pwc-archive/papers-with-abstracts huggingface dataset:
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
Authors: Seyed Mehran Kazemi, David Poole
Date: 2016-06-14 00:00:00

First-order knowledge compilation techniques have proven efficient for lifted
inference. They compile a relational probability model into a target circuit on
which many inference queries can be answered efficiently. Early methods used
data structures as their target circuit. In our KR-2016 paper, we showed that
compiling to a low-level program instead of a data structure offers orders of
magnitude speedup, resulting in the state-of-the-art lifted inference
technique. In this paper, we conduct experiments to address two questions
regarding our KR-2016 results: 1- does the speedup come from more efficient
compilation or more efficient reasoning with the target circuit?, and 2- why
are low-level programs more efficient target circuits than data structures?
Sparse Multinomial Logistic Regression via Approximate Message Passing
Authors: Evan Byrne, Philip Schniter
Date: 2015-09-15 00:00:00
Tasks: feature selection, General Classification, regression

For the problem of multi-class linear classification and feature selection,
we propose approximate message passing approaches to sparse multinomial
logistic regression (MLR). First, we propose two algorithms based on the Hybrid
Generalized Approximate Message Passing (HyGAMP) framework: one finds the
maximum a posteriori (MAP) linear classifier and the other finds an
approximation of the test-error-rate minimizing linear classifier. Then we
design computationally simplified variants of these two algorithms. Next, we
detail methods to tune the hyperparameters of their assumed statistical models
using Stein's unbiased risk estimate (SURE) and expectation-maximization (EM),
respectively. Finally, using both synthetic and real-world datasets, we
demonstrate improved error-rate and runtime performance relative to existing
state-of-the-art approaches to sparse MLR.
Max-Margin Feature Selection
Authors: Yamuna Prasad, Dinesh Khandelwal, K. K. Biswas
Date: 2016-06-14 00:00:00
Tasks: feature selection

Many machine learning applications such as in vision, biology and social
networking deal with data in high dimensions. Feature selection is typically
employed to select a subset of features which im- proves generalization
accuracy as well as reduces the computational cost of learning the model. One
of the criteria used for feature selection is to jointly minimize the
redundancy and maximize the rele- vance of the selected features. In this
paper, we formulate the task of feature selection as a one class SVM problem in
a space where features correspond to the data points and instances correspond
to the dimensions. The goal is to look for a representative subset of the
features (support vectors) which describes the boundary for the region where
the set of the features (data points) exists. This leads to a joint
optimization of relevance and redundancy in a principled max-margin framework.
Additionally, our formulation enables us to leverage existing techniques for
optimizing the SVM objective resulting in highly computationally efficient
solutions for the task of feature selection. Specifically, we employ the dual
coordinate descent algorithm (Hsieh et al., 2008), originally proposed for
SVMs, for our formulation. We use a sparse representation to deal with data in
very high dimensions. Experiments on seven publicly available benchmark
datasets from a variety of domains show that our approach results in orders of
magnitude faster solutions even while retaining the same level of accuracy
compared to the state of the art feature selection techniques.
Shallow Discourse Parsing Using Distributed Argument Representations and Bayesian Optimization
Authors: Akanksha, Jacob Eisenstein
Date: 2016-06-14 00:00:00
Tasks: Bayesian Optimization, Discourse Parsing, General Classification, Relation

This paper describes the Georgia Tech team's approach to the CoNLL-2016
supplementary evaluation on discourse relation sense classification. We use
long short-term memories (LSTM) to induce distributed representations of each
argument, and then combine these representations with surface features in a
neural network. The architecture of the neural network is determined by
Bayesian hyperparameter search.
Discontinuous Piecewise Polynomial Neural Networks
Authors: John Loverich
Date: 2015-05-15 00:00:00
Tasks: Polynomial Neural Networks

An artificial neural network is presented based on the idea of connections
between units that are only active for a specific range of input values and
zero outside that range (and so are not evaluated outside the active range).
The connection function is represented by a polynomial with compact support.
The finite range of activation allows for great activation sparsity in the
network and means that theoretically you are able to add computational power to
the network without increasing the computational time required to evaluate the
network for a given input. The polynomial order ranges from first to fifth
order. Unit dropout is used for regularization and a parameter free weight
update is used. Better performance is obtained by moving from piecewise linear
connections to piecewise quadratic, even better performance can be obtained by
moving to higher order polynomials. The algorithm is tested on the MAGIC Gamma
ray data set as well as the MNIST data set.
The Symbolic Interior Point Method
Authors: Martin Mladenov, Vaishak Belle, Kristian Kersting
Date: 2016-05-26 00:00:00
Tasks: compressed sensing, Decision Making, Descriptive

A recent trend in probabilistic inference emphasizes the codification of
models in a formal syntax, with suitable high-level features such as
individuals, relations, and connectives, enabling descriptive clarity,
succinctness and circumventing the need for the modeler to engineer a custom
solver. Unfortunately, bringing these linguistic and pragmatic benefits to
numerical optimization has proven surprisingly challenging. In this paper, we
turn to these challenges: we introduce a rich modeling language, for which an
interior-point method computes approximate solutions in a generic way. While
logical features easily complicates the underlying model, often yielding
intricate dependencies, we exploit and cache local structure using algebraic
decision diagrams (ADDs). Indeed, standard matrix-vector algebra is efficiently
realizable in ADDs, but we argue and show that well-known optimization methods
are not ideal for ADDs. Our engine, therefore, invokes a sophisticated
matrix-free approach. We demonstrate the flexibility of the resulting
symbolic-numeric optimizer on decision making and compressed sensing tasks with
millions of non-zero entries.
Lifted Convex Quadratic Programming
Authors: Martin Mladenov, Leonard Kleinhans, Kristian Kersting
Date: 2016-06-14 00:00:00

Symmetry is the essential element of lifted inference that has recently
demon- strated the possibility to perform very efficient inference in
highly-connected, but symmetric probabilistic models models. This raises the
question, whether this holds for optimisation problems in general. Here we show
that for a large class of optimisation methods this is actually the case. More
precisely, we introduce the concept of fractional symmetries of convex
quadratic programs (QPs), which lie at the heart of many machine learning
approaches, and exploit it to lift, i.e., to compress QPs. These lifted QPs can
then be tackled with the usual optimization toolbox (off-the-shelf solvers,
cutting plane algorithms, stochastic gradients etc.). If the original QP
exhibits symmetry, then the lifted one will generally be more compact, and
hence their optimization is likely to be more efficient.
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction
Authors: Andrew Holbrook, Alexander Vandenberg-Rodes, Babak Shahbaba
Date: 2016-06-14 00:00:00
Tasks: Bayesian Inference, Dimensionality Reduction

We reframe linear dimensionality reduction as a problem of Bayesian inference
on matrix manifolds. This natural paradigm extends the Bayesian framework to
dimensionality reduction tasks in higher dimensions with simpler models at
greater speeds. Here an orthogonal basis is treated as a single point on a
manifold and is associated with a linear subspace on which observations vary
maximally. Throughout this paper, we employ the Grassmann and Stiefel manifolds
for various dimensionality reduction problems, explore the connection between
the two manifolds, and use Hybrid Monte Carlo for posterior sampling on the
Grassmannian for the first time. We delineate in which situations either
manifold should be considered. Further, matrix manifold models are used to
yield scientific insight in the context of cognitive neuroscience, and we
conclude that our methods are suitable for basic inference as well as accurate
prediction.
Efficient adaptation of complex-valued noiselet sensing matrices for compressed single-pixel imaging
Authors: Anna Pastuszczak, Bartłomiej Szczygieł, Michał Mikołajczyk, Rafał Kotyński
Date: 2016-06-14 00:00:00
Tasks: compressed sensing

Minimal mutual coherence of discrete noiselets and Haar wavelets makes this
pair of bases an essential choice for the measurement and compression matrices
in compressed-sensing-based single-pixel detectors. In this paper we propose an
efficient way of using complex-valued and non-binary noiselet functions for
object sampling in single-pixel cameras with binary spatial light modulators
and incoherent illumination. The proposed method allows to determine m complex
noiselet coefficients from m+1 binary sampling measurements. Further, we
introduce a modification to the complex fast noiselet transform, which enables
computationally-efficient real-time generation of the binary noiselet-based
patterns using efficient integer calculations on bundled patterns. The proposed
method is verified experimentally with a single-pixel camera system using a
binary spatial light modulator.
Neural Networks and Continuous Time
Authors: Frieder Stolzenburg, Florian Ruh
Date: 2016-06-14 00:00:00
Tasks: General Classification, Logical Reasoning

The fields of neural computation and artificial neural networks have
developed much in the last decades. Most of the works in these fields focus on
implementing and/or learning discrete functions or behavior. However,
technical, physical, and also cognitive processes evolve continuously in time.
This cannot be described directly with standard architectures of artificial
neural networks such as multi-layer feed-forward perceptrons. Therefore, in
this paper, we will argue that neural networks modeling continuous time are
needed explicitly for this purpose, because with them the synthesis and
analysis of continuous and possibly periodic processes in time are possible
(e.g. for robot behavior) besides computing discrete classification functions
(e.g. for logical reasoning). We will relate possible neural network
architectures with (hybrid) automata models that allow to express continuous
processes.
Model-Free Episodic Control
Authors: Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack Rae, Daan Wierstra, Demis Hassabis
Date: 2016-06-14 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Hippocampus, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

State of the art deep reinforcement learning algorithms take many millions of
interactions to attain human-level performance. Humans, on the other hand, can
very quickly exploit highly rewarding nuances of an environment upon first
discovery. In the brain, such rapid learning is thought to depend on the
hippocampus and its capacity for episodic memory. Here we investigate whether a
simple model of hippocampal episodic control can learn to solve difficult
sequential decision-making tasks. We demonstrate that it not only attains a
highly rewarding strategy significantly faster than state-of-the-art deep
reinforcement learning algorithms, but also achieves a higher overall reward on
some of the more challenging domains.
Multiple Human Tracking in RGB-D Data: A Survey
Authors: Massimo Camplani, Adeline Paiement, Majid Mirmehdi, Dima Damen, Sion Hannuna, Tilo Burghardt, Lili Tao
Date: 2016-06-14 00:00:00
Tasks: Survey

Multiple human tracking (MHT) is a fundamental task in many computer vision
applications. Appearance-based approaches, primarily formulated on RGB data,
are constrained and affected by problems arising from occlusions and/or
illumination variations. In recent years, the arrival of cheap RGB-Depth
(RGB-D) devices has {led} to many new approaches to MHT, and many of these
integrate color and depth cues to improve each and every stage of the process.
In this survey, we present the common processing pipeline of these methods and
review their methodology based (a) on how they implement this pipeline and (b)
on what role depth plays within each stage of it. We identify and introduce
existing, publicly available, benchmark datasets and software resources that
fuse color and depth data for MHT. Finally, we present a brief comparative
evaluation of the performance of those works that have applied their methods to
these datasets.
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization
Authors: Spyros Gidaris, Nikos Komodakis
Date: 2016-06-14 00:00:00
Tasks: object-detection, Object Detection

The problem of computing category agnostic bounding box proposals is utilized
as a core component in many computer vision tasks and thus has lately attracted
a lot of attention. In this work we propose a new approach to tackle this
problem that is based on an active strategy for generating box proposals that
starts from a set of seed boxes, which are uniformly distributed on the image,
and then progressively moves its attention on the promising image areas where
it is more likely to discover well localized bounding box proposals. We call
our approach AttractioNet and a core component of it is a CNN-based category
agnostic object location refinement module that is capable of yielding accurate
and robust bounding box predictions regardless of the object category.
  We extensively evaluate our AttractioNet approach on several image datasets
(i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on
all of them state-of-the-art results that surpass the previous work in the
field by a significant margin and also providing strong empirical evidence that
our approach is capable to generalize to unseen categories. Furthermore, we
evaluate our AttractioNet proposals in the context of the object detection task
using a VGG16-Net based detector and the achieved detection performance on COCO
manages to significantly surpass all other VGG16-Net based detectors while even
being competitive with a heavily tuned ResNet-101 based detector. Code as well
as box proposals computed for several datasets are available at::
https://github.com/gidariss/AttractioNet.
Automatic learning of gait signatures for people identification
Authors: F. M. Castro, M. J. Marin-Jimenez, N. Guil, N. Perez de la Blanca
Date: 2016-03-03 00:00:00
Tasks: Optical Flow Estimation

This work targets people identification in video based on the way they walk
(i.e. gait). While classical methods typically derive gait signatures from
sequences of binary silhouettes, in this work we explore the use of
convolutional neural networks (CNN) for learning high-level descriptors from
low-level motion features (i.e. optical flow components). We carry out a
thorough experimental evaluation of the proposed CNN architecture on the
challenging TUM-GAID dataset. The experimental results indicate that using
spatio-temporal cuboids of optical flow as input data for CNN allows to obtain
state-of-the-art results on the gait task with an image resolution eight times
lower than the previously reported results (i.e. 80x60 pixels).
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks
Authors: Aren Jansen, Gregory Sell, Vince Lyzinski
Date: 2015-08-18 00:00:00
Tasks: Dimensionality Reduction, Graph Embedding, Model Selection, Representation Learning

Several popular graph embedding techniques for representation learning and
dimensionality reduction rely on performing computationally expensive
eigendecompositions to derive a nonlinear transformation of the input data
space. The resulting eigenvectors encode the embedding coordinates for the
training samples only, and so the embedding of novel data samples requires
further costly computation. In this paper, we present a method for the
out-of-sample extension of graph embeddings using deep neural networks (DNN) to
parametrically approximate these nonlinear maps. Compared with traditional
nonparametric out-of-sample extension methods, we demonstrate that the DNNs can
generalize with equal or better fidelity and require orders of magnitude less
computation at test time. Moreover, we find that unsupervised pretraining of
the DNNs improves optimization for larger network sizes, thus removing
sensitivity to model selection.
Simple epistemic planning: generalised gossiping
Authors: Martin C. Cooper, Andreas Herzig, Faustine Maffre, Frédéric Maris, Pierre Régnier
Date: 2016-06-10 00:00:00
Tasks: All

The gossip problem, in which information (known as secrets) must be shared
among a certain number of agents using the minimum number of calls, is of
interest in the conception of communication networks and protocols. We extend
the gossip problem to arbitrary epistemic depths. For example, we may require
not only that all agents know all secrets but also that all agents know that
all agents know all secrets. We give optimal protocols for various versions of
the generalised gossip problem, depending on the graph of communication links,
in the case of two-way communications, one-way communications and parallel
communication. We also study different variants which allow us to impose
negative goals such as that certain agents must not know certain secrets. We
show that in the presence of negative goals testing the existence of a
successful protocol is NP-complete whereas this is always polynomial-time in
the case of purely positive goals.
Using Fuzzy Logic to Leverage HTML Markup for Web Page Representation
Authors: Alberto P. García-Plaza, Víctor Fresno, Raquel Martínez, Arkaitz Zubiaga
Date: 2016-06-14 00:00:00
Tasks: Clustering

The selection of a suitable document representation approach plays a crucial
role in the performance of a document clustering task. Being able to pick out
representative words within a document can lead to substantial improvements in
document clustering. In the case of web documents, the HTML markup that defines
the layout of the content provides additional structural information that can
be further exploited to identify representative words. In this paper we
introduce a fuzzy term weighing approach that makes the most of the HTML
structure for document clustering. We set forth and build on the hypothesis
that a good representation can take advantage of how humans skim through
documents to extract the most representative words. The authors of web pages
make use of HTML tags to convey the most important message of a web page
through page elements that attract the readers' attention, such as page titles
or emphasized elements. We define a set of criteria to exploit the information
provided by these page elements, and introduce a fuzzy combination of these
criteria that we evaluate within the context of a web page clustering task. Our
proposed approach, called Abstract Fuzzy Combination of Criteria (AFCC), can
adapt to datasets whose features are distributed differently, achieving good
results compared to other similar fuzzy logic based approaches and TF-IDF
across different datasets.
Dissociation and Propagation for Approximate Lifted Inference with Standard Relational Database Management Systems
Authors: Wolfgang Gatterbauer, Dan Suciu
Date: 2013-10-23 00:00:00
Tasks: Management

Probabilistic inference over large data sets is a challenging data management
problem since exact inference is generally #P-hard and is most often solved
approximately with sampling-based methods today. This paper proposes an
alternative approach for approximate evaluation of conjunctive queries with
standard relational databases: In our approach, every query is evaluated
entirely in the database engine by evaluating a fixed number of query plans,
each providing an upper bound on the true probability, then taking their
minimum. We provide an algorithm that takes into account important schema
information to enumerate only the minimal necessary plans among all possible
plans. Importantly, this algorithm is a strict generalization of all known
PTIME self-join-free conjunctive queries: A query is in PTIME if and only if
our algorithm returns one single plan. Furthermore, our approach is a
generalization of a family of efficient ranking methods from graphs to
hypergraphs. We also adapt three relational query optimization techniques to
evaluate all necessary plans very fast. We give a detailed experimental
evaluation of our approach and, in the process, provide a new way of thinking
about the value of probabilistic methods over non-probabilistic methods for
ranking query answers. We also note that the techniques developed in this paper
apply immediately to lifted inference from statistical relational models since
lifted inference corresponds to PTIME plans in probabilistic databases.
Relating Strong Spatial Cognition to Symbolic Problem Solving --- An Example
Authors: Ulrich Furbach, Florian Furbach, Christian Freksa
Date: 2016-06-14 00:00:00

In this note, we discuss and analyse a shortest path finding approach using
strong spatial cognition. It is compared with a symbolic graph-based algorithm
and it is shown that both approaches are similar with respect to structure and
complexity. Nevertheless, the strong spatial cognition solution is easy to
understand and even pops up immediately when one has to solve the problem.
Sifting Robotic from Organic Text: A Natural Language Approach for Detecting Automation on Twitter
Authors: Eric M. Clark, Jake Ryland Williams, Chris A. Jones, Richard A. Galbraith, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2015-05-17 00:00:00

Twitter, a popular social media outlet, has evolved into a vast source of
linguistic data, rich with opinion, sentiment, and discussion. Due to the
increasing popularity of Twitter, its perceived potential for exerting social
influence has led to the rise of a diverse community of automatons, commonly
referred to as bots. These inorganic and semi-organic Twitter entities can
range from the benevolent (e.g., weather-update bots, help-wanted-alert bots)
to the malevolent (e.g., spamming messages, advertisements, or radical
opinions). Existing detection algorithms typically leverage meta-data (time
between tweets, number of followers, etc.) to identify robotic accounts. Here,
we present a powerful classification scheme that exclusively uses the natural
language text from organic users to provide a criterion for identifying
accounts posting automated messages. Since the classifier operates on text
alone, it is flexible and may be applied to any textual data beyond the
Twitter-sphere.