pwc-archive/papers-with-abstracts huggingface dataset:
ToPs: Ensemble Learning with Trees of Predictors
Authors: Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Date: 2017-06-05 00:00:00
Tasks: Ensemble Learning

We present a new approach to ensemble learning. Our approach constructs a
tree of subsets of the feature space and associates a predictor (predictive
model) - determined by training one of a given family of base learners on an
endogenously determined training set - to each node of the tree; we call the
resulting object a tree of predictors. The (locally) optimal tree of predictors
is derived recursively; each step involves jointly optimizing the split of the
terminal nodes of the previous tree and the choice of learner and training set
(hence predictor) for each set in the split. The feature vector of a new
instance determines a unique path through the optimal tree of predictors; the
final prediction aggregates the predictions of the predictors along this path.
We derive loss bounds for the final predictor in terms of the Rademacher
complexity of the base learners. We report the results of a number of
experiments on a variety of datasets, showing that our approach provides
statistically significant improvements over state-of-the-art machine learning
algorithms, including various ensemble learning methods. Our approach works
because it allows us to endogenously create more complex learners - when needed
- and endogenously match both the learner and the training set to the
characteristics of the dataset while still avoiding over-fitting.
Modeling of Facial Aging and Kinship: A Survey
Authors: Markos Georgopoulos, Yannis Panagakis, Maja Pantic
Date: 2018-02-13 00:00:00
Tasks: Age Estimation, Kinship Verification, Survey

Computational facial models that capture properties of facial cues related to
aging and kinship increasingly attract the attention of the research community,
enabling the development of reliable methods for age progression, age
estimation, age-invariant facial characterization, and kinship verification
from visual data. In this paper, we review recent advances in modeling of
facial aging and kinship. In particular, we provide an up-to date, complete
list of available annotated datasets and an in-depth analysis of geometric,
hand-crafted, and learned facial representations that are used for facial aging
and kinship characterization. Moreover, evaluation protocols and metrics are
reviewed and notable experimental results for each surveyed task are analyzed.
This survey allows us to identify challenges and discuss future research
directions for the development of robust facial models in real-world
conditions.
Barista - a Graphical Tool for Designing and Training Deep Neural Networks
Authors: Soeren Klemm, Aaron Scherzinger, Dominik Drees, Xiaoyi Jiang
Date: 2018-02-13 00:00:00
Tasks: Deep Learning

In recent years, the importance of deep learning has significantly increased
in pattern recognition, computer vision, and artificial intelligence research,
as well as in industry. However, despite the existence of multiple deep
learning frameworks, there is a lack of comprehensible and easy-to-use
high-level tools for the design, training, and testing of deep neural networks
(DNNs). In this paper, we introduce Barista, an open-source graphical
high-level interface for the Caffe deep learning framework. While Caffe is one
of the most popular frameworks for training DNNs, editing prototext files in
order to specify the net architecture and hyper parameters can become a
cumbersome and error-prone task. Instead, Barista offers a fully graphical user
interface with a graph-based net topology editor and provides an end-to-end
training facility for DNNs, which allows researchers to focus on solving their
problems without having to write code, edit text files, or manually parse
logged data.
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity
Authors: Dan Garber
Date: 2018-02-13 00:00:00

Hoffman's classical result gives a bound on the distance of a point from a
convex and compact polytope in terms of the magnitude of violation of the
constraints. Recently, several results showed that Hoffman's bound can be used
to derive strongly-convex-like rates for first-order methods for
\textit{offline} convex optimization of curved, though not strongly convex,
functions, over polyhedral sets. In this work, we use this classical result for
the first time to obtain faster rates for \textit{online convex optimization}
over polyhedral sets with curved convex, though not strongly convex, loss
functions. We show that under several reasonable assumptions on the data, the
standard \textit{Online Gradient Descent} algorithm guarantees logarithmic
regret. To the best of our knowledge, the only previous algorithm to achieve
logarithmic regret in the considered settings is the \textit{Online Newton
Step} algorithm which requires quadratic (in the dimension) memory and at least
quadratic runtime per iteration, which greatly limits its applicability to
large-scale problems. In particular, our results hold for
\textit{semi-adversarial} settings in which the data is a combination of an
arbitrary (adversarial) sequence and a stochastic sequence, which might provide
reasonable approximation for many real-world sequences, or under a natural
assumption that the data is low-rank. We demonstrate via experiments that the
regret of OGD is indeed comparable to that of ONS (and even far better) on
curved though not strongly-convex losses.
Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks
Authors: M. Shahzeb Khan Gul, Bahadir K. Gunturk
Date: 2017-07-04 00:00:00
Tasks: Depth Estimation

Light field imaging extends the traditional photography by capturing both
spatial and angular distribution of light, which enables new capabilities,
including post-capture refocusing, post-capture aperture control, and depth
estimation from a single shot. Micro-lens array (MLA) based light field cameras
offer a cost-effective approach to capture light field. A major drawback of MLA
based light field cameras is low spatial resolution, which is due to the fact
that a single image sensor is shared to capture both spatial and angular
information. In this paper, we present a learning based light field enhancement
approach. Both spatial and angular resolution of captured light field is
enhanced using convolutional neural networks. The proposed method is tested
with real light field data captured with a Lytro light field camera, clearly
demonstrating spatial and angular resolution improvement.
Beyond Markov Logic: Efficient Mining of Prediction Rules in Large Graphs
Authors: Tommaso Soru, André Valdestilhas, Edgard Marx, Axel-Cyrille Ngonga Ngomo
Date: 2018-02-10 00:00:00
Tasks: Link Prediction

Graph representations of large knowledge bases may comprise billions of
edges. Usually built upon human-generated ontologies, several knowledge bases
do not feature declared ontological rules and are far from being complete.
Current rule mining approaches rely on schemata or store the graph in-memory,
which can be unfeasible for large graphs. In this paper, we introduce
HornConcerto, an algorithm to discover Horn clauses in large graphs without the
need of a schema. Using a standard fact-based confidence score, we can mine
close Horn rules having an arbitrary body size. We show that our method can
outperform existing approaches in terms of runtime and memory consumption and
mine high-quality rules for the link prediction task, achieving
state-of-the-art results on a widely-used benchmark. Moreover, we find that
rules alone can perform inference significantly faster than embedding-based
methods and achieve accuracies on link prediction comparable to
resource-demanding approaches such as Markov Logic Networks.
Scaling up Echo-State Networks with multiple light scattering
Authors: Jonathan Dong, Sylvain Gigan, Florent Krzakala, Gilles Wainrib
Date: 2016-09-15 00:00:00
Tasks: Time Series, Time Series Analysis

Echo-State Networks and Reservoir Computing have been studied for more than a
decade. They provide a simpler yet powerful alternative to Recurrent Neural
Networks, every internal weight is fixed and only the last linear layer is
trained. They involve many multiplications by dense random matrices. Very large
networks are difficult to obtain, as the complexity scales quadratically both
in time and memory. Here, we present a novel optical implementation of
Echo-State Networks using light-scattering media and a Digital Micromirror
Device. As a proof of concept, binary networks have been successfully trained
to predict the chaotic Mackey-Glass time series. This new method is fast, power
efficient and easily scalable to very large networks.
Fast Global Convergence via Landscape of Empirical Loss
Authors: Chao Qu, Yan Li, Huan Xu
Date: 2018-02-13 00:00:00
Tasks: General Classification

While optimizing convex objective (loss) functions has been a powerhouse for
machine learning for at least two decades, non-convex loss functions have
attracted fast growing interests recently, due to many desirable properties
such as superior robustness and classification accuracy, compared with their
convex counterparts. The main obstacle for non-convex estimators is that it is
in general intractable to find the optimal solution. In this paper, we study
the computational issues for some non-convex M-estimators. In particular, we
show that the stochastic variance reduction methods converge to the global
optimal with linear rate, by exploiting the statistical property of the
population loss. En route, we improve the convergence analysis for the batch
gradient method in \cite{mei2016landscape}.
Network Features Based Co-hyponymy Detection
Authors: Abhik Jana, Pawan Goyal
Date: 2018-02-13 00:00:00
Proceeding: LREC 2018 5
Tasks: Relation

Distinguishing lexical relations has been a long term pursuit in natural
language processing (NLP) domain. Recently, in order to detect lexical
relations like hypernymy, meronymy, co-hyponymy etc., distributional semantic
models are being used extensively in some form or the other. Even though a lot
of efforts have been made for detecting hypernymy relation, the problem of
co-hyponymy detection has been rarely investigated. In this paper, we are
proposing a novel supervised model where various network measures have been
utilized to identify co-hyponymy relation with high accuracy performing better
or at par with the state-of-the-art models.
tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data
Authors: Evangelos Papalexakis, Konstantinos Pelechrinis
Date: 2017-12-04 00:00:00
Tasks: Sports Analytics, Tensor Decomposition

During the past few years advancements in sports information systems and
technology has allowed us to collect a number of detailed spatio-temporal data
capturing various aspects of basketball. For example, shot charts, that is,
maps capturing locations of (made or missed) shots, and spatio-temporal
trajectories for all the players on the court can capture information about the
offensive and defensive tendencies and schemes of a team. Characterization of
these processes is important for player and team comparisons, pre-game
scouting, game preparation etc. Playing tendencies among teams have
traditionally been compared in a heuristic manner. Recently automated ways for
similar comparisons have appeared in the sports analytics literature. However,
these approaches are almost exclusively focused on the spatial distribution of
the underlying actions (usually shots taken), ignoring a multitude of other
parameters that can affect the action studied. In this work, we propose a
framework based on tensor decomposition for obtaining a set of prototype
spatio-temporal patterns based on the core spatiotemporal information and
contextual meta-data. The core of our framework is a 3D tensor X, whose
dimensions represent the entity under consideration (team, player, possession
etc.), the location on the court and time. We make use of the PARAFAC
decomposition and we decompose the tensor into several interpretable patterns,
that can be thought of as prototype patterns of the process examined (e.g.,
shot selection, offensive schemes etc.). We also introduce an approach for
choosing the number of components to be considered. Using the tensor
components, we can then express every entity as a weighted combination of these
components. The framework introduced in this paper can have further
applications in the work-flow of the basketball operations of a franchise,
which we also briefly discuss.
A generalized concept-cognitive learning: A machine learning viewpoint
Authors: Yunlong Mi, Yong Shi, Jinhai Li
Date: 2018-01-08 00:00:00
Tasks: BIG-bench Machine Learning

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has
attracted much attention from the communities of formal concept analysis,
granular computing and cognitive computing. However, the relationship among
cognitive computing (CC), concept-cognitive computing (CCC), CCL and
concept-cognitive learning model (CCLM) is not clearly described. To this end,
we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a
generalized concept-cognitive learning (GCCL) from the point of view of machine
learning. Finally, experiments on some data sets are conducted to verify the
feasibility of concept formation and concept-cognitive process of GCCL.
Few-shot Learning by Exploiting Visual Concepts within CNNs
Authors: Boyang Deng, Qing Liu, Siyuan Qiao, Alan Yuille
Date: 2017-11-22 00:00:00
Tasks: Few-Shot Learning

Convolutional neural networks (CNNs) are one of the driving forces for the
advancement of computer vision. Despite their promising performances on many
tasks, CNNs still face major obstacles on the road to achieving ideal machine
intelligence. One is that CNNs are complex and hard to interpret. Another is
that standard CNNs require large amounts of annotated data, which is sometimes
hard to obtain, and it is desirable to learn to recognize objects from few
examples. In this work, we address these limitations of CNNs by developing
novel, flexible, and interpretable models for few-shot learning. Our models are
based on the idea of encoding objects in terms of visual concepts (VCs), which
are interpretable visual cues represented by the feature vectors within CNNs.
We first adapt the learning of VCs to the few-shot setting, and then uncover
two key properties of feature encoding using VCs, which we call category
sensitivity and spatial pattern. Motivated by these properties, we present two
intuitive models for the problem of few-shot learning. Experiments show that
our models achieve competitive performances, while being more flexible and
interpretable than alternative state-of-the-art few-shot learning methods. We
conclude that using VCs helps expose the natural capability of CNNs for
few-shot learning.
Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation
Authors: Takatomo Kano, Sakriani Sakti, Satoshi Nakamura
Date: 2018-02-13 00:00:00
Tasks: Decoder, Machine Translation, speech-recognition, Speech Recognition, Translation

Sequence-to-sequence attentional-based neural network architectures have been
shown to provide a powerful model for machine translation and speech
recognition. Recently, several works have attempted to extend the models for
end-to-end speech translation task. However, the usefulness of these models
were only investigated on language pairs with similar syntax and word order
(e.g., English-French or English-Spanish). In this work, we focus on end-to-end
speech translation tasks on syntactically distant language pairs (e.g.,
English-Japanese) that require distant word reordering.
  To guide the encoder-decoder attentional model to learn this difficult
problem, we propose a structured-based curriculum learning strategy.
  Unlike conventional curriculum learning that gradually emphasizes difficult
data examples, we formalize learning strategies from easier network structures
to more difficult network structures. Here, we start the training with
end-to-end encoder-decoder for speech recognition or text-based machine
translation task then gradually move to end-to-end speech translation task. The
experiment results show that the proposed approach could provide significant
improvements in comparison with the one without curriculum learning.
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Authors: Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
Date: 2018-02-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, Diversity, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficient exploration remains a challenging research problem in reinforcement
learning, especially when an environment contains large state spaces, deceptive
local optima, or sparse rewards. To tackle this problem, we present a
diversity-driven approach for exploration, which can be easily combined with
both off- and on-policy reinforcement learning algorithms. We show that by
simply adding a distance measure to the loss function, the proposed methodology
significantly enhances an agent's exploratory behaviors, and thus preventing
the policy from being trapped in local optima. We further propose an adaptive
scaling method for stabilizing the learning process. Our experimental results
in Atari 2600 show that our method outperforms baseline approaches in several
tasks in terms of mean scores and exploration efficiency.
Sentence Boundary Detection for French with Subword-Level Information Vectors and Convolutional Neural Networks
Authors: Carlos-Emiliano González-Gallardo, Juan-Manuel Torres-Moreno
Date: 2018-02-13 00:00:00
Tasks: Binary Classification, Boundary Detection, Sentence

In this work we tackle the problem of sentence boundary detection applied to
French as a binary classification task ("sentence boundary" or "not sentence
boundary"). We combine convolutional neural networks with subword-level
information vectors, which are word embedding representations learned from
Wikipedia that take advantage of the words morphology; so each word is
represented as a bag of their character n-grams.
  We decide to use a big written dataset (French Gigaword) instead of standard
size transcriptions to train and evaluate the proposed architectures with the
intention of using the trained models in posterior real life ASR
transcriptions.
  Three different architectures are tested showing similar results; general
accuracy for all models overpasses 0.96. All three models have good F1 scores
reaching values over 0.97 regarding the "not sentence boundary" class. However,
the "sentence boundary" class reflects lower scores decreasing the F1 metric to
0.778 for one of the models.
  Using subword-level information vectors seem to be very effective leading to
conclude that the morphology of words encoded in the embeddings representations
behave like pixels in an image making feasible the use of convolutional neural
network architectures.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Authors: Jaehun Kim, Julián Urbano, Cynthia C. S. Liem, Alan Hanjalic
Date: 2018-02-12 00:00:00
Tasks: All, Deep Learning, Information Retrieval, Music Information Retrieval, Representation Learning, Retrieval, Transfer Learning

Inspired by the success of deploying deep learning in the fields of Computer
Vision and Natural Language Processing, this learning paradigm has also found
its way into the field of Music Information Retrieval. In order to benefit from
deep learning in an effective, but also efficient manner, deep transfer
learning has become a common approach. In this approach, it is possible to
reuse the output of a pre-trained neural network as the basis for a new
learning task. The underlying hypothesis is that if the initial and new
learning tasks show commonalities and are applied to the same type of input
data (e.g. music audio), the generated deep representation of the data is also
informative for the new task. Since, however, most of the networks used to
generate deep representations are trained using a single initial learning
source, their representation is unlikely to be informative for all possible
future tasks. In this paper, we present the results of our investigation of
what are the most important factors to generate deep representations for the
data and learning tasks in the music domain. We conducted this investigation
via an extensive empirical study that involves multiple learning sources, as
well as multiple deep learning architectures with varying levels of information
sharing between sources, in order to learn music representations. We then
validate these representations considering multiple target datasets for
evaluation. The results of our experiments yield several insights on how to
approach the design of methods for learning widely deployable deep data
representations in the music domain.
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow
Authors: Markus Hofinger, Thomas Pock, Thomas Moosbrugger
Date: 2018-02-13 00:00:00
Tasks: Optical Flow Estimation

Wood-composite materials are widely used today as they homogenize humidity
related directional deformations. Quantification of these deformations as
coefficients is important for construction and engineering and topic of current
research but still a manual process.
  This work introduces a novel computer vision approach that automatically
extracts these properties directly from scans of the wooden specimens, taken at
different humidity levels during the long lasting humidity conditioning
process. These scans are used to compute a humidity dependent deformation field
for each pixel, from which the desired coefficients can easily be calculated.
  The overall method includes automated registration of the wooden blocks,
numerical optimization to compute a variational optical flow field which is
further used to calculate dense strain fields and finally the engineering
coefficients and their variance throughout the wooden blocks. The methods
regularization is fully parameterizable which allows to model and suppress
artifacts due to surface appearance changes of the specimens from mold, cracks,
etc. that typically arise in the conditioning process.
On the Relative Succinctness of Sentential Decision Diagrams
Authors: Beate Bollig, Matthias Buttkus
Date: 2018-02-13 00:00:00

Sentential decision diagrams (SDDs) introduced by Darwiche in 2011 are a
promising representation type used in knowledge compilation. The relative
succinctness of representation types is an important subject in this area. The
aim of the paper is to identify which kind of Boolean functions can be
represented by SDDs of small size with respect to the number of variables the
functions are defined on. For this reason the sets of Boolean functions
representable by different representation types in polynomial size are
investigated and SDDs are compared with representation types from the classical
knowledge compilation map of Darwiche and Marquis. Ordered binary decision
diagrams (OBDDs) which are a popular data structure for Boolean functions are
one of these representation types. SDDs are more general than OBDDs by
definition but only recently, a Boolean function was presented with polynomial
SDD size but exponential OBDD size. This result is strengthened in several
ways. The main result is a quasipolynomial simulation of SDDs by equivalent
unambiguous nondeterministic OBDDs, a nondeterministic variant where there
exists exactly one accepting computation for each satisfying input. As a side
effect an open problem about the relative succinctness between SDDs and free
binary decision diagrams (FBDDs) which are more general than OBDDs is answered.