pwc-archive/papers-with-abstracts huggingface dataset:
Deterministic Hypothesis Generation for Robust Fitting of Multiple Structures
Authors: Kwang Hee Lee, Chanki Yu, Sang Wook Lee
Date: 2018-07-25 00:00:00
Tasks: global-optimization

We present a novel algorithm for generating robust and consistent hypotheses
for multiple-structure model fitting. Most of the existing methods utilize
random sampling which produce varying results especially when outlier ratio is
high. For a structure where a model is fitted, the inliers of other structures
are regarded as outliers when multiple structures are present. Global
optimization has recently been investigated to provide stable and unique
solutions, but the computational cost of the algorithms is prohibitively high
for most image data with reasonable sizes. The algorithm presented in this
paper uses a maximum feasible subsystem (MaxFS) algorithm to generate
consistent initial hypotheses only from partial datasets in spatially
overlapping local image regions. Our assumption is that each genuine structure
will exist as a dominant structure in at least one of the local regions. To
refine initial hypotheses estimated from partial datasets and to remove
residual tolerance dependency of the MaxFS algorithm, iterative re-weighted L1
(IRL1) minimization is performed for all the image data. Initial weights of
IRL1 framework are determined from the initial hypotheses generated in local
regions. Our approach is significantly more efficient than those that use only
global optimization for all the image data. Experimental results demonstrate
that the presented method can generate more reliable and consistent hypotheses
than random-sampling methods for estimating single and multiple structures from
data with a large amount of outliers. We clearly expose the influence of
algorithm parameter settings on the results in our experiments.
Efficient algorithms for robust submodular maximization under matroid constraints
Authors: Sebastian Pokutta, Mohit Singh, Alfredo Torrico
Date: 2018-07-25 00:00:00

In this work, we consider robust submodular maximization with matroid
constraints. We give an efficient bi-criteria approximation algorithm that
outputs a small family of feasible sets whose union has (nearly) optimal
objective value. This algorithm theoretically performs less function calls than
previous works at cost of adding more elements to the final solution. We also
provide significant implementation improvements showing that our algorithm
outperforms the algorithms in the existing literature. We finally assess the
performance of our contributions in three real-world applications.
LAPRAN: A Scalable Laplacian Pyramid Reconstructive Adversarial Network for Flexible Compressive Sensing Reconstruction
Authors: Kai Xu, Zhikang Zhang, Fengbo Ren
Date: 2018-07-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Compressive Sensing, SSIM

This paper addresses the single-image compressive sensing (CS) and
reconstruction problem. We propose a scalable Laplacian pyramid reconstructive
adversarial network (LAPRAN) that enables high-fidelity, flexible and fast CS
images reconstruction. LAPRAN progressively reconstructs an image following the
concept of Laplacian pyramid through multiple stages of reconstructive
adversarial networks (RANs). At each pyramid level, CS measurements are fused
with a contextual latent vector to generate a high-frequency image residual.
Consequently, LAPRAN can produce hierarchies of reconstructed images and each
with an incremental resolution and improved quality. The scalable pyramid
structure of LAPRAN enables high-fidelity CS reconstruction with a flexible
resolution that is adaptive to a wide range of compression ratios (CRs), which
is infeasible with existing methods. Experimental results on multiple public
datasets show that LAPRAN offers an average 7.47dB and 5.98dB PSNR, and an
average 57.93% and 33.20% SSIM improvement compared to model-based and
data-driven baselines, respectively.
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems
Authors: Timothy A. Mann, Sven Gowal, András György, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, Prav Srinivasan
Date: 2018-07-24 00:00:00
Tasks: Recommendation Systems

Predicting delayed outcomes is an important problem in recommender systems (e.g., if customers will finish reading an ebook). We formalize the problem as an adversarial, delayed online learning problem and consider how a proxy for the delayed outcome (e.g., if customers read a third of the book in 24 hours) can help minimize regret, even though the proxy is not available when making a prediction. Motivated by our regret analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if the proxy is informative of the outcome in hindsight, and Residual Factored Forecaster (RFF) that is robust to a non-informative proxy. Experiments on two real-world datasets for predicting human behavior show that RFF outperforms both FF and a direct forecaster that does not make use of the proxy. Our results suggest that exploiting proxies by factorization is a promising way to mitigate the impact of long delays in human-behavior prediction tasks.
On the Randomized Complexity of Minimizing a Convex Quadratic Function
Authors: Max Simchowitz
Date: 2018-07-24 00:00:00

Minimizing a convex, quadratic objective of the form
$f_{\mathbf{A},\mathbf{b}}(x) := \frac{1}{2}x^\top \mathbf{A} x - \langle
\mathbf{b}, x \rangle$ for $\mathbf{A} \succ 0 $ is a fundamental problem in
machine learning and optimization. In this work, we prove gradient-query
complexity lower bounds for minimizing convex quadratic functions which apply
to both deterministic and \emph{randomized} algorithms. Specifically, for
$\kappa > 1$, we exhibit a distribution over $(\mathbf{A},\mathbf{b})$ with
condition number $\mathrm{cond}(\mathbf{A}) \le \kappa$, such that any
\emph{randomized} algorithm requires $\Omega(\sqrt{\kappa})$ gradient queries
to find a solution $\hat x$ for which $\|\hat x - \mathbf x_\star\| \le
\epsilon_0\|\mathbf{x}_{\star}\|$, where $\mathbf x_{\star} =
\mathbf{A}^{-1}\mathbf{b}$ is the optimal solution, and $\epsilon_0$ a small
constant. Setting $\kappa =1/\epsilon$, this lower bound implies the minimax
rate of $T = \Omega(\lambda_1(\mathbf{A})\|\mathbf
x_\star\|^2/\sqrt{\epsilon})$ queries required to minimize an arbitrary convex
quadratic function up to error $f(\hat{x}) - f(\mathbf x_\star) \le \epsilon$.
  Our lower bound holds for a distribution derived from classical ensembles in
random matrix theory, and relies on a careful reduction from adaptively
estimating a planted vector $\mathbf u$ in a deformed Wigner model. A key step
in deriving sharp lower bounds is demonstrating that the optimization error
$\mathbf x_\star - \hat x$ cannot align too closely with $\mathbf{u}$. To this
end, we prove an upper bound on the cosine between $\mathbf x_\star - \hat x$
and $\mathbf u$ in terms of the MMSE of estimating the plant $\mathbf u$ in a
deformed Wigner model. We then bound the MMSE by carefully modifying a result
due to Lelarge and Miolane 2016, which rigorously establishes a general
replica-symmetric formula for planted matrix models.
Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
Authors: Aysegul Dundar, Ming-Yu Liu, Ting-Chun Wang, John Zedlewski, Jan Kautz
Date: 2018-07-24 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Style Transfer, Translation

Deep neural networks have largely failed to effectively utilize synthetic
data when applied to real images due to the covariate shift problem. In this
paper, we show that by applying a straightforward modification to an existing
photorealistic style transfer algorithm, we achieve state-of-the-art
synthetic-to-real domain adaptation results. We conduct extensive experimental
validations on four synthetic-to-real tasks for semantic segmentation and
object detection, and show that our approach exceeds the performance of any
current state-of-the-art GAN-based image translation approach as measured by
segmentation and object detection metrics. Furthermore we offer a distance
based analysis of our method which shows a dramatic reduction in Frechet
Inception distance between the source and target domains, offering a
quantitative metric that demonstrates the effectiveness of our algorithm in
bridging the synthetic-to-real gap.
On sampling from a log-concave density using kinetic Langevin diffusions
Authors: Arnak S. Dalalyan, Lionel Riou-Durand
Date: 2018-07-24 00:00:00

Langevin diffusion processes and their discretizations are often used for
sampling from a target density. The most convenient framework for assessing the
quality of such a sampling scheme corresponds to smooth and strongly
log-concave densities defined on $\mathbb R^p$. The present work focuses on
this framework and studies the behavior of Monte Carlo algorithms based on
discretizations of the kinetic Langevin diffusion. We first prove the geometric
mixing property of the kinetic Langevin diffusion with a mixing rate that is,
in the overdamped regime, optimal in terms of its dependence on the condition
number. We then use this result for obtaining improved guarantees of sampling
using the kinetic Langevin Monte Carlo method, when the quality of sampling is
measured by the Wasserstein distance. We also consider the situation where the
Hessian of the log-density of the target distribution is Lipschitz-continuous.
In this case, we introduce a new discretization of the kinetic Langevin
diffusion and prove that this leads to a substantial improvement of the upper
bound on the sampling error measured in Wasserstein distance.
Contrastive Video Representation Learning via Adversarial Perturbations
Authors: Jue Wang, Anoop Cherian
Date: 2018-07-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Recognition, Binary Classification, Contrastive Learning, Representation Learning, Riemannian optimization, Video Recognition

Adversarial perturbations are noise-like patterns that can subtly change the data, while failing an otherwise accurate classifier. In this paper, we propose to use such perturbations within a novel contrastive learning setup to build negative samples, which are then used to produce improved video representations. To this end, given a well-trained deep model for per-frame video recognition, we first generate adversarial noise adapted to this model. Positive and negative bags are produced using the original data features from the full video sequence and their perturbed counterparts, respectively. Unlike the classic contrastive learning methods, we develop a binary classification problem that learns a set of discriminative hyperplanes -- as a subspace -- that will separate the two bags from each other. This subspace is then used as a descriptor for the video, dubbed \emph{discriminative subspace pooling}. As the perturbed features belong to data classes that are likely to be confused with the original features, the discriminative subspace will characterize parts of the feature space that are more representative of the original data, and thus may provide robust video representations. To learn such descriptors, we formulate a subspace learning objective on the Stiefel manifold and resort to Riemannian optimization methods for solving it efficiently. We provide experiments on several video datasets and demonstrate state-of-the-art results.
Unsupervised Learning with Self-Organizing Spiking Neural Networks
Authors: Hananel Hazan, Daniel J. Saunders, Darpan T. Sanghavi, Hava T. Siegelmann, Robert Kozma
Date: 2018-07-24 00:00:00
Tasks: General Classification

We present a system comprising a hybridization of self-organized map (SOM)
properties with spiking neural networks (SNNs) that retain many of the features
of SOMs. Networks are trained in an unsupervised manner to learn a
self-organized lattice of filters via excitatory-inhibitory interactions among
populations of neurons. We develop and test various inhibition strategies, such
as growing with inter-neuron distance and two distinct levels of inhibition.
The quality of the unsupervised learning algorithm is evaluated using examples
with known labels. Several biologically-inspired classification tools are
proposed and compared, including population-level confidence rating, and
n-grams using spike motif algorithm. Using the optimal choice of parameters,
our approach produces improvements over state-of-art spiking neural networks.
A Synchronized Stereo and Plenoptic Visual Odometry Dataset
Authors: Niclas Zeller, Franz Quint, Uwe Stilla
Date: 2018-07-24 00:00:00
Tasks: Visual Odometry

We present a new dataset to evaluate monocular, stereo, and plenoptic camera
based visual odometry algorithms. The dataset comprises a set of synchronized
image sequences recorded by a micro lens array (MLA) based plenoptic camera and
a stereo camera system. For this, the stereo cameras and the plenoptic camera
were assembled on a common hand-held platform. All sequences are recorded in a
very large loop, where beginning and end show the same scene. Therefore, the
tracking accuracy of a visual odometry algorithm can be measured from the drift
between beginning and end of the sequence. For both, the plenoptic camera and
the stereo system, we supply full intrinsic camera models, as well as
vignetting data. The dataset consists of 11 sequences which were recorded in
challenging indoor and outdoor scenarios. We present, by way of example, the
results achieved by state-of-the-art algorithms.
An Approximation Algorithm for Risk-averse Submodular Optimization
Authors: Lifeng Zhou, Pratap Tokekar
Date: 2018-07-24 00:00:00
Tasks: Combinatorial Optimization, Financial Analysis

We study the problem of incorporating risk while making combinatorial
decisions under uncertainty. We formulate a discrete submodular maximization
problem for selecting a set using Conditional-Value-at-Risk (CVaR), a risk
metric commonly used in financial analysis. While CVaR has recently been used
in optimization of linear cost functions in robotics, we take the first stages
towards extending this to discrete submodular optimization and provide several
positive results. Specifically, we propose the Sequential Greedy Algorithm that
provides an approximation guarantee on finding the maxima of the CVaR cost
function under a matroidal constraint. The approximation guarantee shows that
the solution produced by our algorithm is within a constant factor of the
optimal and an additive term that depends on the optimal. Our analysis uses the
curvature of the submodular set function, and proves that the algorithm runs in
polynomial time. This formulates a number of combinatorial optimization
problems that appear in robotics. We use two such problems, vehicle assignment
under uncertainty for mobility-on-demand and sensor selection with failures for
environmental monitoring, as case studies to demonstrate the efficacy of our
formulation.
Iterative Amortized Inference
Authors: Joseph Marino, Yisong Yue, Stephan Mandt
Date: 2018-07-24 00:00:00
Proceeding: ICML 2018 7
Tasks: Inference Optimization, Variational Inference

Inference models are a key component in scaling variational inference to deep
latent variable models, most notably as encoder networks in variational
auto-encoders (VAEs). By replacing conventional optimization-based inference
with a learned model, inference is amortized over data examples and therefore
more computationally efficient. However, standard inference models are
restricted to direct mappings from data to approximate posterior estimates. The
failure of these models to reach fully optimized approximate posterior
estimates results in an amortization gap. We aim toward closing this gap by
proposing iterative inference models, which learn to perform inference
optimization through repeatedly encoding gradients. Our approach generalizes
standard inference models in VAEs and provides insight into several empirical
findings, including top-down inference techniques. We demonstrate the inference
optimization capabilities of iterative inference models and show that they
outperform standard inference models on several benchmark data sets of images
and text.
Learning Plannable Representations with Causal InfoGAN
Authors: Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel
Date: 2018-07-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Representation Learning

In recent years, deep generative models have been shown to 'imagine'
convincing high-dimensional observations such as images, audio, and even video,
learning directly from raw data. In this work, we ask how to imagine
goal-directed visual plans -- a plausible sequence of observations that
transition a dynamical system from its current configuration to a desired goal
state, which can later be used as a reference trajectory for control. We focus
on systems with high-dimensional observations, such as images, and propose an
approach that naturally combines representation learning and planning. Our
framework learns a generative model of sequential observations, where the
generative process is induced by a transition in a low-dimensional planning
model, and an additional noise. By maximizing the mutual information between
the generated observations and the transition in the planning model, we obtain
a low-dimensional representation that best explains the causal nature of the
data. We structure the planning model to be compatible with efficient planning
algorithms, and we propose several such models based on either discrete or
continuous states. Finally, to generate a visual plan, we project the current
and goal observations onto their respective states in the planning model, plan
a trajectory, and then use the generative model to transform the trajectory to
a sequence of observations. We demonstrate our method on imagining plausible
visual plans of rope manipulation.
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Authors: Mattes Mollenhauer, Ingmar Schuster, Stefan Klus, Christof Schütte
Date: 2018-07-24 00:00:00
Tasks: Gaussian Processes

Reproducing kernel Hilbert spaces (RKHSs) play an important role in many statistics and machine learning applications ranging from support vector machines to Gaussian processes and kernel embeddings of distributions. Operators acting on such spaces are, for instance, required to embed conditional probability distributions in order to implement the kernel Bayes rule and build sequential data models. It was recently shown that transfer operators such as the Perron-Frobenius or Koopman operator can also be approximated in a similar fashion using covariance and cross-covariance operators and that eigenfunctions of these operators can be obtained by solving associated matrix eigenvalue problems. The goal of this paper is to provide a solid functional analytic foundation for the eigenvalue decomposition of RKHS operators and to extend the approach to the singular value decomposition. The results are illustrated with simple guiding examples.
Handwritten Digit Recognition by Elastic Matching
Authors: Sagnik Majumder, C. von der Malsburg, Aashish Richhariya, Surekha Bhanot
Date: 2018-07-24 00:00:00
Tasks: Face Recognition, Handwritten Digit Recognition, Translation

A simple model of MNIST handwritten digit recognition is presented here. The
model is an adaptation of a previous theory of face recognition. It realizes
translation and rotation invariance in a principled way instead of being based
on extensive learning from large masses of sample data. The presented
recognition rates fall short of other publications, but due to its
inspectability and conceptual and numerical simplicity, our system commends
itself as a basis for further development.
A Simple Probabilistic Model for Uncertainty Estimation
Authors: Alexander Kuvaev, Roman Khudorozhkov
Date: 2018-07-24 00:00:00
Tasks: Atrial Fibrillation Detection, model

The article focuses on determining the predictive uncertainty of a model on
the example of atrial fibrillation detection problem by a single-lead ECG
signal. To this end, the model predicts parameters of the beta distribution
over class probabilities instead of these probabilities themselves. It was
shown that the described approach allows to detect atypical recordings and
significantly improve the quality of the algorithm on confident predictions.
Automatic Bayesian Density Analysis
Authors: Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera
Date: 2018-07-24 00:00:00
Tasks: Anomaly Detection, Bayesian Inference, Density Estimation

Making sense of a dataset in an automatic and unsupervised fashion is a
challenging problem in statistics and AI. Classical approaches for {exploratory
data analysis} are usually not flexible enough to deal with the uncertainty
inherent to real-world data: they are often restricted to fixed latent
interaction models and homogeneous likelihoods; they are sensitive to missing,
corrupt and anomalous data; moreover, their expressiveness generally comes at
the price of intractable inference. As a result, supervision from statisticians
is usually needed to find the right model for the data. However, since domain
experts are not necessarily also experts in statistics, we propose Automatic
Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible
at large. Specifically, ABDA allows for automatic and efficient missing value
estimation, statistical data type and likelihood discovery, anomaly detection
and dependency structure mining, on top of providing accurate density
estimation. Extensive empirical evidence shows that ABDA is a suitable tool for
automatic exploratory analysis of mixed continuous and discrete tabular data.
Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
Authors: Christopher L. Choi, Jason Rebello, Leonid Koppel, Pranav Ganti, Arun Das, Steven L. Waslander
Date: 2018-07-24 00:00:00

Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more
cameras are mounted on actuated mechanisms such as a gimbal. Existing methods
for DCC calibration rely on joint angle measurements to resolve the
time-varying transformation between the dynamic and static camera. This
information is usually provided by motor encoders, however, joint angle
measurements are not always readily available on off-the-shelf mechanisms. In
this paper, we present an encoderless approach for DCC calibration which
simultaneously estimates the kinematic parameters of the transformation chain
as well as the unknown joint angles. We also demonstrate the integration of an
encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show
the extensions required in order to perform simultaneous online estimation of
the joint angles and vehicle localization state. The proposed calibration
approach is validated both in simulation and on a physical DCC composed of a
2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the
calibrated mechanism integrated into the OKVIS VIO package, and demonstrate
successful online joint angle estimation while maintaining localization
accuracy that is comparable to a standard static multi-camera configuration.