pwc-archive/papers-with-abstracts huggingface dataset:
Reuse of Neural Modules for General Video Game Playing
Authors: Alexander Braylan, Mark Hollenbeck, Elliot Meyerson, Risto Miikkulainen
Date: 2015-12-04 00:00:00
Tasks: Atari Games, Decision Making, Reinforcement Learning, Sequential Decision Making, Transfer Learning

A general approach to knowledge transfer is introduced in which an agent
controlled by a neural network adapts how it reuses existing networks as it
learns in a new domain. Networks trained for a new domain can improve their
performance by routing activation selectively through previously learned neural
structure, regardless of how or for what it was learned. A neuroevolution
implementation of this approach is presented with application to
high-dimensional sequential decision-making domains. This approach is more
general than previous approaches to neural transfer for reinforcement learning.
It is domain-agnostic and requires no prior assumptions about the nature of
task relatedness or mappings. The method is analyzed in a stochastic version of
the Arcade Learning Environment, demonstrating that it improves performance in
some of the more complex Atari 2600 games, and that the success of transfer can
be predicted based on a high-level characterization of game dynamics.
Motion trails from time-lapse video
Authors: Camille Goudeseune
Date: 2015-12-04 00:00:00

From an image sequence captured by a stationary camera, background
subtraction can detect moving foreground objects in the scene. Distinguishing
foreground from background is further improved by various heuristics. Then each
object's motion can be emphasized by duplicating its positions as a motion
trail. These trails clarify the objects' spatial relationships. Also, adding
motion trails to a video before previewing it at high speed reduces the risk of
overlooking transient events.
Detecting Road Surface Wetness from Audio: A Deep Learning Approach
Authors: Irman Abdić, Lex Fridman, Erik Marchi, Daniel E. Brown, William Angell, Bryan Reimer, Björn Schuller
Date: 2015-11-22 00:00:00
Tasks: Deep Learning, General Classification

We introduce a recurrent neural network architecture for automated road
surface wetness detection from audio of tire-surface interaction. The
robustness of our approach is evaluated on 785,826 bins of audio that span an
extensive range of vehicle speeds, noises from the environment, road surface
types, and pavement conditions including international roughness index (IRI)
values from 25 in/mi to 1400 in/mi. The training and evaluation of the model
are performed on different roads to minimize the impact of environmental and
other external factors on the accuracy of the classification. We achieve an
unweighted average recall (UAR) of 93.2% across all vehicle speeds including 0
mph. The classifier still works at 0 mph because the discriminating signal is
present in the sound of other vehicles driving by.
Learning the Semantics of Manipulation Action
Authors: Yezhou Yang, Yiannis Aloimonos, Cornelia Fermuller, Eren Erdal Aksoy
Date: 2015-12-04 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Semantic Parsing

In this paper we present a formal computational framework for modeling
manipulation actions. The introduced formalism leads to semantics of
manipulation action and has applications to both observing and understanding
human manipulation actions as well as executing them with a robotic mechanism
(e.g. a humanoid robot). It is based on a Combinatory Categorial Grammar. The
goal of the introduced framework is to: (1) represent manipulation actions with
both syntax and semantic parts, where the semantic part employs
$\lambda$-calculus; (2) enable a probabilistic semantic parsing schema to learn
the $\lambda$-calculus representation of manipulation action from an annotated
action corpus of videos; (3) use (1) and (2) to develop a system that visually
observes manipulation actions and understands their meaning while it can reason
beyond observations using propositional logic and axiom schemata. The
experiments conducted on a public available large manipulation action dataset
validate the theoretical framework and our implementation.
ASIST: Automatic Semantically Invariant Scene Transformation
Authors: Or Litany, Tal Remez, Daniel Freedman, Lior Shapira, Alex Bronstein, Ran Gal
Date: 2015-12-04 00:00:00
Tasks: Object

We present ASIST, a technique for transforming point clouds by replacing
objects with their semantically equivalent counterparts. Transformations of
this kind have applications in virtual reality, repair of fused scans, and
robotics. ASIST is based on a unified formulation of semantic labeling and
object replacement; both result from minimizing a single objective. We present
numerical tools for the efficient solution of this optimization problem. The
method is experimentally assessed on new datasets of both synthetic and real
point clouds, and is additionally compared to two recent works on object
replacement on data from the corresponding papers.
Necessary and Sufficient Conditions and a Provably Efficient Algorithm for Separable Topic Discovery
Authors: Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
Date: 2015-08-23 00:00:00
Tasks: Topic Models

We develop necessary and sufficient conditions and a novel provably
consistent and efficient algorithm for discovering topics (latent factors) from
observations (documents) that are realized from a probabilistic mixture of
shared latent factors that have certain properties. Our focus is on the class
of topic models in which each shared latent factor contains a novel word that
is unique to that factor, a property that has come to be known as separability.
Our algorithm is based on the key insight that the novel words correspond to
the extreme points of the convex hull formed by the row-vectors of a suitably
normalized word co-occurrence matrix. We leverage this geometric insight to
establish polynomial computation and sample complexity bounds based on a few
isotropic random projections of the rows of the normalized word co-occurrence
matrix. Our proposed random-projections-based algorithm is naturally amenable
to an efficient distributed implementation and is attractive for modern
web-scale distributed data mining applications.
Hand-held Video Deblurring via Efficient Fourier Aggregation
Authors: Mauricio Delbracio, Guillermo Sapiro
Date: 2015-09-17 00:00:00
Tasks: Deblurring, Video Deblurring

Videos captured with hand-held cameras often suffer from a significant amount
of blur, mainly caused by the inevitable natural tremor of the photographer's
hand. In this work, we present an algorithm that removes blur due to camera
shake by combining information in the Fourier domain from nearby frames in a
video. The dynamic nature of typical videos with the presence of multiple
moving objects and occlusions makes this problem of camera shake removal
extremely challenging, in particular when low complexity is needed. Given an
input video frame, we first create a consistent registered version of
temporally adjacent frames. Then, the set of consistently registered frames is
block-wise fused in the Fourier domain with weights depending on the Fourier
spectrum magnitude. The method is motivated from the physiological fact that
camera shake blur has a random nature and therefore, nearby video frames are
generally blurred differently. Experiments with numerous videos recorded in the
wild, along with extensive comparisons, show that the proposed algorithm
achieves state-of-the-art results while at the same time being much faster than
its competitors.
Removing Camera Shake via Weighted Fourier Burst Accumulation
Authors: Mauricio Delbracio, Guillermo Sapiro
Date: 2015-05-11 00:00:00

Numerous recent approaches attempt to remove image blur due to camera shake,
either with one or multiple input images, by explicitly solving an inverse and
inherently ill-posed deconvolution problem. If the photographer takes a burst
of images, a modality available in virtually all modern digital cameras, we
show that it is possible to combine them to get a clean sharp version. This is
done without explicitly solving any blur estimation and subsequent inverse
problem. The proposed algorithm is strikingly simple: it performs a weighted
average in the Fourier domain, with weights depending on the Fourier spectrum
magnitude. The method can be seen as a generalization of the align and average
procedure, with a weighted average, motivated by hand-shake physiology and
theoretically supported, taking place in the Fourier domain. The method's
rationale is that camera shake has a random nature and therefore each image in
the burst is generally blurred differently. Experiments with real camera data,
and extensive comparisons, show that the proposed Fourier Burst Accumulation
(FBA) algorithm achieves state-of-the-art results an order of magnitude faster,
with simplicity for on-board implementation on camera phones. Finally, we also
present experiments in real high dynamic range (HDR) scenes, showing how the
method can be straightforwardly extended to HDR photography.
Compressive Deconvolution in Medical Ultrasound Imaging
Authors: Zhouye Chen, Adrian Basarab, Denis Kouamé
Date: 2015-07-01 00:00:00

The interest of compressive sampling in ultrasound imaging has been recently
extensively evaluated by several research teams. Following the different
application setups, it has been shown that the RF data may be reconstructed
from a small number of measurements and/or using a reduced number of ultrasound
pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal
to noise ratio are affected by the limited bandwidth of the imaging transducer
and the physical phenomenon related to US wave propagation. To overcome these
limitations, several deconvolution-based image processing techniques have been
proposed to enhance the ultrasound images. In this paper, we propose a novel
framework, named compressive deconvolution, that reconstructs enhanced RF
images from compressed measurements. Exploiting an unified formulation of the
direct acquisition model, combining random projections and 2D convolution with
a spatially invariant point spread function, the benefit of our approach is the
joint data volume reduction and image quality improvement. The proposed
optimization method, based on the Alternating Direction Method of Multipliers,
is evaluated on both simulated and in vivo data.
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
Authors: Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Thang Bui, Richard E. Turner
Date: 2015-11-10 00:00:00
Tasks: Classification, General Classification, Variational Inference

A method for large scale Gaussian process classification has been recently
proposed based on expectation propagation (EP). Such a method allows Gaussian
process classifiers to be trained on very large datasets that were out of the
reach of previous deployments of EP and has been shown to be competitive with
related techniques based on stochastic variational inference. Nevertheless, the
memory resources required scale linearly with the dataset size, unlike in
variational methods. This is a severe limitation when the number of instances
is very large. Here we show that this problem is avoided when stochastic EP is
used to train the model.
What Makes it Difficult to Understand a Scientific Literature?
Authors: Mengyun Cao, Jiao Tian, Dezhi Cheng, Jin Liu, Xiaoping Sun
Date: 2015-12-04 00:00:00
Tasks: Reading Comprehension

In the artificial intelligence area, one of the ultimate goals is to make
computers understand human language and offer assistance. In order to achieve
this ideal, researchers of computer science have put forward a lot of models
and algorithms attempting at enabling the machine to analyze and process human
natural language on different levels of semantics. Although recent progress in
this field offers much hope, we still have to ask whether current research can
provide assistance that people really desire in reading and comprehension. To
this end, we conducted a reading comprehension test on two scientific papers
which are written in different styles. We use the semantic link models to
analyze the understanding obstacles that people will face in the process of
reading and figure out what makes it difficult for human to understand a
scientific literature. Through such analysis, we summarized some
characteristics and problems which are reflected by people with different
levels of knowledge on the comprehension of difficult science and technology
literature, which can be modeled in semantic link network. We believe that
these characteristics and problems will help us re-examine the existing machine
models and are helpful in the designing of new one.
Model Validation for Vision Systems via Graphics Simulation
Authors: V. S. R. Veeravasarapu, Rudra Narayan Hota, Constantin Rothkopf, Ramesh Visvanathan
Date: 2015-12-04 00:00:00
Tasks: model, valid

Rapid advances in computation, combined with latest advances in computer
graphics simulations have facilitated the development of vision systems and
training them in virtual environments. One major stumbling block is in
certification of the designs and tuned parameters of these systems to work in
real world. In this paper, we begin to explore the fundamental question: Which
type of information transfer is more analogous to real world? Inspired from the
performance characterization methodology outlined in the 90's, we note that
insights derived from simulations can be qualitative or quantitative depending
on the degree of the fidelity of models used in simulations and the nature of
the questions posed by the experimenter. We adapt the methodology in the
context of current graphics simulation tools for modeling data generation
processes and, for systematic performance characterization and trade-off
analysis for vision system design leading to qualitative and quantitative
insights. In concrete, we examine invariance assumptions used in vision
algorithms for video surveillance settings as a case study and assess the
degree to which those invariance assumptions deviate as a function of
contextual variables on both graphics simulations and in real data. As computer
graphics rendering quality improves, we believe teasing apart the degree to
which model assumptions are valid via systematic graphics simulation can be a
significant aid to assisting more principled ways of approaching vision system
design and performance modeling.
Max-Pooling Dropout for Regularization of Convolutional Neural Networks
Authors: Haibing Wu, Xiaodong Gu
Date: 2015-12-04 00:00:00

Recently, dropout has seen increasing use in deep learning. For deep
convolutional neural networks, dropout is known to work well in fully-connected
layers. However, its effect in pooling layers is still not clear. This paper
demonstrates that max-pooling dropout is equivalent to randomly picking
activation based on a multinomial distribution at training time. In light of
this insight, we advocate employing our proposed probabilistic weighted
pooling, instead of commonly used max-pooling, to act as model averaging at
test time. Empirical evidence validates the superiority of probabilistic
weighted pooling. We also compare max-pooling dropout and stochastic pooling,
both of which introduce stochasticity based on multinomial distributions at
pooling stage.
Topic segmentation via community detection in complex networks
Authors: Henrique F. de Arruda, Luciano da F. Costa, Diego R. Amancio
Date: 2015-12-04 00:00:00
Tasks: Articles, Community Detection

Many real systems have been modelled in terms of network concepts, and
written texts are a particular example of information networks. In recent
years, the use of network methods to analyze language has allowed the discovery
of several interesting findings, including the proposition of novel models to
explain the emergence of fundamental universal patterns. While syntactical
networks, one of the most prevalent networked models of written texts, display
both scale-free and small-world properties, such representation fails in
capturing other textual features, such as the organization in topics or
subjects. In this context, we propose a novel network representation whose main
purpose is to capture the semantical relationships of words in a simple way. To
do so, we link all words co-occurring in the same semantic context, which is
defined in a threefold way. We show that the proposed representations favours
the emergence of communities of semantically related words, and this feature
may be used to identify relevant topics. The proposed methodology to detect
topics was applied to segment selected Wikipedia articles. We have found that,
in general, our methods outperform traditional bag-of-words representations,
which suggests that a high-level textual representation may be useful to study
semantical features of texts.
Sublabel-Accurate Relaxation of Nonconvex Energies
Authors: Thomas Möllenhoff, Emanuel Laude, Michael Moeller, Jan Lellmann, Daniel Cremers
Date: 2015-12-04 00:00:00
Proceeding: CVPR 2016 6

We propose a novel spatially continuous framework for convex relaxations
based on functional lifting. Our method can be interpreted as a
sublabel-accurate solution to multilabel problems. We show that previously
proposed functional lifting methods optimize an energy which is linear between
two labels and hence require (often infinitely) many labels for a faithful
approximation. In contrast, the proposed formulation is based on a piecewise
convex approximation and therefore needs far fewer labels. In comparison to
recent MRF-based approaches, our method is formulated in a spatially continuous
setting and shows less grid bias. Moreover, in a local sense, our formulation
is the tightest possible convex relaxation. It is easy to implement and allows
an efficient primal-dual optimization on GPUs. We show the effectiveness of our
approach on several computer vision problems.
Completely random measures for modelling block-structured networks
Authors: Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Date: 2015-07-10 00:00:00

Many statistical methods for network data parameterize the edge-probability
by attributing latent traits to the vertices such as block structure and assume
exchangeability in the sense of the Aldous-Hoover representation theorem.
Empirical studies of networks indicate that many real-world networks have a
power-law distribution of the vertices which in turn implies the number of
edges scale slower than quadratically in the number of vertices. These
assumptions are fundamentally irreconcilable as the Aldous-Hoover theorem
implies quadratic scaling of the number of edges. Recently Caron and Fox (2014)
proposed the use of a different notion of exchangeability due to Kallenberg
(2009) and obtained a network model which admits power-law behaviour while
retaining desirable statistical properties, however this model does not capture
latent vertex traits such as block-structure. In this work we re-introduce the
use of block-structure for network models obeying Kallenberg's notion of
exchangeability and thereby obtain a model which admits the inference of
block-structure and edge inhomogeneity. We derive a simple expression for the
likelihood and an efficient sampling method. The obtained model is not
significantly more difficult to implement than existing approaches to
block-modelling and performs well on real network datasets.
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Authors: Alexander G. de G. Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani
Date: 2015-04-27 00:00:00
Tasks: Variational Inference

The variational framework for learning inducing variables (Titsias, 2009a)
has had a large impact on the Gaussian process literature. The framework may be
interpreted as minimizing a rigorously defined Kullback-Leibler divergence
between the approximating and posterior processes. To our knowledge this
connection has thus far gone unremarked in the literature. In this paper we
give a substantial generalization of the literature on this topic. We give a
new proof of the result for infinite index sets which allows inducing points
that are not data points and likelihoods that depend on all function values. We
then discuss augmented index sets and show that, contrary to previous works,
marginal consistency of augmentation is not enough to guarantee consistency of
variational inference with the original model. We then characterize an extra
condition where such a guarantee is obtainable. Finally we show how our
framework sheds light on interdomain sparse approximations and sparse
approximations for Cox processes.
Locally Adaptive Translation for Knowledge Graph Embedding
Authors: Yantao Jia, Yuanzhuo Wang, Hailun Lin, Xiaolong Jin, Xue-Qi Cheng
Date: 2015-12-04 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Translation

Knowledge graph embedding aims to represent entities and relations in a
large-scale knowledge graph as elements in a continuous vector space. Existing
methods, e.g., TransE and TransH, learn embedding representation by defining a
global margin-based loss function over the data. However, the optimal loss
function is determined during experiments whose parameters are examined among a
closed set of candidates. Moreover, embeddings over two knowledge graphs with
different entities and relations share the same set of candidate loss
functions, ignoring the locality of both graphs. This leads to the limited
performance of embedding related applications. In this paper, we propose a
locally adaptive translation method for knowledge graph embedding, called
TransA, to find the optimal loss function by adaptively determining its margin
over different knowledge graphs. Experiments on two benchmark data sets
demonstrate the superiority of the proposed method, as compared to
the-state-of-the-art ones.
Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms
Authors: Collins Leke, Tshilidzi Marwala, Satyakama Paul
Date: 2015-12-04 00:00:00
Tasks: Decision Making, Denoising, Evolutionary Algorithms, Imputation

In the last couple of decades, there has been major advancements in the
domain of missing data imputation. The techniques in the domain include amongst
others: Expectation Maximization, Neural Networks with Evolutionary Algorithms
or optimization techniques and K-Nearest Neighbor approaches to solve the
problem. The presence of missing data entries in databases render the tasks of
decision-making and data analysis nontrivial. As a result this area has
attracted a lot of research interest with the aim being to yield accurate and
time efficient and sensitive missing data imputation techniques especially when
time sensitive applications are concerned like power plants and winding
processes. In this article, considering arbitrary and monotone missing data
patterns, we hypothesize that the use of deep neural networks built using
autoencoders and denoising autoencoders in conjunction with genetic algorithms,
swarm intelligence and maximum likelihood estimator methods as novel data
imputation techniques will lead to better imputed values than existing
techniques. Also considered are the missing at random, missing completely at
random and missing not at random missing data mechanisms. We also intend to use
fuzzy logic in tandem with deep neural networks to perform the missing data
imputation tasks, as well as different building blocks for the deep neural
networks like Stacked Restricted Boltzmann Machines and Deep Belief Networks to
test our hypothesis. The motivation behind this article is the need for missing
data imputation techniques that lead to better imputed values than existing
methods with higher accuracies and lower errors.