pwc-archive/papers-with-abstracts huggingface dataset:
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Authors: Deepti Ghadiyaram, Alan C. Bovik
Date: 2015-11-09 00:00:00
Tasks: Blind Image Quality Assessment, Image Quality Assessment, Small Data Image Classification

Most publicly available image quality databases have been created under
highly controlled conditions by introducing graded simulated distortions onto
high-quality photographs. However, images captured using typical real-world
mobile camera devices are usually afflicted by complex mixtures of multiple
distortions, which are not necessarily well-modeled by the synthetic
distortions found in existing databases. The originators of existing legacy
databases usually conducted human psychometric studies to obtain statistically
meaningful sets of human opinion scores on images in a stringently controlled
visual environment, resulting in small data collections relative to other kinds
of image analysis databases. Towards overcoming these limitations, we designed
and created a new database that we call the LIVE In the Wild Image Quality
Challenge Database, which contains widely diverse authentic image distortions
on a large number of images captured using a representative variety of modern
mobile devices. We also designed and implemented a new online crowdsourcing
system, which we have used to conduct a very large-scale, multi-month image
quality assessment subjective study. Our database consists of over 350000
opinion scores on 1162 images evaluated by over 7000 unique human observers.
Despite the lack of control over the experimental environments of the numerous
study participants, we demonstrate excellent internal consistency of the
subjective dataset. We also evaluate several top-performing blind Image Quality
Assessment algorithms on it and present insights on how mixtures of distortions
challenge both end users as well as automatic perceptual quality prediction
models.
Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Authors: Zhouhan Lin, Yushi Chen, Xing Zhao, Gang Wang
Date: 2015-11-09 00:00:00
Tasks: Classification, General Classification

Hyperspectral image (HSI) classification is a hot topic in the remote sensing
community. This paper proposes a new framework of spectral-spatial feature
extraction for HSI classification, in which for the first time the concept of
deep learning is introduced. Specifically, the model of autoencoder is
exploited in our framework to extract various kinds of features. First we
verify the eligibility of autoencoder by following classical spectral
information based classification and use autoencoders with different depth to
classify hyperspectral image. Further in the proposed framework, we combine PCA
on spectral dimension and autoencoder on the other two spatial dimensions to
extract spectral-spatial information for classification. The experimental
results show that this framework achieves the highest classification accuracy
among all methods, and outperforms classical classifiers such as SVM and
PCA-based SVM.
Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques
Authors: Azam Moosavi, Razvan Stefanescu, Adrian Sandu
Date: 2015-11-09 00:00:00
Tasks: BIG-bench Machine Learning

Reduced order models are computationally inexpensive approximations that
capture the important dynamical characteristics of large, high-fidelity
computer models of physical systems. This paper applies machine learning
techniques to improve the design of parametric reduced order models.
Specifically, machine learning is used to develop feasible regions in the
parameter space where the admissible target accuracy is achieved with a
predefined reduced order basis, to construct parametric maps, to chose the best
two already existing bases for a new parameter configuration from accuracy
point of view and to pre-select the optimal dimension of the reduced basis such
as to meet the desired accuracy. By combining available information using bases
concatenation and interpolation as well as high-fidelity solutions
interpolation we are able to build accurate reduced order models associated
with new parameter settings. Promising numerical results with a viscous Burgers
model illustrate the potential of machine learning approaches to help design
better reduced order models.
A disembodied developmental robotic agent called Samu Bátfai
Authors: Norbert Bátfai
Date: 2015-11-09 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Sentence

The agent program, called Samu, is an experiment to build a disembodied
DevRob (Developmental Robotics) chatter bot that can talk in a natural language
like humans do. One of the main design feature is that Samu can be interacted
with using only a character terminal. This is important not only for practical
aspects of Turing test or Loebner prize, but also for the study of basic
principles of Developmental Robotics. Our purpose is to create a rapid
prototype of Q-learning with neural network approximators for Samu. We sketch
out the early stages of the development process of this prototype, where Samu's
task is to predict the next sentence of tales or conversations. The basic
objective of this paper is to reach the same results using reinforcement
learning with general function approximators that can be achieved by using the
classical Q lookup table on small input samples. The paper is closed by an
experiment that shows a significant improvement in Samu's learning when using
LZW tree to narrow the number of possible Q-actions.
Visual Language Modeling on CNN Image Representations
Authors: Hiroharu Kato, Tatsuya Harada
Date: 2015-11-09 00:00:00
Tasks: Language Modeling, Language Modelling

Measuring the naturalness of images is important to generate realistic images
or to detect unnatural regions in images. Additionally, a method to measure
naturalness can be complementary to Convolutional Neural Network (CNN) based
features, which are known to be insensitive to the naturalness of images.
However, most probabilistic image models have insufficient capability of
modeling the complex and abstract naturalness that we feel because they are
built directly on raw image pixels. In this work, we assume that naturalness
can be measured by the predictability on high-level features during eye
movement. Based on this assumption, we propose a novel method to evaluate the
naturalness by building a variant of Recurrent Neural Network Language Models
on pre-trained CNN representations. Our method is applied to two tasks,
demonstrating that 1) using our method as a regularizer enables us to generate
more understandable images from image features than existing approaches, and 2)
unnaturalness maps produced by our method achieve state-of-the-art eye fixation
prediction performance on two well-studied datasets.
Tree-structured composition in neural networks without tree-structured architectures
Authors: Samuel R. Bowman, Christopher D. Manning, Christopher Potts
Date: 2015-06-16 00:00:00
Tasks: Sentence

Tree-structured neural networks encode a particular tree geometry for a
sentence in the network design. However, these models have at best only
slightly outperformed simpler sequence-based models. We hypothesize that neural
sequence models like LSTMs are in fact able to discover and implicitly use
recursive compositional structure, at least for tasks with clear cues to that
structure in the data. We demonstrate this possibility using an artificial data
task for which recursive compositional structure is crucial, and find an
LSTM-based sequence model can indeed learn to exploit the underlying tree
structure. However, its performance consistently lags behind that of tree
models, even on large training sets, suggesting that tree-structured models are
more effective at exploiting recursive structure.
Using Behavior Objects to Manage Complexity in Virtual Worlds
Authors: Martin Černý, Tomáš Plch, Matěj Marko, Jakub Gemrot, Petr Ondráček, Cyril Brom
Date: 2015-08-03 00:00:00

The quality of high-level AI of non-player characters (NPCs) in commercial
open-world games (OWGs) has been increasing during the past years. However, due
to constraints specific to the game industry, this increase has been slow and
it has been driven by larger budgets rather than adoption of new complex AI
techniques. Most of the contemporary AI is still expressed as hard-coded
scripts. The complexity and manageability of the script codebase is one of the
key limiting factors for further AI improvements. In this paper we address this
issue. We present behavior objects - a general approach to development of NPC
behaviors for large OWGs. Behavior objects are inspired by object-oriented
programming and extend the concept of smart objects. Our approach promotes
encapsulation of data and code for multiple related behaviors in one place,
hiding internal details and embedding intelligence in the environment. Behavior
objects are a natural abstraction of five different techniques that we have
implemented to manage AI complexity in an upcoming AAA OWG. We report the
details of the implementations in the context of behavior trees and the lessons
learned during development. Our work should serve as inspiration for AI
architecture designers from both the academia and the industry.
Bayesian Inference in Cumulative Distribution Fields
Authors: Ricardo Silva
Date: 2015-11-09 00:00:00
Tasks: Bayesian Inference

One approach for constructing copula functions is by multiplication. Given
that products of cumulative distribution functions (CDFs) are also CDFs, an
adjustment to this multiplication will result in a copula model, as discussed
by Liebscher (J Mult Analysis, 2008). Parameterizing models via products of
CDFs has some advantages, both from the copula perspective (e.g., it is
well-defined for any dimensionality) and from general multivariate analysis
(e.g., it provides models where small dimensional marginal distributions can be
easily read-off from the parameters). Independently, Huang and Frey (J Mach
Learn Res, 2011) showed the connection between certain sparse graphical models
and products of CDFs, as well as message-passing (dynamic programming) schemes
for computing the likelihood function of such models. Such schemes allows
models to be estimated with likelihood-based methods. We discuss and
demonstrate MCMC approaches for estimating such models in a Bayesian context,
their application in copula modeling, and how message-passing can be strongly
simplified. Importantly, our view of message-passing opens up possibilities to
scaling up such methods, given that even dynamic programming is not a scalable
solution for calculating likelihood functions in many models.
Communication-Efficient False Discovery Rate Control via Knockoff Aggregation
Authors: Weijie Su, Junyang Qian, Linxi Liu
Date: 2015-06-17 00:00:00

The false discovery rate (FDR)---the expected fraction of spurious
discoveries among all the discoveries---provides a popular statistical
assessment of the reproducibility of scientific studies in various disciplines.
In this work, we introduce a new method for controlling the FDR in
meta-analysis of many decentralized linear models. Our method targets the
scenario where many research groups---possibly the number of which is
random---are independently testing a common set of hypotheses and then sending
summary statistics to a coordinating center in an online manner. Built on the
knockoffs framework introduced by Barber and Candes (2015), our procedure
starts by applying the knockoff filter to each linear model and then aggregates
the summary statistics via one-shot communication in a novel way. This method
gives exact FDR control non-asymptotically without any knowledge of the noise
variances or making any assumption about sparsity of the signal. In certain
settings, it has a communication complexity that is optimal up to a logarithmic
factor.
A Survey of Algorithms and Analysis for Adaptive Online Learning
Authors: H. Brendan McMahan
Date: 2014-03-14 00:00:00
Tasks: Survey

We present tools for the analysis of Follow-The-Regularized-Leader (FTRL),
Dual Averaging, and Mirror Descent algorithms when the regularizer
(equivalently, prox-function or learning rate schedule) is chosen adaptively
based on the data. Adaptivity can be used to prove regret bounds that hold on
every round, and also allows for data-dependent regret bounds as in
AdaGrad-style algorithms (e.g., Online Gradient Descent with adaptive
per-coordinate learning rates). We present results from a large number of prior
works in a unified manner, using a modular and tight analysis that isolates the
key arguments in easily re-usable lemmas. This approach strengthens pre-viously
known FTRL analysis techniques to produce bounds as tight as those achieved by
potential functions or primal-dual analysis. Further, we prove a general and
exact equivalence between an arbitrary adaptive Mirror Descent algorithm and a
correspond- ing FTRL update, which allows us to analyze any Mirror Descent
algorithm in the same framework. The key to bridging the gap between Dual
Averaging and Mirror Descent algorithms lies in an analysis of the
FTRL-Proximal algorithm family. Our regret bounds are proved in the most
general form, holding for arbitrary norms and non-smooth regularizers with
time-varying weight.
PAC-Bayesian High Dimensional Bipartite Ranking
Authors: Benjamin Guedj, Sylvain Robbiano
Date: 2015-11-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the PAC-Bayesian approach. We consider nonlinear additive scoring functions, and we derive non-asymptotic risk bounds under a sparsity assumption. In particular, oracle inequalities in probability holding under a margin condition assess the performance of our procedure, and prove its minimax optimality. An MCMC-flavored algorithm is proposed to implement our method, along with its behavior on synthetic and real-life datasets.
Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Authors: Ricardo Silva, Shohei Shimizu
Date: 2015-11-09 00:00:00

Learning a causal effect from observational data is not straightforward, as
this is not possible without further assumptions. If hidden common causes
between treatment $X$ and outcome $Y$ cannot be blocked by other measurements,
one possibility is to use an instrumental variable. In principle, it is
possible under some assumptions to discover whether a variable is structurally
instrumental to a target causal effect $X \rightarrow Y$, but current
frameworks are somewhat lacking on how general these assumptions can be. A
instrumental variable discovery problem is challenging, as no variable can be
tested as an instrument in isolation but only in groups, but different
variables might require different conditions to be considered an instrument.
Moreover, identification constraints might be hard to detect statistically. In
this paper, we give a theoretical characterization of instrumental variable
discovery, highlighting identifiability problems and solutions, the need for
non-Gaussianity assumptions, and how they fit within existing methods.
Biologically Inspired Dynamic Textures for Probing Motion Perception
Authors: Jonathan Vacher, Andrew Meso, Laurent U. Perrinet, Gabriel Peyré
Date: 2015-11-09 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Bayesian Inference

Perception is often described as a predictive process based on an optimal
inference with respect to a generative model. We study here the principled
construction of a generative model specifically crafted to probe motion
perception. In that context, we first provide an axiomatic, biologically-driven
derivation of the model. This model synthesizes random dynamic textures which
are defined by stationary Gaussian distributions obtained by the random
aggregation of warped patterns. Importantly, we show that this model can
equivalently be described as a stochastic partial differential equation. Using
this characterization of motion in images, it allows us to recast motion-energy
models into a principled Bayesian inference framework. Finally, we apply these
textures in order to psychophysically probe speed perception in humans. In this
framework, while the likelihood is derived from the generative model, the prior
is estimated from the observed results and accounts for the perceptual bias in
a principled fashion.
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
Authors: Francis Bach
Date: 2015-02-24 00:00:00
Tasks: valid

We show that kernel-based quadrature rules for computing integrals can be
seen as a special case of random feature expansions for positive definite
kernels, for a particular decomposition that always exists for such kernels. We
provide a theoretical analysis of the number of required samples for a given
approximation error, leading to both upper and lower bounds that are based
solely on the eigenvalues of the associated integral operator and match up to
logarithmic terms. In particular, we show that the upper bound may be obtained
from independent and identically distributed samples from a specific
non-uniform distribution, while the lower bound if valid for any set of points.
Applying our results to kernel-based quadrature, while our results are fairly
general, we recover known upper and lower bounds for the special cases of
Sobolev spaces. Moreover, our results extend to the more general problem of
full function approximations (beyond simply computing an integral), with
results in L2- and L$\infty$-norm that match known results for special cases.
Applying our results to random features, we show an improvement of the number
of random features needed to preserve the generalization guarantees for
learning with Lipschitz-continuous losses.
Exploiting Egocentric Object Prior for 3D Saliency Detection
Authors: Gedas Bertasius, Hyun Soo Park, Jianbo Shi
Date: 2015-11-09 00:00:00
Tasks: Object, Saliency Detection, Saliency Prediction

On a minute-to-minute basis people undergo numerous fluid interactions with
objects that barely register on a conscious level. Recent neuroscientific
research demonstrates that humans have a fixed size prior for salient objects.
This suggests that a salient object in 3D undergoes a consistent transformation
such that people's visual system perceives it with an approximately fixed size.
This finding indicates that there exists a consistent egocentric object prior
that can be characterized by shape, size, depth, and location in the first
person view.
  In this paper, we develop an EgoObject Representation, which encodes these
characteristics by incorporating shape, location, size and depth features from
an egocentric RGBD image. We empirically show that this representation can
accurately characterize the egocentric object prior by testing it on an
egocentric RGBD dataset for three tasks: the 3D saliency detection, future
saliency prediction, and interaction classification. This representation is
evaluated on our new Egocentric RGBD Saliency dataset that includes various
activities such as cooking, dining, and shopping. By using our EgoObject
representation, we outperform previously proposed models for saliency detection
(relative 30% improvement for 3D saliency detection task) on our dataset.
Additionally, we demonstrate that this representation allows us to predict
future salient objects based on the gaze cue and classify people's interactions
with objects.
Enacting textual entailment and ontologies for automated essay grading in chemical domain
Authors: Adrian Groza, Roxana Szabo
Date: 2015-11-09 00:00:00
Tasks: Natural Language Inference

We propose a system for automated essay grading using ontologies and textual
entailment. The process of textual entailment is guided by hypotheses, which
are extracted from a domain ontology. Textual entailment checks if the truth of
the hypothesis follows from a given text. We enact textual entailment to
compare students answer to a model answer obtained from ontology. We validated
the solution against various essays written by students in the chemistry
domain.
Learning Definite Horn Formulas from Closure Queries
Authors: Marta Arias, José L. Balcázar, Cristina Tîrnăucă
Date: 2015-03-31 00:00:00

A definite Horn theory is a set of n-dimensional Boolean vectors whose
characteristic function is expressible as a definite Horn formula, that is, as
conjunction of definite Horn clauses. The class of definite Horn theories is
known to be learnable under different query learning settings, such as learning
from membership and equivalence queries or learning from entailment. We propose
yet a different type of query: the closure query. Closure queries are a natural
extension of membership queries and also a variant, appropriate in the context
of definite Horn formulas, of the so-called correction queries. We present an
algorithm that learns conjunctions of definite Horn clauses in polynomial time,
using closure and equivalence queries, and show how it relates to the canonical
Guigues-Duquenne basis for implicational systems. We also show how the
different query models mentioned relate to each other by either showing
full-fledged reductions by means of query simulation (where possible), or by
showing their connections in the context of particular algorithms that use them
for learning definite Horn formulas.
Toward Biochemical Probabilistic Computation
Authors: Jacques Droulez, David Colliaux, Audrey Houillon, Pierre Bessière
Date: 2015-11-09 00:00:00
Tasks: Bayesian Inference

Living organisms survive and multiply even though they have uncertain and
incomplete information about their environment and imperfect models to predict
the consequences of their actions. Bayesian models have been proposed to face
this challenge. Indeed, Bayesian inference is a way to do optimal reasoning
when only uncertain and incomplete information is available. Various
perceptive, sensory-motor, and cognitive functions have been successfully
modeled this way. However, the biological mechanisms allowing animals and
humans to represent and to compute probability distributions are not known. It
has been proposed that neurons and assemblies of neurons could be the
appropriate scale to search for clues to probabilistic reasoning. In contrast,
in this paper, we propose that interacting populations of macromolecules and
diffusible messengers can perform probabilistic computation. This suggests that
probabilistic reasoning, based on cellular signaling pathways, is a fundamental
skill of living organisms available to the simplest unicellular organisms as
well as the most complex brains.
Decomposition Bounds for Marginal MAP
Authors: Wei Ping, Qiang Liu, Alexander Ihler
Date: 2015-11-09 00:00:00
Proceeding: NeurIPS 2015 12

Marginal MAP inference involves making MAP predictions in systems defined
with latent variables or missing information. It is significantly more
difficult than pure marginalization and MAP tasks, for which a large class of
efficient and convergent variational algorithms, such as dual decomposition,
exist. In this work, we generalize dual decomposition to a generic power sum
inference task, which includes marginal MAP, along with pure marginalization
and MAP, as special cases. Our method is based on a block coordinate descent
algorithm on a new convex decomposition bound, that is guaranteed to converge
monotonically, and can be parallelized efficiently. We demonstrate our approach
on marginal MAP queries defined on real-world problems from the UAI approximate
inference challenge, showing that our framework is faster and more reliable
than previous methods.