pwc-archive/papers-with-abstracts huggingface dataset:
Exploring high-level Perspectives on Self-Configuration Capabilities of Systems
Authors: Aleksander Lodwich
Date: 2016-06-28 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Optimization of product performance repetitively introduces the need to make
products adaptive in a more general sense. This more general idea is often
captured under the term 'self-configuration'. Despite the importance of such
capability, research work on this feature appears isolated by technical
domains. It is not easy to tell quickly whether the approaches chosen in
different technological domains introduce new ideas or whether the differences
just reflect domain idiosyncrasies. For the sake of easy identification of key
differences between systems with self-configuring capabilities, I will explore
higher level concepts for understanding self-configuration, such as the
{\Omega}-units, in order to provide theoretical instruments for connecting
different areas of technology and research.
On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic
Authors: Joohyung Lee, Yi Wang
Date: 2016-06-28 00:00:00
Tasks: Relational Reasoning

Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widely
applied formalisms in Statistical Relational Learning, an emerging area in
Artificial Intelligence that is concerned with combining logical and
statistical AI. Despite their resemblance, the relationship has not been
formally stated. In this paper, we describe the precise semantic relationship
between them from a logical perspective. This is facilitated by first extending
fuzzy logic to allow weights, which can be also viewed as a generalization of
PSL, and then relate that generalization to MLN. We observe that the
relationship between PSL and MLN is analogous to the known relationship between
fuzzy logic and Boolean logic, and furthermore the weight scheme of PSL is
essentially a generalization of the weight scheme of MLN for the many-valued
setting.
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
Authors: Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney
Date: 2016-01-22 00:00:00
Tasks: Sparse Learning

This paper considers the problem of high dimensional signal detection in a
large distributed network whose nodes can collaborate with their one-hop
neighboring nodes (spatial collaboration). We assume that only a small subset
of nodes communicate with the Fusion Center (FC). We design optimal
collaboration strategies which are universal for a class of deterministic
signals. By establishing the equivalence between the collaboration strategy
design problem and sparse PCA, we solve the problem efficiently and evaluate
the impact of collaboration on detection performance.
Defending Non-Bayesian Learning against Adversarial Attacks
Authors: Lili Su, Nitin H. Vaidya
Date: 2016-06-28 00:00:00

This paper addresses the problem of non-Bayesian learning over multi-agent
networks, where agents repeatedly collect partially informative observations
about an unknown state of the world, and try to collaboratively learn the true
state. We focus on the impact of the adversarial agents on the performance of
consensus-based non-Bayesian learning, where non-faulty agents combine local
learning updates with consensus primitives. In particular, we consider the
scenario where an unknown subset of agents suffer Byzantine faults -- agents
suffering Byzantine faults behave arbitrarily. Two different learning rules are
proposed.
Tracking Switched Dynamic Network Topologies from Information Cascades
Authors: Brian Baingana, Georgios B. Giannakis
Date: 2016-06-28 00:00:00

Contagions such as the spread of popular news stories, or infectious
diseases, propagate in cascades over dynamic networks with unobservable
topologies. However, "social signals" such as product purchase time, or blog
entry timestamps are measurable, and implicitly depend on the underlying
topology, making it possible to track it over time. Interestingly, network
topologies often "jump" between discrete states that may account for sudden
changes in the observed signals. The present paper advocates a switched dynamic
structural equation model to capture the topology-dependent cascade evolution,
as well as the discrete states driving the underlying topologies. Conditions
under which the proposed switched model is identifiable are established.
Leveraging the edge sparsity inherent to social networks, a recursive
$\ell_1$-norm regularized least-squares estimator is put forth to jointly track
the states and network topologies. An efficient first-order proximal-gradient
algorithm is developed to solve the resulting optimization problem. Numerical
experiments on both synthetic data and real cascades measured over the span of
one year are conducted, and test results corroborate the efficacy of the
advocated approach.
Consciousness is Pattern Recognition
Authors: Ray Van De Walker
Date: 2016-05-04 00:00:00

This is a proof of the strong AI hypothesis, i.e. that machines can be
conscious. It is a phenomenological proof that pattern-recognition and
subjective consciousness are the same activity in different terms. Therefore,
it proves that essential subjective processes of consciousness are computable,
and identifies significant traits and requirements of a conscious system. Since
Husserl, many philosophers have accepted that consciousness consists of
memories of logical connections between an ego and external objects. These
connections are called "intentions." Pattern recognition systems are achievable
technical artifacts. The proof links this respected introspective philosophical
theory of consciousness with technical art. The proof therefore endorses the
strong AI hypothesis and may therefore also enable a theoretically-grounded
form of artificial intelligence called a "synthetic intentionality," able to
synthesize, generalize, select and repeat intentions. If the pattern
recognition is reflexive, able to operate on the set of intentions, and
flexible, with several methods of synthesizing intentions, an SI may be a
particularly strong form of AI. Similarities and possible applications to
several AI paradigms are discussed. The article then addresses some problems:
The proof's limitations, reflexive cognition, Searles' Chinese room, and how an
SI could "understand" "meanings" and "be creative."
Technical Report: Towards a Universal Code Formatter through Machine Learning
Authors: Terence Parr, Jurgin Vinju
Date: 2016-06-28 00:00:00
Tasks: BIG-bench Machine Learning

There are many declarative frameworks that allow us to implement code
formatters relatively easily for any specific language, but constructing them
is cumbersome. The first problem is that "everybody" wants to format their code
differently, leading to either many formatter variants or a ridiculous number
of configuration options. Second, the size of each implementation scales with a
language's grammar size, leading to hundreds of rules.
  In this paper, we solve the formatter construction problem using a novel
approach, one that automatically derives formatters for any given language
without intervention from a language expert. We introduce a code formatter
called CodeBuff that uses machine learning to abstract formatting rules from a
representative corpus, using a carefully designed feature set. Our experiments
on Java, SQL, and ANTLR grammars show that CodeBuff is efficient, has excellent
accuracy, and is grammar invariant for a given language. It also generalizes to
a 4th language tested during manuscript preparation.
Generation and Pruning of Pronunciation Variants to Improve ASR Accuracy
Authors: Zhenhao Ge, Aravind Ganapathiraju, Ananth N. Iyer, Scott A. Randal, Felix I. Wyss
Date: 2016-06-28 00:00:00
Tasks: speech-recognition, Speech Recognition

Speech recognition, especially name recognition, is widely used in phone
services such as company directory dialers, stock quote providers or location
finders. It is usually challenging due to pronunciation variations. This paper
proposes an efficient and robust data-driven technique which automatically
learns acceptable word pronunciations and updates the pronunciation dictionary
to build a better lexicon without affecting recognition of other words similar
to the target word. It generalizes well on datasets with various sizes, and
reduces the error rate on a database with 13000+ human names by 42%, compared
to a baseline with regular dictionaries already covering canonical
pronunciations of 97%+ words in names, plus a well-trained
spelling-to-pronunciation (STP) engine.
Unsupervised Learning from Narrated Instruction Videos
Authors: Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, Simon Lacoste-Julien
Date: 2015-06-30 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering

We address the problem of automatically learning the main steps to complete a
certain task, such as changing a car tire, from a set of narrated instruction
videos. The contributions of this paper are three-fold. First, we develop a new
unsupervised learning approach that takes advantage of the complementary nature
of the input video and the associated narration. The method solves two
clustering problems, one in text and one in video, applied one after each other
and linked by joint constraints to obtain a single coherent sequence of steps
in both modalities. Second, we collect and annotate a new challenging dataset
of real-world instruction videos from the Internet. The dataset contains about
800,000 frames for five different tasks that include complex interactions
between people and objects, and are captured in a variety of indoor and outdoor
settings. Third, we experimentally demonstrate that the proposed method can
automatically discover, in an unsupervised manner, the main steps to achieve
the task and locate the steps in the input videos.
Multi-View Kernel Consensus For Data Analysis
Authors: Moshe Salhov, Ofir Lindenbaum, Yariv Aizenbud, Avi Silberschatz, Yoel Shkolnisky, Amir Averbuch
Date: 2016-06-28 00:00:00
Tasks: Attribute

The input data features set for many data driven tasks is high-dimensional
while the intrinsic dimension of the data is low. Data analysis methods aim to
uncover the underlying low dimensional structure imposed by the low dimensional
hidden parameters by utilizing distance metrics that consider the set of
attributes as a single monolithic set. However, the transformation of the low
dimensional phenomena into the measured high dimensional observations might
distort the distance metric, This distortion can effect the desired estimated
low dimensional geometric structure. In this paper, we suggest to utilize the
redundancy in the attribute domain by partitioning the attributes into multiple
subsets we call views. The proposed methods utilize the agreement also called
consensus between different views to extract valuable geometric information
that unifies multiple views about the intrinsic relationships among several
different observations. This unification enhances the information that a single
view or a simple concatenations of views provides.
Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo Theories
Authors: Przemysław Andrzej Wałęga, Carl Schultz, Mehul Bhatt
Date: 2016-06-25 00:00:00
Tasks: Spatial Reasoning

The systematic modelling of dynamic spatial systems is a key requirement in a
wide range of application areas such as commonsense cognitive robotics,
computer-aided architecture design, and dynamic geographic information systems.
We present ASPMT(QS), a novel approach and fully-implemented prototype for
non-monotonic spatial reasoning -a crucial requirement within dynamic spatial
systems- based on Answer Set Programming Modulo Theories (ASPMT).
  ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and
a method for turning tight ASPMT instances into Satisfiability Modulo Theories
(SMT) instances in order to compute stable models by means of SMT solvers. We
formalise and implement concepts of default spatial reasoning and spatial frame
axioms. Spatial reasoning is performed by encoding spatial relations as systems
of polynomial constraints, and solving via SMT with the theory of real
nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with
other contemporary spatial reasoning systems both within and outside the
context of logic programming. ASPMT(QS) is currently the only existing system
that is capable of reasoning about indirect spatial effects (i.e., addressing
the ramification problem), and integrating geometric and qualitative spatial
information within a non-monotonic spatial reasoning context.
  This paper is under consideration for publication in TPLP.
A Topological Lowpass Filter for Quasiperiodic Signals
Authors: Michael Robinson
Date: 2016-06-28 00:00:00

This article presents a two-stage topological algorithm for recovering an
estimate of a quasiperiodic function from a set of noisy measurements. The
first stage of the algorithm is a topological phase estimator, which detects
the quasiperiodic structure of the function without placing additional
restrictions on the function. By respecting this phase estimate, the algorithm
avoids creating distortion even when it uses a large number of samples for the
estimate of the function.
"Show me the cup": Reference with Continuous Representations
Authors: Gemma Boleda, Sebastian Padó, Marco Baroni
Date: 2016-06-28 00:00:00

One of the most basic functions of language is to refer to objects in a
shared scene. Modeling reference with continuous representations is challenging
because it requires individuation, i.e., tracking and distinguishing an
arbitrary number of referents. We introduce a neural network model that, given
a definite description and a set of objects represented by natural images,
points to the intended object if the expression has a unique referent, or
indicates a failure, if it does not. The model, directly trained on reference
acts, is competitive with a pipeline manually engineered to perform the same
task, both when referents are purely visual, and when they are characterized by
a combination of visual and linguistic properties.
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Authors: Johannes Heinrich, David Silver
Date: 2016-03-03 00:00:00
Tasks: Card Games, Deep Reinforcement Learning, Game of Poker, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many real-world applications can be described as large-scale games of
imperfect information. To deal with these challenging domains, prior work has
focused on computing Nash equilibria in a handcrafted abstraction of the
domain. In this paper we introduce the first scalable end-to-end approach to
learning approximate Nash equilibria without prior domain knowledge. Our method
combines fictitious self-play with deep reinforcement learning. When applied to
Leduc poker, Neural Fictitious Self-Play (NFSP) approached a Nash equilibrium,
whereas common reinforcement learning methods diverged. In Limit Texas Holdem,
a poker game of real-world scale, NFSP learnt a strategy that approached the
performance of state-of-the-art, superhuman algorithms based on significant
domain expertise.
Expectation propagation for continuous time stochastic processes
Authors: Botond Cseke, David Schnoerr, Manfred Opper, Guido Sanguinetti
Date: 2015-12-18 00:00:00

We consider the inverse problem of reconstructing the posterior measure over
the trajec- tories of a diffusion process from discrete time observations and
continuous time constraints. We cast the problem in a Bayesian framework and
derive approximations to the posterior distributions of single time marginals
using variational approximate inference. We then show how the approximation can
be extended to a wide class of discrete-state Markov jump pro- cesses by making
use of the chemical Langevin equation. Our empirical results show that the
proposed method is computationally efficient and provides good approximations
for these classes of inverse problems.
Scalable image coding based on epitomes
Authors: Martin Alain, Christine Guillemot, Dominique Thoreau, Philippe Guillotel
Date: 2016-06-28 00:00:00
Tasks: Super-Resolution

In this paper, we propose a novel scheme for scalable image coding based on
the concept of epitome. An epitome can be seen as a factorized representation
of an image. Focusing on spatial scalability, the enhancement layer of the
proposed scheme contains only the epitome of the input image. The pixels of the
enhancement layer not contained in the epitome are then restored using two
approaches inspired from local learning-based super-resolution methods. In the
first method, a locally linear embedding model is learned on base layer patches
and then applied to the corresponding epitome patches to reconstruct the
enhancement layer. The second approach learns linear mappings between pairs of
co-located base layer and epitome patches. Experiments have shown that
significant improvement of the rate-distortion performances can be achieved
compared to an SHVC reference.
Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content
Authors: Nemanja Djuric, Hao Wu, Vladan Radosavljevic, Mihajlo Grbovic, Narayan Bhamidipati
Date: 2016-06-28 00:00:00

We consider the problem of learning distributed representations for documents
in data streams. The documents are represented as low-dimensional vectors and
are jointly learned with distributed vector representations of word tokens
using a hierarchical framework with two embedded neural language models. In
particular, we exploit the context of documents in streams and use one of the
language models to model the document sequences, and the other to model word
sequences within them. The models learn continuous vector representations for
both word tokens and documents such that semantically similar documents and
words are close in a common vector space. We discuss extensions to our model,
which can be applied to personalized recommendation and social relationship
mining by adding further user layers to the hierarchy, thus learning
user-specific vectors to represent individual preferences. We validated the
learned representations on a public movie rating data set from MovieLens, as
well as on a large-scale Yahoo News data comprising three months of user
activity logs collected on Yahoo servers. The results indicate that the
proposed model can learn useful representations of both documents and word
tokens, outperforming the current state-of-the-art by a large margin.
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition
Authors: Saeed Reza Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh, Timothée Masquelier
Date: 2015-08-17 00:00:00
Tasks: Object Recognition

Deep convolutional neural networks (DCNNs) have attracted much attention
recently, and have shown to be able to recognize thousands of object categories
in natural image databases. Their architecture is somewhat similar to that of
the human visual system: both use restricted receptive fields, and a hierarchy
of layers which progressively extract more and more abstracted features. Yet it
is unknown whether DCNNs match human performance at the task of view-invariant
object recognition, whether they make similar errors and use similar
representations for this task, and whether the answers depend on the magnitude
of the viewpoint variations. To investigate these issues, we benchmarked eight
state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and
compared their results to those of humans with backward masking. Unlike in all
previous DCNN studies, we carefully controlled the magnitude of the viewpoint
variations to demonstrate that shallow nets can outperform deep nets and humans
when variations are weak. When facing larger variations, however, more layers
were needed to match human performance and error distributions, and to have
representations that are consistent with human behavior. A very deep net with
18 layers even outperformed humans at the highest variation level, using the
most human-like representations.
Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition
Authors: Saeed Reza Kheradpisheh, Mohammad Ganjtabesh, Timothée Masquelier
Date: 2015-04-15 00:00:00
Tasks: Object, Object Categorization, Object Recognition

Retinal image of surrounding objects varies tremendously due to the changes
in position, size, pose, illumination condition, background context, occlusion,
noise, and nonrigid deformations. But despite these huge variations, our visual
system is able to invariantly recognize any object in just a fraction of a
second. To date, various computational models have been proposed to mimic the
hierarchical processing of the ventral visual pathway, with limited success.
Here, we show that the association of both biologically inspired network
architecture and learning rule significantly improves the models' performance
when facing challenging invariant object recognition problems. Our model is an
asynchronous feedforward spiking neural network. When the network is presented
with natural images, the neurons in the entry layers detect edges, and the most
activated ones fire first, while neurons in higher layers are equipped with
spike timing-dependent plasticity. These neurons progressively become selective
to intermediate complexity visual features appropriate for object
categorization. The model is evaluated on 3D-Object and ETH-80 datasets which
are two benchmarks for invariant object recognition, and is shown to outperform
state-of-the-art models, including DeepConvNet and HMAX. This demonstrates its
ability to accurately recognize different instances of multiple object classes
even under various appearance conditions (different views, scales, tilts, and
backgrounds). Several statistical analysis techniques are used to show that our
model extracts class specific and highly informative features.
Fourier descriptors based on the structure of the human primary visual cortex with applications to object recognition
Authors: Amine Bohi, Dario Prandi, Vincente Guis, Frédéric Bouchara, Jean-Paul Gauthier
Date: 2015-07-23 00:00:00
Tasks: Object Recognition, Translation

In this paper we propose a supervised object recognition method using new
global features and inspired by the model of the human primary visual cortex V1
as the semidiscrete roto-translation group $SE(2,N) = \mathbb Z_N\rtimes
\mathbb R^2$. The proposed technique is based on generalized Fourier
descriptors on the latter group, which are invariant to natural geometric
transformations (rotations, translations). These descriptors are then used to
feed an SVM classifier. We have tested our method against the COIL-100 image
database and the ORL face database, and compared it with other techniques based
on traditional descriptors, global and local. The obtained results have shown
that our approach looks extremely efficient and stable to noise, in presence of
which it outperforms the other techniques analyzed in the paper.
Facial Expression Classification Using Rotation Slepian-based Moment Invariants
Authors: Cuiming Zou, Kit Ian Kou
Date: 2016-06-28 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

Rotation moment invariants have been of great interest in image processing
and pattern recognition. This paper presents a novel kind of rotation moment
invariants based on the Slepian functions, which were originally introduced in
the method of separation of variables for Helmholtz equations. They were first
proposed for time series by Slepian and his coworkers in the 1960s. Recent
studies have shown that these functions have an good performance in local
approximation compared to other approximation basis. Motivated by the good
approximation performance, we construct the Slepian-based moments and derive
the rotation invariant. We not only theoretically prove the invariance, but
also discuss the experiments on real data. The proposed rotation invariants are
robust to noise and yield decent performance in facial expression
classification.