pwc-archive/papers-with-abstracts huggingface dataset:
Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling
Authors: Sam Kriegman, Marcin Szubert, Josh C. Bongard, Christian Skalka
Date: 2017-06-24 00:00:00
Tasks: regression

Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia.
Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations
Authors: Damla Kizilay, Deniz T. Eliiyi, Pascal Van Hentenryck
Date: 2017-12-14 00:00:00
Tasks: Scheduling

This paper considers the integrated problem of quay crane assignment, quay
crane scheduling, yard location assignment, and vehicle dispatching operations
at a container terminal. The main objective is to minimize vessel turnover
times and maximize the terminal throughput, which are key economic drivers in
terminal operations. Due to their computational complexities, these problems
are not optimized jointly in existing work. This paper revisits this limitation
and proposes Mixed Integer Programming (MIP) and Constraint Programming (CP)
models for the integrated problem, under some realistic assumptions.
Experimental results show that the MIP formulation can only solve small
instances, while the CP model finds optimal solutions in reasonable times for
realistic instances derived from actual container terminal operations.
Accelerated Image Reconstruction for Nonlinear Diffractive Imaging
Authors: Yanting Ma, Hassan Mansour, Dehong Liu, Petros T. Boufounos, Ulugbek S. Kamilov
Date: 2017-08-04 00:00:00
Tasks: Image Reconstruction

The problem of reconstructing an object from the measurements of the light it
scatters is common in numerous imaging applications. While the most popular
formulations of the problem are based on linearizing the object-light
relationship, there is an increased interest in considering nonlinear
formulations that can account for multiple light scattering. In this paper, we
propose an image reconstruction method, called CISOR, for nonlinear diffractive
imaging, based on a nonconvex optimization formulation with total variation
(TV) regularization. The nonconvex solver used in CISOR is our new variant of
fast iterative shrinkage/thresholding algorithm (FISTA). We provide fast and
memory-efficient implementation of the new FISTA variant and prove that it
reliably converges for our nonconvex optimization problem. In addition, we
systematically compare our method with other state-of-the-art methods on
simulated as well as experimentally measured data in both 2D and 3D settings.
A Bayesian Clearing Mechanism for Combinatorial Auctions
Authors: Gianluca Brero, Sébastien Lahaie
Date: 2017-12-14 00:00:00

We cast the problem of combinatorial auction design in a Bayesian framework
in order to incorporate prior information into the auction process and minimize
the number of rounds to convergence. We first develop a generative model of
agent valuations and market prices such that clearing prices become maximum a
posteriori estimates given observed agent valuations. This generative model
then forms the basis of an auction process which alternates between refining
estimates of agent valuations and computing candidate clearing prices. We
provide an implementation of the auction using assumed density filtering to
estimate valuations and expectation maximization to compute prices. An
empirical evaluation over a range of valuation domains demonstrates that our
Bayesian auction mechanism is highly competitive against the combinatorial
clock auction in terms of rounds to convergence, even under the most favorable
choices of price increment for this baseline.
Strictly proper kernel scores and characteristic kernels on compact spaces
Authors: Ingo Steinwart, Johanna F. Ziegel
Date: 2017-12-14 00:00:00
Tasks: Translation

Strictly proper kernel scores are well-known tool in probabilistic
forecasting, while characteristic kernels have been extensively investigated in
the machine learning literature. We first show that both notions coincide, so
that insights from one part of the literature can be used in the other. We then
show that the metric induced by a characteristic kernel cannot reliably
distinguish between distributions that are far apart in the total variation
norm as soon as the underlying space of measures is infinite dimensional. In
addition, we provide a characterization of characteristic kernels in terms of
eigenvalues and -functions and apply this characterization to the case of
continuous kernels on (locally) compact spaces. In the compact case we further
show that characteristic kernels exist if and only if the space is metrizable.
As special cases of our general theory we investigate translation-invariant
kernels on compact Abelian groups and isotropic kernels on spheres. The latter
are of particular interest for forecast evaluation of probabilistic predictions
on spherical domains as frequently encountered in meteorology and climatology.
A Performance Evaluation of Local Features for Image Based 3D Reconstruction
Authors: Bin Fan, Qingqun Kong, Xinchao Wang, Zhiheng Wang, Shiming Xiang, Chunhong Pan, Pascal Fua
Date: 2017-12-14 00:00:00
Tasks: 3D Reconstruction

This paper performs a comprehensive and comparative evaluation of the state
of the art local features for the task of image based 3D reconstruction. The
evaluated local features cover the recently developed ones by using powerful
machine learning techniques and the elaborately designed handcrafted features.
To obtain a comprehensive evaluation, we choose to include both float type
features and binary ones. Meanwhile, two kinds of datasets have been used in
this evaluation. One is a dataset of many different scene types with
groundtruth 3D points, containing images of different scenes captured at fixed
positions, for quantitative performance evaluation of different local features
in the controlled image capturing situations. The other dataset contains
Internet scale image sets of several landmarks with a lot of unrelated images,
which is used for qualitative performance evaluation of different local
features in the free image collection situations. Our experimental results show
that binary features are competent to reconstruct scenes from controlled image
sequences with only a fraction of processing time compared to use float type
features. However, for the case of large scale image set with many distracting
images, float type features show a clear advantage over binary ones.
Counterfactual Multi-Agent Policy Gradients
Authors: Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon Whiteson
Date: 2017-05-24 00:00:00
Tasks: Autonomous Vehicles, counterfactual, Reinforcement Learning, SMAC+, Starcraft

Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.
Improved Linear Embeddings via Lagrange Duality
Authors: Kshiteej Sheth, Dinesh Garg, Anirban Dasgupta
Date: 2017-11-30 00:00:00

Near isometric orthogonal embeddings to lower dimensions are a fundamental
tool in data science and machine learning. In this paper, we present the
construction of such embeddings that minimizes the maximum distortion for a
given set of points. We formulate the problem as a non convex constrained
optimization problem. We first construct a primal relaxation and then use the
theory of Lagrange duality to create dual relaxation. We also suggest a
polynomial time algorithm based on the theory of convex optimization to solve
the dual relaxation provably. We provide a theoretical upper bound on the
approximation guarantees for our algorithm, which depends only on the spectral
properties of the dataset. We experimentally demonstrate the superiority of our
algorithm compared to baselines in terms of the scalability and the ability to
achieve lower distortion.
Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization
Authors: Freek Stulp, Pierre-Yves Oudeyer
Date: 2017-12-14 00:00:00
Tasks: Stochastic Optimization

To harness the complexity of their high-dimensional bodies during
sensorimotor development, infants are guided by patterns of freezing and
freeing of degrees of freedom. For instance, when learning to reach, infants
free the degrees of freedom in their arm proximodistally, i.e. from joints that
are closer to the body to those that are more distant. Here, we formulate and
study computationally the hypothesis that such patterns can emerge
spontaneously as the result of a family of stochastic optimization processes
(evolution strategies with covariance-matrix adaptation), without an innate
encoding of a maturational schedule. In particular, we present simulated
experiments with an arm where a computational learner progressively acquires
reaching skills through adaptive exploration, and we show that a proximodistal
organization appears spontaneously, which we denote PDFF (ProximoDistal
Freezing and Freeing of degrees of freedom). We also compare this emergent
organization between different arm morphologies -- from human-like to quite
unnatural ones -- to study the effect of different kinematic structures on the
emergence of PDFF. Keywords: human motor learning; proximo-distal exploration;
stochastic optimization; modelling; evolution strategies; cross-entropy
methods; policy search; morphology.}
Image Super-resolution via Feature-augmented Random Forest
Authors: Hailiang Li, Kin-Man Lam, Miaohui Wang
Date: 2017-12-14 00:00:00
Tasks: Content-Based Image Retrieval, Dictionary Learning, Dimensionality Reduction, Image Retrieval, Image Super-Resolution, Retrieval, Super-Resolution

Recent random-forest (RF)-based image super-resolution approaches inherit
some properties from dictionary-learning-based algorithms, but the
effectiveness of the properties in RF is overlooked in the literature. In this
paper, we present a novel feature-augmented random forest (FARF) for image
super-resolution, where the conventional gradient-based features are augmented
with gradient magnitudes and different feature recipes are formulated on
different stages in an RF. The advantages of our method are that, firstly, the
dictionary-learning-based features are enhanced by adding gradient magnitudes,
based on the observation that the non-linear gradient magnitude are with highly
discriminative property. Secondly, generalized locality-sensitive hashing (LSH)
is used to replace principal component analysis (PCA) for feature
dimensionality reduction and original high-dimensional features are employed,
instead of the compressed ones, for the leaf-nodes' regressors, since
regressors can benefit from higher dimensional features. This
original-compressed coupled feature sets scheme unifies the unsupervised LSH
evaluation on both image super-resolution and content-based image retrieval
(CBIR). Finally, we present a generalized weighted ridge regression (GWRR)
model for the leaf-nodes' regressors. Experiment results on several public
benchmark datasets show that our FARF method can achieve an average gain of
about 0.3 dB, compared to traditional RF-based methods. Furthermore, a
fine-tuned FARF model can compare to or (in many cases) outperform some recent
stateof-the-art deep-learning-based algorithms.
Intrinsic Point of Interest Discovery from Trajectory Data
Authors: Matthew Piekenbrock, Derek Doran
Date: 2017-12-14 00:00:00

This paper presents a framework for intrinsic point of interest discovery
from trajectory databases. Intrinsic points of interest are regions of a
geospatial area innately defined by the spatial and temporal aspects of
trajectory data, and can be of varying size, shape, and resolution. Any
trajectory database exhibits such points of interest, and hence are intrinsic,
as compared to most other point of interest definitions which are said to be
extrinsic, as they require trajectory metadata, external knowledge about the
region the trajectories are observed, or other application-specific
information. Spatial and temporal aspects are qualities of any trajectory
database, making the framework applicable to data from any domain and of any
resolution. The framework is developed under recent developments on the
consistency of nonparametric hierarchical density estimators and enables the
possibility of formal statistical inference and evaluation over such intrinsic
points of interest. Comparisons of the POIs uncovered by the framework in
synthetic truth data to thousands of parameter settings for common POI
discovery methods show a marked improvement in fidelity without the need to
tune any parameters by hand.
CNNs are Globally Optimal Given Multi-Layer Support
Authors: Chen Huang, Chen Kong, Simon Lucey
Date: 2017-12-07 00:00:00

Stochastic Gradient Descent (SGD) is the central workhorse for training
modern CNNs. Although giving impressive empirical performance it can be slow to
converge. In this paper we explore a novel strategy for training a CNN using an
alternation strategy that offers substantial speedups during training. We make
the following contributions: (i) replace the ReLU non-linearity within a CNN
with positive hard-thresholding, (ii) reinterpret this non-linearity as a
binary state vector making the entire CNN linear if the multi-layer support is
known, and (iii) demonstrate that under certain conditions a global optima to
the CNN can be found through local descent. We then employ a novel alternation
strategy (between weights and support) for CNN training that leads to
substantially faster convergence rates, nice theoretical properties, and
achieving state of the art results across large scale datasets (e.g. ImageNet)
as well as other standard benchmarks.
Robust Estimation of Similarity Transformation for Visual Object Tracking
Authors: Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu
Date: 2017-12-14 00:00:00
Tasks: Object, Object Tracking, Video Object Tracking, Visual Object Tracking

Most of existing correlation filter-based tracking approaches only estimate
simple axis-aligned bounding boxes, and very few of them is capable of
recovering the underlying similarity transformation. To tackle this challenging
problem, in this paper, we propose a new correlation filter-based tracker with
a novel robust estimation of similarity transformation on the large
displacements. In order to efficiently search in such a large 4-DoF space in
real-time, we formulate the problem into two 2-DoF sub-problems and apply an
efficient Block Coordinates Descent solver to optimize the estimation result.
Specifically, we employ an efficient phase correlation scheme to deal with both
scale and rotation changes simultaneously in log-polar coordinates. Moreover, a
variant of correlation filter is used to predict the translational motion
individually. Our experimental results demonstrate that the proposed tracker
achieves very promising prediction performance compared with the
state-of-the-art visual object tracking methods while still retaining the
advantages of high efficiency and simplicity in conventional correlation
filter-based tracking methods.
Adaptive kNN using Expected Accuracy for Classification of Geo-Spatial Data
Authors: Mark Kibanov, Martin Becker, Juergen Mueller, Martin Atzmueller, Andreas Hotho, Gerd Stumme
Date: 2017-12-14 00:00:00
Tasks: Classification, General Classification

The k-Nearest Neighbor (kNN) classification approach is conceptually simple -
yet widely applied since it often performs well in practical applications.
However, using a global constant k does not always provide an optimal solution,
e.g., for datasets with an irregular density distribution of data points. This
paper proposes an adaptive kNN classifier where k is chosen dynamically for
each instance (point) to be classified, such that the expected accuracy of
classification is maximized. We define the expected accuracy as the accuracy of
a set of structurally similar observations. An arbitrary similarity function
can be used to find these observations. We introduce and evaluate different
similarity functions. For the evaluation, we use five different classification
tasks based on geo-spatial data. Each classification task consists of (tens of)
thousands of items. We demonstrate, that the presented expected accuracy
measures can be a good estimator for kNN performance, and the proposed adaptive
kNN classifier outperforms common kNN and previously introduced adaptive kNN
algorithms. Also, we show that the range of considered k can be significantly
reduced to speed up the algorithm without negative influence on classification
accuracy.
Counterfactual Learning for Machine Translation: Degeneracies and Solutions
Authors: Carolin Lawrence, Pratik Gajane, Stefan Riezler
Date: 2017-11-23 00:00:00
Tasks: counterfactual, Machine Translation, Translation

Counterfactual learning is a natural scenario to improve web-based machine
translation services by offline learning from feedback logged during user
interactions. In order to avoid the risk of showing inferior translations to
users, in such scenarios mostly exploration-free deterministic logging policies
are in place. We analyze possible degeneracies of inverse and reweighted
propensity scoring estimators, in stochastic and deterministic settings, and
relate them to recently proposed techniques for counterfactual learning under
deterministic logging.
Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation
Authors: Carolin Lawrence, Artem Sokolov, Stefan Riezler
Date: 2017-07-28 00:00:00
Tasks: counterfactual, Machine Translation, Translation

The goal of counterfactual learning for statistical machine translation (SMT)
is to optimize a target SMT system from logged data that consist of user
feedback to translations that were predicted by another, historic SMT system. A
challenge arises by the fact that risk-averse commercial SMT systems
deterministically log the most probable translation. The lack of sufficient
exploration of the SMT output space seemingly contradicts the theoretical
requirements for counterfactual learning. We show that counterfactual learning
from deterministic bandit logs is possible nevertheless by smoothing out
deterministic components in learning. This can be achieved by additive and
multiplicative control variates that avoid degenerate behavior in empirical
risk minimization. Our simulation experiments show improvements of up to 2 BLEU
points by counterfactual learning from deterministic bandit feedback.
Lucid Data Dreaming for Video Object Segmentation
Authors: Anna Khoreva, Rodrigo Benenson, Eddy Ilg, Thomas Brox, Bernt Schiele
Date: 2017-03-28 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Convolutional networks reach top quality in pixel-level video object
segmentation but require a large amount of training data (1k~100k) to deliver
such results. We propose a new training strategy which achieves
state-of-the-art results across three evaluation datasets while using 20x~1000x
less annotated data than competing methods. Our approach is suitable for both
single and multiple object segmentation. Instead of using large training sets
hoping to generalize across domains, we generate in-domain training data using
the provided annotation on the first frame of each video to synthesize ("lucid
dream") plausible future video frames. In-domain per-video training data allows
us to train high quality appearance- and motion-based models, as well as tune
the post-processing stage. This approach allows to reach competitive results
even when training from only a single annotated frame, without ImageNet
pre-training. Our results indicate that using a larger training set is not
automatically better, and that for the video object segmentation task a smaller
training set that is closer to the target domain is more effective. This
changes the mindset regarding how many training samples and general
"objectness" knowledge are required for the video object segmentation task.
Top-down Transformation Choice
Authors: Torsten Hothorn
Date: 2017-06-26 00:00:00

Simple models are preferred over complex models, but over-simplistic models
could lead to erroneous interpretations. The classical approach is to start
with a simple model, whose shortcomings are assessed in residual-based model
diagnostics. Eventually, one increases the complexity of this initial overly
simple model and obtains a better-fitting model. I illustrate how
transformation analysis can be used as an alternative approach to model choice.
Instead of adding complexity to simple models, step-wise complexity reduction
is used to help identify simpler and better-interpretable models. As an
example, body mass index distributions in Switzerland are modelled by means of
transformation models to understand the impact of sex, age, smoking and other
lifestyle factors on a person's body mass index. In this process, I searched
for a compromise between model fit and model interpretability. Special emphasis
is given to the understanding of the connections between transformation models
of increasing complexity. The models used in this analysis ranged from
evergreens, such as the normal linear regression model with constant variance,
to novel models with extremely flexible conditional distribution functions,
such as transformation trees and transformation forests.