pwc-archive/papers-with-abstracts huggingface dataset:
Verb Physics: Relative Physical Knowledge of Actions and Objects
Authors: Maxwell Forbes, Yejin Choi
Date: 2017-06-12 00:00:00
Proceeding: ACL 2017 7

Learning commonsense knowledge from natural language text is nontrivial due
to reporting bias: people rarely state the obvious, e.g., "My house is bigger
than me." However, while rarely stated explicitly, this trivial everyday
knowledge does influence the way people talk about the world, which provides
indirect clues to reason about the world. For example, a statement like, "Tyler
entered his house" implies that his house is bigger than Tyler.
  In this paper, we present an approach to infer relative physical knowledge of
actions and objects along five dimensions (e.g., size, weight, and strength)
from unstructured natural language text. We frame knowledge acquisition as
joint inference over two closely related problems: learning (1) relative
physical knowledge of object pairs and (2) physical implications of actions
when applied to those object pairs. Empirical results demonstrate that it is
possible to extract knowledge of actions and objects from language and that
joint inference over different types of knowledge improves performance.
Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis
Authors: Mostafa Rahmani, George Atia
Date: 2016-09-15 00:00:00

This paper presents a remarkably simple, yet powerful, algorithm termed
Coherence Pursuit (CoP) to robust Principal Component Analysis (PCA). As
inliers lie in a low dimensional subspace and are mostly correlated, an inlier
is likely to have strong mutual coherence with a large number of data points.
By contrast, outliers either do not admit low dimensional structures or form
small clusters. In either case, an outlier is unlikely to bear strong
resemblance to a large number of data points. Given that, CoP sets an outlier
apart from an inlier by comparing their coherence with the rest of the data
points. The mutual coherences are computed by forming the Gram matrix of the
normalized data points. Subsequently, the sought subspace is recovered from the
span of the subset of the data points that exhibit strong coherence with the
rest of the data. As CoP only involves one simple matrix multiplication, it is
significantly faster than the state-of-the-art robust PCA algorithms. We derive
analytical performance guarantees for CoP under different models for the
distributions of inliers and outliers in both noise-free and noisy settings.
CoP is the first robust PCA algorithm that is simultaneously non-iterative,
provably robust to both unstructured and structured outliers, and can tolerate
a large number of unstructured outliers.
The Surfacing of Multiview 3D Drawings via Lofting and Occlusion Reasoning
Authors: Anil Usumezbas, Ricardo Fabbri, Benjamin Kimia
Date: 2017-07-13 00:00:00
Proceeding: CVPR 2017 7

The three-dimensional reconstruction of scenes from multiple views has made
impressive strides in recent years, chiefly by methods correlating isolated
feature points, intensities, or curvilinear structure. In the general setting,
i.e., without requiring controlled acquisition, limited number of objects,
abundant patterns on objects, or object curves to follow particular models, the
majority of these methods produce unorganized point clouds, meshes, or voxel
representations of the reconstructed scene, with some exceptions producing 3D
drawings as networks of curves. Many applications, e.g., robotics, urban
planning, industrial design, and hard surface modeling, however, require
structured representations which make explicit 3D curves, surfaces, and their
spatial relationships. Reconstructing surface representations can now be
constrained by the 3D drawing acting like a scaffold to hang on the computed
representations, leading to increased robustness and quality of reconstruction.
This paper presents one way of completing such 3D drawings with surface
reconstructions, by exploring occlusion reasoning through lofting algorithms.
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
Authors: M. K. Mudunuru, S. Karra, N. Makedonska, T. Chen
Date: 2016-06-14 00:00:00
Tasks: Clustering

Subsurface applications including geothermal, geological carbon
sequestration, oil and gas, etc., typically involve maximizing either the
extraction of energy or the storage of fluids. Characterizing the subsurface is
extremely complex due to heterogeneity and anisotropy. Due to this complexity,
there are uncertainties in the subsurface parameters, which need to be
estimated from multiple diverse as well as fragmented data streams. In this
paper, we present a non-intrusive sequential inversion framework, for
integrating data from geophysical and flow sources to constraint subsurface
Discrete Fracture Networks (DFN). In this approach, we first estimate bounds on
the statistics for the DFN fracture orientations using microseismic data. These
bounds are estimated through a combination of a focal mechanism (physics-based
approach) and clustering analysis (statistical approach) of seismic data. Then,
the fracture lengths are constrained based on the flow data. The efficacy of
this multi-physics based sequential inversion is demonstrated through a
representative synthetic example.
End-to-End Instance Segmentation with Recurrent Attention
Authors: Mengye Ren, Richard S. Zemel
Date: 2016-05-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Autonomous Driving, Image Captioning, Instance Segmentation, Object, Question Answering, Segmentation, Semantic Segmentation, Structured Prediction, Visual Question Answering, Visual Question Answering (VQA)

While convolutional neural networks have gained impressive success recently
in solving structured prediction problems such as semantic segmentation, it
remains a challenge to differentiate individual object instances in the scene.
Instance segmentation is very important in a variety of applications, such as
autonomous driving, image captioning, and visual question answering. Techniques
that combine large graphical models with low-level vision have been proposed to
address this problem; however, we propose an end-to-end recurrent neural
network (RNN) architecture with an attention mechanism to model a human-like
counting process, and produce detailed instance segmentations. The network is
jointly trained to sequentially produce regions of interest as well as a
dominant object segmentation within each region. The proposed model achieves
competitive results on the CVPPP, KITTI, and Cityscapes datasets.
Spatial Random Sampling: A Structure-Preserving Data Sketching Tool
Authors: Mostafa Rahmani, George Atia
Date: 2017-05-09 00:00:00
Tasks: Descriptive

Random column sampling is not guaranteed to yield data sketches that preserve
the underlying structures of the data and may not sample sufficiently from
less-populated data clusters. Also, adaptive sampling can often provide
accurate low rank approximations, yet may fall short of producing descriptive
data sketches, especially when the cluster centers are linearly dependent.
Motivated by that, this paper introduces a novel randomized column sampling
tool dubbed Spatial Random Sampling (SRS), in which data points are sampled
based on their proximity to randomly sampled points on the unit sphere. The
most compelling feature of SRS is that the corresponding probability of
sampling from a given data cluster is proportional to the surface area the
cluster occupies on the unit sphere, independently from the size of the cluster
population. Although it is fully randomized, SRS is shown to provide
descriptive and balanced data representations. The proposed idea addresses a
pressing need in data science and holds potential to inspire many novel
approaches for analysis of big data.
Explainable Entity-based Recommendations with Knowledge Graphs
Authors: Rose Catherine, Kathryn Mazaitis, Maxine Eskenazi, William Cohen
Date: 2017-07-12 00:00:00
Tasks: Explainable Recommendation, Knowledge Graphs

Explainable recommendation is an important task. Many methods have been
proposed which generate explanations from the content and reviews written for
items. When review text is unavailable, generating explanations is still a hard
problem. In this paper, we illustrate how explanations can be generated in such
a scenario by leveraging external knowledge in the form of knowledge graphs.
Our method jointly ranks items and knowledge graph entities using a
Personalized PageRank procedure to produce recommendations together with their
explanations.
Well-Founded Operators for Normal Hybrid MKNF Knowledge Bases
Authors: Jianmin Ji, Fangfang Liu, Jia-Huai You
Date: 2017-07-06 00:00:00

Hybrid MKNF knowledge bases have been considered one of the dominant
approaches to combining open world ontology languages with closed world
rule-based languages. Currently, the only known inference methods are based on
the approach of guess-and-verify, while most modern SAT/ASP solvers are built
under the DPLL architecture. The central impediment here is that it is not
clear what constitutes a constraint propagator, a key component employed in any
DPLL-based solver. In this paper, we address this problem by formulating the
notion of unfounded sets for nondisjunctive hybrid MKNF knowledge bases, based
on which we propose and study two new well-founded operators. We show that by
employing a well-founded operator as a constraint propagator, a sound and
complete DPLL search engine can be readily defined. We compare our approach
with the operator based on the alternating fixpoint construction by Knorr et al
[2011] and show that, when applied to arbitrary partial partitions, the new
well-founded operators not only propagate more truth values but also circumvent
the non-converging behavior of the latter. In addition, we study the
possibility of simplifying a given hybrid MKNF knowledge base by employing a
well-founded operator, and show that, out of the two operators proposed in this
paper, the weaker one can be applied for this purpose and the stronger one
cannot. These observations are useful in implementing a grounder for hybrid
MKNF knowledge bases, which can be applied before the computation of MKNF
models.
  The paper is under consideration for acceptance in TPLP.
Enhanced Particle Swarm Optimization Algorithms for Multiple-Input Multiple-Output System Modelling using Convolved Gaussian Process Models
Authors: Gang Cao, Edmund M-K Lai, Fakhrul Alam
Date: 2017-07-12 00:00:00

Convolved Gaussian Process (CGP) is able to capture the correlations not only
between inputs and outputs but also among the outputs. This allows a superior
performance of using CGP than standard Gaussian Process (GP) in the modelling
of Multiple-Input Multiple-Output (MIMO) systems when observations are missing
for some of outputs. Similar to standard GP, a key issue of CGP is the learning
of hyperparameters from a set of input-output observations. It typically
performed by maximizing the Log-Likelihood (LL) function which leads to an
unconstrained nonlinear and non-convex optimization problem. Algorithms such as
Conjugate Gradient (CG) or Broyden-Fletcher-Goldfarb-Shanno (BFGS) are commonly
used but they often get stuck in local optima, especially for CGP where there
are more hyperparameters. In addition, the LL value is not a reliable indicator
for judging the quality intermediate models in the optimization process. In
this paper, we propose to use enhanced Particle Swarm Optimization (PSO)
algorithms to solve this problem by minimizing the model output error instead.
This optimization criterion enables the quality of intermediate solutions to be
directly observable during the optimization process. Two enhancements to the
standard PSO algorithm which make use of gradient information and the multi-
start technique are proposed. Simulation results on the modelling of both
linear and nonlinear systems demonstrate the effectiveness of minimizing the
model output error to learn hyperparameters and the performance of using
enhanced algorithms.
Density Level Set Estimation on Manifolds with DBSCAN
Authors: Heinrich Jiang
Date: 2017-03-10 00:00:00
Proceeding: ICML 2017 8

We show that DBSCAN can estimate the connected components of the
$\lambda$-density level set $\{ x : f(x) \ge \lambda\}$ given $n$ i.i.d.
samples from an unknown density $f$. We characterize the regularity of the
level set boundaries using parameter $\beta > 0$ and analyze the estimation
error under the Hausdorff metric. When the data lies in $\mathbb{R}^D$ we
obtain a rate of $\widetilde{O}(n^{-1/(2\beta + D)})$, which matches known
lower bounds up to logarithmic factors. When the data lies on an embedded
unknown $d$-dimensional manifold in $\mathbb{R}^D$, then we obtain a rate of
$\widetilde{O}(n^{-1/(2\beta + d\cdot \max\{1, \beta \})})$. Finally, we
provide adaptive parameter tuning in order to attain these rates with no a
priori knowledge of the intrinsic dimension, density, or $\beta$.
Large Scale Variable Fidelity Surrogate Modeling
Authors: Evgeny Burnaev, Alexey Zaytsev
Date: 2017-07-12 00:00:00
Tasks: regression

Engineers widely use Gaussian process regression framework to construct
surrogate models aimed to replace computationally expensive physical models
while exploring design space. Thanks to Gaussian process properties we can use
both samples generated by a high fidelity function (an expensive and accurate
representation of a physical phenomenon) and a low fidelity function (a cheap
and coarse approximation of the same physical phenomenon) while constructing a
surrogate model. However, if samples sizes are more than few thousands of
points, computational costs of the Gaussian process regression become
prohibitive both in case of learning and in case of prediction calculation. We
propose two approaches to circumvent this computational burden: one approach is
based on the Nystr\"om approximation of sample covariance matrices and another
is based on an intelligent usage of a blackbox that can evaluate a~low fidelity
function on the fly at any point of a design space. We examine performance of
the proposed approaches using a number of artificial and real problems,
including engineering optimization of a rotating disk shape.
Model Selection for Anomaly Detection
Authors: Evgeny Burnaev, Pavel Erofeev, Dmitry Smolyakov
Date: 2017-07-12 00:00:00
Tasks: Anomaly Detection, Classification, General Classification, Intrusion Detection, model, Model Selection, Network Intrusion Detection, One-Class Classification

Anomaly detection based on one-class classification algorithms is broadly
used in many applied domains like image processing (e.g. detection of whether a
patient is "cancerous" or "healthy" from mammography image), network intrusion
detection, etc. Performance of an anomaly detection algorithm crucially depends
on a kernel, used to measure similarity in a feature space. The standard
approaches (e.g. cross-validation) for kernel selection, used in two-class
classification problems, can not be used directly due to the specific nature of
a data (absence of a second, abnormal, class data). In this paper we generalize
several kernel selection methods from binary-class case to the case of
one-class classification and perform extensive comparison of these approaches
using both synthetic and real-world data.
Deep Over-sampling Framework for Classifying Imbalanced Data
Authors: Shin Ando, Chun-Yuan Huang
Date: 2017-04-25 00:00:00
Tasks: Representation Learning

Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings.
Automatic Mapping of NES Games with Mappy
Authors: Joseph C. Osborn, Adam Summerville, Michael Mateas
Date: 2017-07-12 00:00:00

Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.
Influence of Resampling on Accuracy of Imbalanced Classification
Authors: Evgeny Burnaev, Pavel Erofeev, Artem Papanov
Date: 2017-07-12 00:00:00
Tasks: Binary Classification, Classification, General Classification, imbalanced classification

In many real-world binary classification tasks (e.g. detection of certain
objects from images), an available dataset is imbalanced, i.e., it has much
less representatives of a one class (a minor class), than of another.
Generally, accurate prediction of the minor class is crucial but it's hard to
achieve since there is not much information about the minor class. One approach
to deal with this problem is to preliminarily resample the dataset, i.e., add
new elements to the dataset or remove existing ones. Resampling can be done in
various ways which raises the problem of choosing the most appropriate one. In
this paper we experimentally investigate impact of resampling on classification
accuracy, compare resampling methods and highlight key points and difficulties
of resampling.
Autoencoder-augmented Neuroevolution for Visual Doom Playing
Authors: Samuel Alvernaz, Julian Togelius
Date: 2017-07-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Neuroevolution has proven effective at many reinforcement learning tasks, but
does not seem to scale well to high-dimensional controller representations,
which are needed for tasks where the input is raw pixel data. We propose a
novel method where we train an autoencoder to create a comparatively
low-dimensional representation of the environment observation, and then use
CMA-ES to train neural network controllers acting on this input data. As the
behavior of the agent changes the nature of the input data, the autoencoder
training progresses throughout evolution. We test this method in the VizDoom
environment built on the classic FPS Doom, where it performs well on a
health-pack gathering task.
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks
Authors: Swapna Buccapatnam, Fang Liu, Atilla Eryilmaz, Ness B. Shroff
Date: 2017-04-26 00:00:00

We study the stochastic multi-armed bandit (MAB) problem in the presence of
side-observations across actions that occur as a result of an underlying
network structure. In our model, a bipartite graph captures the relationship
between actions and a common set of unknowns such that choosing an action
reveals observations for the unknowns that it is connected to. This models a
common scenario in online social networks where users respond to their friends'
activity, thus providing side information about each other's preferences. Our
contributions are as follows: 1) We derive an asymptotic lower bound (with
respect to time) as a function of the bi-partite network structure on the
regret of any uniformly good policy that achieves the maximum long-term average
reward. 2) We propose two policies - a randomized policy; and a policy based on
the well-known upper confidence bound (UCB) policies - both of which explore
each action at a rate that is a function of its network position. We show,
under mild assumptions, that these policies achieve the asymptotic lower bound
on the regret up to a multiplicative factor, independent of the network
structure. Finally, we use numerical examples on a real-world social network
and a routing example network to demonstrate the benefits obtained by our
policies over other existing policies.
Backpropagation in matrix notation
Authors: N. M. Mishachev
Date: 2017-07-10 00:00:00

In this note we calculate the gradient of the network function in matrix
notation.
A Formal Framework to Characterize Interpretability of Procedures
Authors: Amit Dhurandhar, Vijay Iyengar, Ronny Luss, Karthikeyan Shanmugam
Date: 2017-07-12 00:00:00

We provide a novel notion of what it means to be interpretable, looking past
the usual association with human understanding. Our key insight is that
interpretability is not an absolute concept and so we define it relative to a
target model, which may or may not be a human. We define a framework that
allows for comparing interpretable procedures by linking it to important
practical aspects such as accuracy and robustness. We characterize many of the
current state-of-the-art interpretable methods in our framework portraying its
general applicability.
Identification and Interpretation of Belief Structure in Dempster-Shafer Theory
Authors: Mieczysław A. Kłopotek
Date: 2017-07-12 00:00:00

Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is
known as a foundation for reasoning when knowledge is expressed at various
levels of detail. Though much research effort has been committed to this theory
since its foundation, many questions remain open. One of the most important
open questions seems to be the relationship between frequencies and the
Mathematical Theory of Evidence. The theory is blamed to leave frequencies
outside (or aside of) its framework. The seriousness of this accusation is
obvious: (1) no experiment may be run to compare the performance of DST-based
models of real world processes against real world data, (2) data may not serve
as foundation for construction of an appropriate belief model.
  In this paper we develop a frequentist interpretation of the DST bringing to
fall the above argument against DST. An immediate consequence of it is the
possibility to develop algorithms acquiring automatically DST belief models
from data. We propose three such algorithms for various classes of belief model
structures: for tree structured belief networks, for poly-tree belief networks
and for general type belief networks.