trpovski
Date: 2018-10-27 00:00:00

Sparse representation of structured signals requires modelling strategies
that maintain specific signal properties, in addition to preserving original
information content and achieving simpler signal representation. Therefore, the
major design challenge is to introduce adequate problem formulations and offer
solutions that will efficiently lead to desired representations. In this
context, sparse representation of covariance and precision matrices, which
appear as feature descriptors or mixture model parameters, respectively, will
be in the main focus of this paper.
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
Authors: Andrew Hundt, Varun Jain, Chia-Hung Lin, Chris Paxton, Gregory D. Hager
Date: 2018-10-27 00:00:00
Tasks: 6D Pose Estimation using RGBD, Industrial Robots, Neural Architecture Search, Object, Offline RL, Robotic Grasping, Robot Task Planning, Time Series Analysis

A robot can now grasp an object more effectively than ever before, but once
it has the object what happens next? We show that a mild relaxation of the task
and workspace constraints implicit in existing object grasping datasets can
cause neural network based grasping algorithms to fail on even a simple block
stacking task when executed under more realistic circumstances.
  To address this, we introduce the JHU CoSTAR Block Stacking Dataset (BSD),
where a robot interacts with 5.1 cm colored blocks to complete an
order-fulfillment style block stacking task. It contains dynamic scenes and
real time-series data in a less constrained environment than comparable
datasets. There are nearly 12,000 stacking attempts and over 2 million frames
of real data. We discuss the ways in which this dataset provides a valuable
resource for a broad range of other topics of investigation.
  We find that hand-designed neural networks that work on prior datasets do not
generalize to this task. Thus, to establish a baseline for this dataset, we
demonstrate an automated search of neural network based models using a novel
multiple-input HyperTree MetaModel, and find a final model which makes
reasonable 3D pose predictions for grasping and stacking on our dataset.
  The CoSTAR BSD, code, and instructions are available at
https://sites.google.com/site/costardataset.
Regularization Effect of Fast Gradient Sign Method and its Generalization
Authors: Chandler Zuo
Date: 2018-10-27 00:00:00

Fast Gradient Sign Method (FGSM) is a popular method to generate adversarial
examples that make neural network models robust against perturbations. Despite
its empirical success, its theoretical property is not well understood. This
paper develops theory to explain the regularization effect of Generalized FGSM,
a class of methods to generate adversarial examples. Motivated from the
relationship between FGSM and LASSO penalty, the asymptotic properties of
Generalized FGSM are derived in the Generalized Linear Model setting, which is
essentially the 1-layer neural network setting with certain activation
functions. In such simple neural network models, I prove that Generalized FGSM
estimation is root n-consistent and weakly oracle under proper conditions. The
asymptotic results are also highly similar to penalized likelihood estimation.
Nevertheless, Generalized FGSM introduces additional bias when data sampling is
not sign neutral, a concept I introduce to describe the balance-ness of the
noise signs. Although the theory in this paper is developed under simple neural
network settings, I argue that it may give insights and justification for FGSM
in deep neural network settings as well.
Flash Photography for Data-Driven Hidden Scene Recovery
Authors: Matthew Tancik, Guy Satat, Ramesh Raskar
Date: 2018-10-27 00:00:00
Tasks: Object, Object Localization

Vehicles, search and rescue personnel, and endoscopes use flash lights to
locate, identify, and view objects in their surroundings. Here we show the
first steps of how all these tasks can be done around corners with consumer
cameras. Recent techniques for NLOS imaging using consumer cameras have not
been able to both localize and identify the hidden object. We introduce a
method that couples traditional geometric understanding and data-driven
techniques. To avoid the limitation of large dataset gathering, we train the
data-driven models on rendered samples to computationally recover the hidden
scene on real data. The method has three independent operating modes: 1) a
regression output to localize a hidden object in 2D, 2) an identification
output to identify the object type or pose, and 3) a generative network to
reconstruct the hidden scene from a new viewpoint. The method is able to
localize 12cm wide hidden objects in 2D with 1.7cm accuracy. The method also
identifies the hidden object class with 87.7% accuracy (compared to 33.3%
random accuracy). This paper also provides an analysis on the distribution of
information that encodes the occluded object in the accessible scene. We show
that, unlike previously thought, the area that extends beyond the corner is
essential for accurate object localization and identification.
Multi-Agent Common Knowledge Reinforcement Learning
Authors: Christian A. Schroeder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show that common knowledge between agents allows for complex decentralised coordination. Common knowledge arises naturally in a large number of decentralised cooperative multi-agent tasks, for example, when agents can reconstruct parts of each others' observations. Since agents an independently agree on their common knowledge, they can execute complex coordinated policies that condition on this knowledge in a fully decentralised fashion. We propose multi-agent common knowledge reinforcement learning (MACKRL), a novel stochastic actor-critic algorithm that learns a hierarchical policy tree. Higher levels in the hierarchy coordinate groups of agents by conditioning on their common knowledge, or delegate to lower levels with smaller subgroups but potentially richer common knowledge. The entire policy tree can be executed in a fully decentralised fashion. As the lowest policy tree level consists of independent policies for each agent, MACKRL reduces to independently learnt decentralised policies as a special case. We demonstrate that our method can exploit common knowledge for superior performance on complex decentralised coordination tasks, including a stochastic matrix game and challenging problems in StarCraft II unit micromanagement.
Hull Form Optimization with Principal Component Analysis and Deep Neural Network
Authors: Dongchi Yu, Lu Wang
Date: 2018-10-27 00:00:00
Tasks: Form

Designing and modifying complex hull forms for optimal vessel performances
have been a major challenge for naval architects. In the present study,
Principal Component Analysis (PCA) is introduced to compress the geometric
representation of a group of existing vessels, and the resulting principal
scores are manipulated to generate a large number of derived hull forms, which
are evaluated computationally for their calm-water performances. The results
are subsequently used to train a Deep Neural Network (DNN) to accurately
establish the relation between different hull forms and their associated
performances. Then, based on the fast, parallel DNN-based hull-form evaluation,
the large-scale search for optimal hull forms is performed.
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Authors: Myriam Tami, Marianne Clausel, Emilie Devijver, Adrien Dulac, Eric Gaussier, Stefan Janaqi, Meriam Chebre
Date: 2018-10-27 00:00:00
Tasks: regression

Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees,
have been successfully used for regression in many applications and research
studies. Furthermore, these methods have been extended in order to deal with
uncertainty in the output variable, using for example a quantile loss in Random
Forests (Meinshausen, 2006). To the best of our knowledge, no extension has
been provided yet for dealing with uncertainties in the input variables, even
though such uncertainties are common in practical situations. We propose here
such an extension by showing how standard regression trees optimizing a
quadratic loss can be adapted and learned while taking into account the
uncertainties in the inputs. By doing so, one no longer assumes that an
observation lies into a single region of the regression tree, but rather that
it belongs to each region with a certain probability. Experiments conducted on
several data sets illustrate the good behavior of the proposed extension.
Stein Variational Gradient Descent as Moment Matching
Authors: Qiang Liu, Dilin Wang
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12

Stein variational gradient descent (SVGD) is a non-parametric inference
algorithm that evolves a set of particles to fit a given distribution of
interest. We analyze the non-asymptotic properties of SVGD, showing that there
exists a set of functions, which we call the Stein matching set, whose
expectations are exactly estimated by any set of particles that satisfies the
fixed point equation of SVGD. This set is the image of Stein operator applied
on the feature maps of the positive definite kernel used in SVGD. Our results
provide a theoretical framework for analyzing the properties of SVGD with
different kernels, shedding insight into optimal kernel choice. In particular,
we show that SVGD with linear kernels yields exact estimation of means and
variances on Gaussian distributions, while random Fourier features enable
probabilistic bounds for distributional approximation. Our results offer a
refreshing view of the classical inference problem as fitting Stein's identity
or solving the Stein equation, which may motivate more efficient algorithms.
3D Terrain Segmentation in the SWIR Spectrum
Authors: Dalton Rosario, Anthony Ortiz, Olac Fuentes
Date: 2018-10-27 00:00:00
Tasks: Image-Based Localization

We focus on the automatic 3D terrain segmentation problem using hyperspectral
shortwave IR (HS-SWIR) imagery and 3D Digital Elevation Models (DEM). The
datasets were independently collected, and metadata for the HS-SWIR dataset are
unavailable. We explore an overall slope of the SWIR spectrum that correlates
with the presence of moisture in soil to propose a band ratio test to be used
as a proxy for soil moisture content to distinguish two broad classes of
objects: live vegetation from impermeable manmade surface. We show that image
based localization techniques combined with the Optimal Randomized RANdom
Sample Consensus (RANSAC) algorithm achieve precise spatial matches between
HS-SWIR data of a portion of downtown Los Angeles (LA (USA)) and the Visible
image of a geo-registered 3D DEM, covering a wider-area of LA. Our
spectral-elevation rule based approach yields an overall accuracy of 97.7%,
segmenting the object classes into buildings, houses, trees, grass, and
roads/parking lots.
Accelerated Inference in Markov Random Fields via Smooth Riemannian Optimization
Authors: Siyi Hu, Luca Carlone
Date: 2018-10-27 00:00:00
Tasks: Image Segmentation, Riemannian optimization, Semantic Segmentation

Markov Random Fields (MRFs) are a popular model for several pattern
recognition and reconstruction problems in robotics and computer vision.
Inference in MRFs is intractable in general and related work resorts to
approximation algorithms. Among those techniques, semidefinite programming
(SDP) relaxations have been shown to provide accurate estimates while scaling
poorly with the problem size and being typically slow for practical
applications. Our first contribution is to design a dual ascent method to solve
standard SDP relaxations that takes advantage of the geometric structure of the
problem to speed up computation. This technique, named Dual Ascent Riemannian
Staircase (DARS), is able to solve large problem instances in seconds. Our
second contribution is to develop a second and faster approach. The backbone of
this second approach is a novel SDP relaxation combined with a fast and
scalable solver based on smooth Riemannian optimization. We show that this
approach, named Fast Unconstrained SEmidefinite Solver (FUSES), can solve large
problems in milliseconds. Contrarily to local MRF solvers, e.g., loopy belief
propagation, our approaches do not require an initial guess. Moreover, we
leverage recent results from optimization theory to provide per-instance
sub-optimality guarantees. We demonstrate the proposed approaches in
multi-class image segmentation problems. Extensive experimental evidence shows
that (i) FUSES and DARS produce near-optimal solutions, attaining an objective
within 0.1% of the optimum, (ii) FUSES and DARS are remarkably faster than
general-purpose SDP solvers, and FUSES is more than two orders of magnitude
faster than DARS while attaining similar solution quality, (iii) FUSES is
faster than local search methods while being a global solver.
The Variational Deficiency Bottleneck
Authors: Pradeep Kr. Banerjee, Guido Montúfar
Date: 2018-10-27 00:00:00
Tasks: General Classification

We introduce a bottleneck method for learning data representations based on information deficiency, rather than the more traditional information sufficiency. A variational upper bound allows us to implement this method efficiently. The bound itself is bounded above by the variational information bottleneck objective, and the two methods coincide in the regime of single-shot Monte Carlo approximations. The notion of deficiency provides a principled way of approximating complicated channels by relatively simpler ones. We show that the deficiency of one channel with respect to another has an operational interpretation in terms of the optimal risk gap of decision problems, capturing classification as a special case. Experiments demonstrate that the deficiency bottleneck can provide advantages in terms of minimal sufficiency as measured by information bottleneck curves, while retaining robust test performance in classification tasks.
Average Convergence Rate of Evolutionary Algorithms II: Continuous Optimization
Authors: Yu Chen, Jun He
Date: 2018-10-27 00:00:00
Tasks: Evolutionary Algorithms

The average convergence rate (ACR) measures how fast the approximation error of an evolutionary algorithm converges to zero per generation. It is defined as the geometric average of the reduction rate of the approximation error over consecutive generations. This paper makes a theoretical analysis of the ACR in continuous optimization. The obtained results are summarized as follows. According to the limit property, the ACR is classified into two categories: (1) linear ACR whose limit inferior value is larger than a positive and (2) sublinear ACR whose value converges to zero. Then, it is proven that the ACR is linear for evolutionary programming using positive landscape-adaptive mutation, but sublinear for that using landscape-invariant or zero landscape-adaptive mutation. The relationship between the ACR and the decision space dimension is also classified into two categories: (1) polynomial ACR whose value is larger than the reciprocal of a polynomial function of the dimension for any generation, and (2) exponential ACR whose value is less than the reciprocal of an exponential function of the dimension for an exponential long period. It is proven that for easy problems such as linear functions, the ACR of the (1+1) adaptive random univariate search is polynomial. But for hard functions such as the deceptive function, the ACR of both the (1+1) adaptive random univariate search and evolutionary programming is exponential.
A no-regret generalization of hierarchical softmax to extreme multi-label classification
Authors: Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczyński
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Extreme Multi-Label Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Extreme multi-label classification (XMLC) is a problem of tagging an instance
with a small subset of relevant labels chosen from an extremely large pool of
possible labels. Large label spaces can be efficiently handled by organizing
labels as a tree, like in the hierarchical softmax (HSM) approach commonly used
for multi-class problems. In this paper, we investigate probabilistic label
trees (PLTs) that have been recently devised for tackling XMLC problems. We
show that PLTs are a no-regret multi-label generalization of HSM when
precision@k is used as a model evaluation metric. Critically, we prove that
pick-one-label heuristic - a reduction technique from multi-label to
multi-class that is routinely used along with HSM - is not consistent in
general. We also show that our implementation of PLTs, referred to as
extremeText (XT), obtains significantly better results than HSM with the
pick-one-label heuristic and XML-CNN, a deep network specifically designed for
XMLC problems. Moreover, XT is competitive to many state-of-the-art approaches
in terms of statistical performance, model size and prediction time which makes
it amenable to deploy in an online system.
Towards Smart City Innovation Under the Perspective of Software-Defined Networking, Artificial Intelligence and Big Data
Authors: Joberto S. B. Martins
Date: 2018-10-27 00:00:00

Smart city projects address many of the current problems afflicting high
populated areas and cities and, as such, are a target for government,
institutions and private organizations that plan to explore its foreseen
advantages. In technical terms, smart city projects present a complex set of
requirements including a large number users with highly different and
heterogeneous requirements. In this scenario, this paper proposes and analyses
the impact and perspectives on adopting software-defined networking and
artificial intelligence as innovative approaches for smart city project
development and deployment. Big data is also considered as an inherent element
of most smart city project that must be tackled. A framework layered view is
proposed with a discussion about software-defined networking and machine
learning impacts on innovation followed by a use case that demonstrates the
potential benefits of cognitive learning for smart cities. It is argued that
the complexity of smart city projects do require new innovative approaches that
potentially result in more efficient and intelligent systems.
Suspicious News Detection Using Micro Blog Text
Authors: Tsubasa Tagami, Hiroki Ouchi, Hiroki Asano, Kazuaki Hanawa, Kaori Uchiyama, Kaito Suzuki, Kentaro Inui, Atsushi Komiya, Atsuo Fujimura, Hitofumi Yanai, Ryo Yamashita, Akinori Machino
Date: 2018-10-27 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Articles, BIG-bench Machine Learning, Fact Checking

We present a new task, suspicious news detection using micro blog text. This
task aims to support human experts to detect suspicious news articles to be
verified, which is costly but a crucial step before verifying the truthfulness
of the articles. Specifically, in this task, given a set of posts on SNS
referring to a news article, the goal is to judge whether the article is to be
verified or not. For this task, we create a publicly available dataset in
Japanese and provide benchmark results by using several basic machine learning
techniques. Experimental results show that our models can reduce the cost of
manual fact-checking process.
3D MRI brain tumor segmentation using autoencoder regularization
Authors: Andriy Myronenko
Date: 2018-10-27 00:00:00
Tasks: Brain Tumor Segmentation, Decoder, Segmentation, Semantic Segmentation, Tumor Segmentation

Automated segmentation of brain tumors from 3D magnetic resonance images
(MRIs) is necessary for the diagnosis, monitoring, and treatment planning of
the disease. Manual delineation practices require anatomical knowledge, are
expensive, time consuming and can be inaccurate due to human error. Here, we
describe a semantic segmentation network for tumor subregion segmentation from
3D MRIs based on encoder-decoder architecture. Due to a limited training
dataset size, a variational auto-encoder branch is added to reconstruct the
input image itself in order to regularize the shared decoder and impose
additional constraints on its layers. The current approach won 1st place in the
BraTS 2018 challenge.
On the Equivalence of Convolutional and Hadamard Networks using DFT
Authors: Marcel Crasmaru
Date: 2018-10-27 00:00:00

In this paper we introduce activation functions that move the entire
computation of Convolutional Networks into the frequency domain, where they are
actually Hadamard Networks. To achieve this result we employ the properties of
Discrete Fourier Transform. We present some implementation details and
experimental results, as well as some insights into why convolutional networks
perform well in learning use cases.
Fabrik: An Online Collaborative Neural Network Editor
Authors: Utsav Garg, Viraj Prabhu, Deshraj Yadav, Ram Ramrakhya, Harsh Agrawal, Dhruv Batra
Date: 2018-10-27 00:00:00

We present Fabrik, an online neural network editor that provides tools to
visualize, edit, and share neural networks from within a browser. Fabrik
provides a simple and intuitive GUI to import neural networks written in
popular deep learning frameworks such as Caffe, Keras, and TensorFlow, and
allows users to interact with, build, and edit models via simple drag and drop.
Fabrik is designed to be framework agnostic and support high interoperability,
and can be used to export models back to any supported framework. Finally, it
provides powerful collaborative features to enable users to iterate over model
design remotely and at scale.
Removing Hidden Confounding by Experimental Grounding
Authors: Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Causal Inference

Observational data is increasingly used as a means for making
individual-level causal predictions and intervention recommendations. The
foremost challenge of causal inference from observational data is hidden
confounding, whose presence cannot be tested in data and can invalidate any
causal conclusion. Experimental data does not suffer from confounding but is
usually limited in both scope and scale. We introduce a novel method of using
limited experimental data to correct the hidden confounding in causal effect
models trained on larger observational data, even if the observational data
does not fully overlap with the experimental data. Our method makes strictly
weaker assumptions than existing approaches, and we prove conditions under
which it yields a consistent estimator. We demonstrate our method's efficacy
using real-world data from a large educational experiment.