pwc-archive/papers-with-abstracts huggingface dataset:
Generalization Properties of hyper-RKHS and its Applications
Authors: Fanghui Liu, Lei Shi, Xiaolin Huang, Jie Yang, Johan A. K. Suykens
Date: 2018-09-26 00:00:00
Tasks: Learning Theory, regression

This paper generalizes regularized regression problems in a hyper-reproducing kernel Hilbert space (hyper-RKHS), illustrates its utility for kernel learning and out-of-sample extensions, and proves asymptotic convergence results for the introduced regression models in an approximation theory view. Algorithmically, we consider two regularized regression models with bivariate forms in this space, including kernel ridge regression (KRR) and support vector regression (SVR) endowed with hyper-RKHS, and further combine divide-and-conquer with Nystr\"{o}m approximation for scalability in large sample cases. This framework is general: the underlying kernel is learned from a broad class, and can be positive definite or not, which adapts to various requirements in kernel learning. Theoretically, we study the convergence behavior of regularized regression algorithms in hyper-RKHS and derive the learning rates, which goes beyond the classical analysis on RKHS due to the non-trivial independence of pairwise samples and the characterisation of hyper-RKHS. Experimentally, results on several benchmarks suggest that the employed framework is able to learn a general kernel function form an arbitrary similarity matrix, and thus achieves a satisfactory performance on classification tasks.
PeSOA: Penguins Search Optimisation Algorithm for Global Optimisation Problems
Authors: Youcef Gheraibia, Abdelouahab Moussaoui, Peng-Yeng Yin, Yiannis Papadopoulos, Smaine Maazouzi
Date: 2018-09-26 00:00:00

This paper develops Penguin search Optimisation Algorithm (PeSOA), a new
metaheuristic algorithm which is inspired by the foraging behaviours of
penguins. A population of penguins located in the solution space of the given
search and optimisation problem is divided into groups and tasked with finding
optimal solutions. The penguins of a group perform simultaneous dives and work
as a team to collaboratively feed on fish the energy content of which
corresponds to the fitness of candidate solutions. Fish stocks have higher
fitness and concentration near areas of solution optima and thus drive the
search. Penguins can migrate to other places if their original habitat lacks
food. We identify two forms of penguin communication both intra-group and
inter-group which are useful in designing intensification and diversification
strategies. An efficient intensification strategy allows fast convergence to a
local optimum, whereas an effective diversification strategy avoids cyclic
behaviour around local optima and explores more effectively the space of
potential solutions. The proposed PeSOA algorithm has been validated on a
well-known set of benchmark functions. Comparative performances with six other
nature-inspired metaheuristics show that the PeSOA performs favourably in these
tests. A run-time analysis shows that the performance obtained by the PeSOA is
very stable at any time of the evolution horizon, making the PeSOA a viable
approach for real world applications.
Learning random points from geometric graphs or orderings
Authors: Josep Diaz, Colin McDiarmid, Dieter Mitsche
Date: 2018-09-26 00:00:00

Suppose that there is a family of $n$ random points $X_v$ for $v \in V$, independently and uniformly distributed in the square $\left[-\sqrt{n}/2,\sqrt{n}/2\right]^2$ of area $n$. We do not see these points, but learn about them in one of the following two ways. Suppose first that we are given the corresponding random geometric graph $G$, where distinct vertices $u$ and $v$ are adjacent when the Euclidean distance $d_E(X_u,X_v)$ is at most $r$. If the threshold distance $r$ satisfies $n^{3/14} \ll r \ll n^{1/2}$, then the following holds with high probability. Given the graph $G$ (without any geometric information), in polynomial time we can approximately reconstruct the hidden embedding, in the sense that, `up to symmetries', for each vertex $v$ we find a point within distance about $r$ of $X_v$; that is, we find an embedding with `displacement' at most about $r$. Now suppose that, instead of being given the graph $G$, we are given, for each vertex $v$, the ordering of the other vertices by increasing Euclidean distance from $v$. Then, with high probability, in polynomial time we can find an embedding with the much smaller displacement error $O(\sqrt{\log n})$.
Underwater Caging and Capture for Autonomous Underwater Vehicles
Authors: Özer Özkahraman, Petter Ögren
Date: 2018-09-26 00:00:00

In this paper, we consider the problem of caging and eventual capture of an underwater entity using multiple Autonomous Underwater Vehicles (AUVs) in a 3D water volume We solve this problem both with and without taking bathymetry into account. Our proposed algorithm for range-limited sensing in 3D environments captures a finite-speed entity based on sparse and irregular observations. After an isolated initial sighting of the entity, the uncertainty of its whereabouts grows while deployment of the AUV system is underway. To contain the entity, an initial cage, or barrier of sensing footprints, is created around the initial sighting, using islands and other terrain as part of the cage if available. After the initial cage is established, the system waits for a second sighting, and the possible opportunity to create a smaller, shrinkable cage. This process continues until at some point it is possible to create this smaller cage, resulting in capture, meaning the entity is sensed directly and continuously. We present a set of algorithms for addressing the scenario above, and illustrate their performance on a set of examples. The proposed algorithm is a combination of solutions to the min-cut problem, the set cover problem, the linear bottleneck assignment problem and the Thomson problem.
Active Learning for Deep Object Detection
Authors: Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
Date: 2018-09-26 00:00:00
Tasks: Active Learning, Incremental Learning, Object, object-detection, Object Detection

The great success that deep models have achieved in the past is mainly owed
to large amounts of labeled training data. However, the acquisition of labeled
data for new tasks aside from existing benchmarks is both challenging and
costly. Active learning can make the process of labeling new data more
efficient by selecting unlabeled samples which, when labeled, are expected to
improve the model the most. In this paper, we combine a novel method of active
learning for object detection with an incremental learning scheme to enable
continuous exploration of new unlabeled datasets. We propose a set of
uncertainty-based active learning metrics suitable for most object detectors.
Furthermore, we present an approach to leverage class imbalances during sample
selection. All methods are evaluated systematically in a continuous exploration
context on the PASCAL VOC 2012 dataset.
A novel approach for venue recommendation using cross-domain techniques
Authors: Pablo Sánchez, Alejandro Bellogín
Date: 2018-09-26 00:00:00
Tasks: Recommendation Systems

Finding the next venue to be visited by a user in a specific city is an
interesting, but challenging, problem. Different techniques have been proposed,
combining collaborative, content, social, and geographical signals; however it
is not trivial to decide which tech- nique works best, since this may depend on
the data density or the amount of activity logged for each user or item. At the
same time, cross-domain strategies have been exploited in the recommender
systems literature when dealing with (very) sparse situations, such as those
inherently arising when recommendations are produced based on information from
a single city.
  In this paper, we address the problem of venue recommendation from a novel
perspective: applying cross-domain recommenda- tion techniques considering each
city as a different domain. We perform an experimental comparison of several
recommendation techniques in a temporal split under two conditions:
single-domain (only information from the target city is considered) and cross-
domain (information from many other cities is incorporated into the
recommendation algorithm). For the latter, we have explored two strategies to
transfer knowledge from one domain to another: testing the target city and
training a model with information of the k cities with more ratings or only
using the k closest cities.
  Our results show that, in general, applying cross-domain by proximity
increases the performance of the majority of the recom- menders in terms of
relevance. This is the first work, to the best of our knowledge, where so many
domains (eight) are combined in the tourism context where a temporal split is
used, and thus we expect these results could provide readers with an overall
picture of what can be achieved in a real-world environment.
Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient
Authors: Liu Liu, Xuanqing Liu, Cho-Jui Hsieh, DaCheng Tao
Date: 2018-09-26 00:00:00
Tasks: Second-order methods

Trust region and cubic regularization methods have demonstrated good
performance in small scale non-convex optimization, showing the ability to
escape from saddle points. Each iteration of these methods involves computation
of gradient, Hessian and function value in order to obtain the search direction
and adjust the radius or cubic regularization parameter. However, exactly
computing those quantities are too expensive in large-scale problems such as
training deep networks. In this paper, we study a family of stochastic trust
region and cubic regularization methods when gradient, Hessian and function
values are computed inexactly, and show the iteration complexity to achieve
$\epsilon$-approximate second-order optimality is in the same order with
previous work for which gradient and function values are computed exactly. The
mild conditions on inexactness can be achieved in finite-sum minimization using
random sampling. We show the algorithm performs well on training convolutional
neural networks compared with previous second-order methods.
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning
Authors: Bo Jiang, Doudou Lin
Date: 2018-09-26 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

Recently, graph convolutional network (GCN) has been widely used for
semi-supervised classification and deep feature representation on
graph-structured data. However, existing GCN generally fails to consider the
local invariance constraint in learning and representation process. That is, if
two data points Xi and Xj are close in the intrinsic geometry of the data
distribution, then their labels/representations should also be close to each
other. This is known as local invariance assumption which plays an essential
role in the development of various kinds of traditional algorithms, such as
dimensionality reduction and semi-supervised learning, in machine learning
area. To overcome this limitation, we introduce a graph Laplacian GCN (gLGCN)
approach for graph data representation and semi-supervised classification. The
proposed gLGCN model is capable of encoding both graph structure and node
features together while maintains the local invariance constraint naturally for
robust data representation and semi-supervised classification. Experiments show
the benefit of the benefits the proposed gLGCN network.
A Problem Reduction Approach for Visual Relationships Detection
Authors: Toshiyuki Fukuzawa
Date: 2018-09-26 00:00:00
Tasks: object-detection, Object Detection, Relationship Detection, Visual Relationship Detection

Identifying different objects (man and cup) is an important problem on its
own, but identifying the relationship between them (holding) is critical for
many real world use cases. This paper describes an approach to reduce a visual
relationship detection problem to object detection problems. The method was
applied to Google AI Open Images V4 Visual Relationship Track Challenge, which
was held in conjunction with 2018 European Conference on Computer Vision (ECCV
2018) and it finished as a prize winner. The challenge was to build an
algorithm that detects pairs of objects in particular relations: things like
"woman playing guitar," "beer on table," or "dog inside car.".
Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning
Authors: Rabindra Lamsal, Ayesha Choudhary
Date: 2018-09-26 00:00:00
Tasks: BIG-bench Machine Learning

Cricket, especially the Twenty20 format, has maximum uncertainty, where a single over can completely change the momentum of the game. With millions of people following the Indian Premier League (IPL), developing a model for predicting the outcome of its matches is a real-world problem. A cricket match depends upon various factors, and in this work, the factors which significantly influence the outcome of a Twenty20 cricket match are identified. Each player's performance in the field is considered to find out the overall weight (relative strength) of the teams. A multivariate regression based solution is proposed to calculate points for each player in the league and the overall weight of a team is computed based on the past performance of the players who have appeared most for the team. Finally, a dataset is modeled based on the identified seven factors which influence the outcome of an IPL match. Six machine learning models were trained and used for predicting the outcome of each 2018 IPL match, 15 minutes before the gameplay, immediately after the toss. Three of the trained models were seen to be correctly predicting more than 40 matches, with Multilayer Perceptron outperforming all other models with an impressive accuracy of 71.66%.
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
Authors: Daniel Seita, Nawid Jamali, Michael Laskey, Ajay Kumar Tanwani, Ron Berenstein, Prakash Baskaran, Soshi Iba, John Canny, Ken Goldberg
Date: 2018-09-26 00:00:00
Tasks: Decision Making, Deformable Object Manipulation, Transfer Learning

A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. We present a supervised deep transfer learning approach to locate pick points using depth images for invariance to color and texture. We consider the task of bed-making, where a robot sequentially grasps and pulls at pick points to increase blanket coverage. We perform physical experiments with two mobile manipulator robots, the Toyota HSR and the Fetch, and three blankets of different colors and textures. We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points. On a quarter-scale twin bed, a model trained with combined data from the two robots achieves 92% blanket coverage compared with 83% for the baseline and 95% for human supervisors. The model transfers to two novel blankets and achieves 93% coverage. Average coverage results of 92% for 193 beds suggest that transfer-invariant robot pick points on fabric can be effectively learned.
Deep contextualized word representations for detecting sarcasm and irony
Authors: Suzana Ilić, Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo
Date: 2018-09-26 00:00:00
Proceeding: WS 2018 10
Tasks: Common Sense Reasoning

Predicting context-dependent and non-literal utterances like sarcastic and
ironic expressions still remains a challenging task in NLP, as it goes beyond
linguistic patterns, encompassing common sense and shared knowledge as crucial
components. To capture complex morpho-syntactic features that can usually serve
as indicators for irony or sarcasm across dynamic contexts, we propose a model
that uses character-level vector representations of words, based on ELMo. We
test our model on 7 different datasets derived from 3 different data sources,
providing state-of-the-art performance in 6 of them, and otherwise offering
competitive results.
Evolving Agents for the Hanabi 2018 CIG Competition
Authors: Rodrigo Canaan, Haotian Shen, Ruben Rodriguez Torrado, Julian Togelius, Andy Nealen, Stefan Menzel
Date: 2018-09-26 00:00:00

Hanabi is a cooperative card game with hidden information that has won
important awards in the industry and received some recent academic attention. A
two-track competition of agents for the game will take place in the 2018 CIG
conference. In this paper, we develop a genetic algorithm that builds
rule-based agents by determining the best sequence of rules from a fixed rule
set to use as strategy. In three separate experiments, we remove human
assumptions regarding the ordering of rules, add new, more expressive rules to
the rule set and independently evolve agents specialized at specific game
sizes. As result, we achieve scores superior to previously published research
for the mirror and mixed evaluation of agents.
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Authors: Ruichao Xiao, Wenxiu Sun, Jiahao Pang, Qiong Yan, Jimmy Ren
Date: 2018-09-26 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

With the developments of dual-lens camera modules,depth information
representing the third dimension of thecaptured scenes becomes available for
smartphones. It isestimated by stereo matching algorithms, taking as input
thetwo views captured by dual-lens cameras at slightly differ-ent viewpoints.
Depth-of-field rendering (also be referred toas synthetic defocus or bokeh) is
one of the trending depth-based applications. However, to achieve fast depth
estima-tion on smartphones, the stereo pairs need to be rectified inthe first
place. In this paper, we propose a cost-effective so-lution to perform stereo
rectification for dual-lens camerascalled direct self-rectification, short for
DSR1. It removesthe need of individual offline calibration for every pair
ofdual-lens cameras. In addition, the proposed solution isrobust to the slight
movements, e.g., due to collisions, ofthe dual-lens cameras after fabrication.
Different with ex-isting self-rectification approaches, our approach
computesthe homography in a novel way with zero geometric distor-tions
introduced to the master image. It is achieved by di-rectly minimizing the
vertical displacements of correspond-ing points between the original master
image and the trans-formed slave image. Our method is evaluated on both
real-istic and synthetic stereo image pairs, and produces supe-rior results
compared to the calibrated rectification or otherself-rectification approaches
Towards Game-based Metrics for Computational Co-creativity
Authors: Rodrigo Canaan, Stefan Menzel, Julian Togelius, Andy Nealen
Date: 2018-09-26 00:00:00

We propose the following question: what game-like interactive system would
provide a good environment for measuring the impact and success of a
co-creative, cooperative agent? Creativity is often formulated in terms of
novelty, value, surprise and interestingness. We review how these concepts are
measured in current computational intelligence research and provide a mapping
from modern electronic and tabletop games to open research problems in
mixed-initiative systems and computational co-creativity. We propose
application scenarios for future research, and a number of metrics under which
the performance of cooperative agents in these environments will be evaluated.
PhotoShape: Photorealistic Materials for Large-Scale Shape Collections
Authors: Keunhong Park, Konstantinos Rematas, Ali Farhadi, Steven M. Seitz
Date: 2018-09-26 00:00:00

Existing online 3D shape repositories contain thousands of 3D models but lack
photorealistic appearance. We present an approach to automatically assign
high-quality, realistic appearance models to large scale 3D shape collections.
The key idea is to jointly leverage three types of online data -- shape
collections, material collections, and photo collections, using the photos as
reference to guide assignment of materials to shapes. By generating a large
number of synthetic renderings, we train a convolutional neural network to
classify materials in real photos, and employ 3D-2D alignment techniques to
transfer materials to different parts of each shape model. Our system produces
photorealistic, relightable, 3D shapes (PhotoShapes).
Confidence Inference for Focused Learning in Stereo Matching
Authors: Ruichao Xiao, Wenxiu Sun, Chengxi Yang
Date: 2018-09-25 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

In this paper, we present confidence inference approachin an unsupervised way
in stereo matching. Deep Neu-ral Networks (DNNs) have recently been achieving
state-of-the-art performance. However, it is often hard to tellwhether the
trained model was making sensible predictionsor just guessing at random. To
address this problem, westart from a probabilistic interpretation of theL1loss
usedin stereo matching, which inherently assumes an indepen-dent and identical
(aka i.i.d.) Laplacian distribution. Weshow that with the newly introduced
dense confidence map,the identical assumption is relaxed. Intuitively, the
vari-ance in the Laplacian distribution is large for low confidentpixels while
small for high-confidence pixels. In practice,the network learns
toattenuatelow-confidence pixels (e.g.,noisy input, occlusions, featureless
regions) andfocusonhigh-confidence pixels. Moreover, it can be observed
fromexperiments that the focused learning is very helpful in find-ing a better
convergence state of the trained model, reduc-ing over-fitting on a given
dataset.
Map matching when the map is wrong: Efficient vehicle tracking on- and off-road for map learning
Authors: James Murphy, Yuanyuan Pao, Albert Yuen
Date: 2018-09-25 00:00:00

Given a sequence of possibly sparse and noisy GPS traces and a map of the road network, map matching algorithms can infer the most accurate trajectory on the road network. However, if the road network is wrong (for example due to missing or incorrectly mapped roads, missing parking lots, misdirected turn restrictions or misdirected one-way streets) standard map matching algorithms fail to reconstruct the correct trajectory. In this paper, an algorithm to tracking vehicles able to move both on and off the known road network is formulated. It efficiently unifies existing hidden Markov model (HMM) approaches for map matching and standard free-space tracking methods (e.g. Kalman smoothing) in a principled way. The algorithm is a form of interacting multiple model (IMM) filter subject to an additional assumption on the type of model interaction permitted, termed here as semi-interacting multiple model (sIMM) filter. A forward filter (suitable for real-time tracking) and backward MAP sampling step (suitable for MAP trajectory inference and map matching) are described. The framework set out here is agnostic to the specific tracking models used, and makes clear how to replace these components with others of a similar type. In addition to avoiding generating misleading map matching trajectories, this algorithm can be applied to learn map features by detecting unmapped or incorrectly mapped roads and parking lots, incorrectly mapped turn restrictions and road directions.