pwc-archive/papers-with-abstracts huggingface dataset:
Retinal Vessel Segmentation Using A New Topological Method
Authors: Martin Brooks
Date: 2016-08-03 00:00:00
Tasks: Retinal Vessel Segmentation, Segmentation

A novel topological segmentation of retinal images represents blood vessels
as connected regions in the continuous image plane, having shape-related
analytic and geometric properties. This paper presents topological segmentation
results from the DRIVE retinal image database.
Learning to Rank for Synthesizing Planning Heuristics
Authors: Caelan Reed Garrett, Leslie Pack Kaelbling, Tomas Lozano-Perez
Date: 2016-08-03 00:00:00
Tasks: Learning-To-Rank, regression

We investigate learning heuristics for domain-specific planning. Prior work
framed learning a heuristic as an ordinary regression problem. However, in a
greedy best-first search, the ordering of states induced by a heuristic is more
indicative of the resulting planner's performance than mean squared error.
Thus, we instead frame learning a heuristic as a learning to rank problem which
we solve using a RankSVM formulation. Additionally, we introduce new methods
for computing features that capture temporal interactions in an approximate
plan. Our experiments on recent International Planning Competition problems
show that the RankSVM learned heuristics outperform both the original
heuristics and heuristics learned through ordinary regression.
A Physical Metaphor to Study Semantic Drift
Authors: Sándor Darányi, Peter Wittek, Konstantinos Konstantinidis, Symeon Papadopoulos, Efstratios Kontopoulos
Date: 2016-08-03 00:00:00

In accessibility tests for digital preservation, over time we experience
drifts of localized and labelled content in statistical models of evolving
semantics represented as a vector field. This articulates the need to detect,
measure, interpret and model outcomes of knowledge dynamics. To this end we
employ a high-performance machine learning algorithm for the training of
extremely large emergent self-organizing maps for exploratory data analysis.
The working hypothesis we present here is that the dynamics of semantic drifts
can be modeled on a relaxed version of Newtonian mechanics called social
mechanics. By using term distances as a measure of semantic relatedness vs.
their PageRank values indicating social importance and applied as variable
`term mass', gravitation as a metaphor to express changes in the semantic
content of a vector field lends a new perspective for experimentation. From
`term gravitation' over time, one can compute its generating potential whose
fluctuations manifest modifications in pairwise term similarity vs. social
importance, thereby updating Osgood's semantic differential. The dataset
examined is the public catalog metadata of Tate Galleries, London.
Learning Online Alignments with Continuous Rewards Policy Gradient
Authors: Yuping Luo, Chung-Cheng Chiu, Navdeep Jaitly, Ilya Sutskever
Date: 2016-08-03 00:00:00
Tasks: Machine Translation, Question Answering, speech-recognition, Speech Recognition, Translation, valid

Sequence-to-sequence models with soft attention had significant success in
machine translation, speech recognition, and question answering. Though capable
and easy to use, they require that the entirety of the input sequence is
available at the beginning of inference, an assumption that is not valid for
instantaneous translation and speech recognition. To address this problem, we
present a new method for solving sequence-to-sequence problems using hard
online alignments instead of soft offline alignments. The online alignments
model is able to start producing outputs without the need to first process the
entire input sequence. A highly accurate online sequence-to-sequence model is
useful because it can be used to build an accurate voice-based instantaneous
translator. Our model uses hard binary stochastic decisions to select the
timesteps at which outputs will be produced. The model is trained to produce
these stochastic decisions using a standard policy gradient method. In our
experiments, we show that this model achieves encouraging performance on TIMIT
and Wall Street Journal (WSJ) speech recognition datasets.
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Authors: Johannes Stegmaier, Ralf Mikut
Date: 2016-08-03 00:00:00

Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.
Fast and Simple Optimization for Poisson Likelihood Models
Authors: Niao He, Zaid Harchaoui, Yichen Wang, Le Song
Date: 2016-08-03 00:00:00
Tasks: Time Series, Time Series Analysis

Poisson likelihood models have been prevalently used in imaging, social
networks, and time series analysis. We propose fast, simple,
theoretically-grounded, and versatile, optimization algorithms for Poisson
likelihood modeling. The Poisson log-likelihood is concave but not
Lipschitz-continuous. Since almost all gradient-based optimization algorithms
rely on Lipschitz-continuity, optimizing Poisson likelihood models with a
guarantee of convergence can be challenging, especially for large-scale
problems.
  We present a new perspective allowing to efficiently optimize a wide range of
penalized Poisson likelihood objectives. We show that an appropriate saddle
point reformulation enjoys a favorable geometry and a smooth structure.
Therefore, we can design a new gradient-based optimization algorithm with
$O(1/t)$ convergence rate, in contrast to the usual $O(1/\sqrt{t})$ rate of
non-smooth minimization alternatives. Furthermore, in order to tackle problems
with large samples, we also develop a randomized block-decomposition variant
that enjoys the same convergence rate yet more efficient iteration cost.
  Experimental results on several point process applications including social
network estimation and temporal recommendation show that the proposed algorithm
and its randomized block variant outperform existing methods both on synthetic
and real-world datasets.
Interactive Image Segmentation Using Constrained Dominant Sets
Authors: Eyasu Zemene, Marcello Pelillo
Date: 2016-08-01 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

We propose a new approach to interactive image segmentation based on some
properties of a family of quadratic optimization problems related to dominant
sets, a well-known graph-theoretic notion of a cluster which generalizes the
concept of a maximal clique to edge-weighted graphs. In particular, we show
that by properly controlling a regularization parameter which determines the
structure and the scale of the underlying problem, we are in a position to
extract groups of dominant-set clusters which are constrained to contain
user-selected elements. The resulting algorithm can deal naturally with any
type of input modality, including scribbles, sloppy contours, and bounding
boxes, and is able to robustly handle noisy annotations on the part of the
user. Experiments on standard benchmark datasets show the effectiveness of our
approach as compared to state-of-the-art algorithms on a variety of natural
images under several input conditions.
Distributed Optimization with Arbitrary Local Solvers
Authors: Chenxin Ma, Jakub Konečný, Martin Jaggi, Virginia Smith, Michael. I. Jordan, Peter Richtárik, Martin Takáč
Date: 2015-12-13 00:00:00
Tasks: Distributed Optimization

With the growth of data and necessity for distributed optimization methods,
solvers that work well on a single machine must be re-designed to leverage
distributed computation. Recent work in this area has been limited by focusing
heavily on developing highly specific methods for the distributed environment.
These special-purpose methods are often unable to fully leverage the
competitive performance of their well-tuned and customized single machine
counterparts. Further, they are unable to easily integrate improvements that
continue to be made to single machine methods. To this end, we present a
framework for distributed optimization that both allows the flexibility of
arbitrary solvers to be used on each (single) machine locally, and yet
maintains competitive performance against other state-of-the-art
special-purpose distributed methods. We give strong primal-dual convergence
rate guarantees for our framework that hold for arbitrary local solvers. We
demonstrate the impact of local solver selection both theoretically and in an
extensive experimental comparison. Finally, we provide thorough implementation
details for our framework, highlighting areas for practical performance gains.
Invariant Models for Causal Transfer Learning
Authors: Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters
Date: 2015-07-19 00:00:00
Tasks: Domain Generalization, Transfer Learning

Methods of transfer learning try to combine knowledge from several related
tasks (or domains) to improve performance on a test task. Inspired by causal
methodology, we relax the usual covariate shift assumption and assume that it
holds true for a subset of predictor variables: the conditional distribution of
the target variable given this subset of predictors is invariant over all
tasks. We show how this assumption can be motivated from ideas in the field of
causality. We focus on the problem of Domain Generalization, in which no
examples from the test task are observed. We prove that in an adversarial
setting using this subset for prediction is optimal in Domain Generalization;
we further provide examples, in which the tasks are sufficiently diverse and
the estimator therefore outperforms pooling the data, even on average. If
examples from the test task are available, we also provide a method to transfer
knowledge from the training tasks and exploit all available features for
prediction. However, we provide no guarantees for this method. We introduce a
practical method which allows for automatic inference of the above subset and
provide corresponding code. We present results on synthetic data sets and a
gene deletion data set.
Improving Quality of Hierarchical Clustering for Large Data Series
Authors: Manuel R. Ciosici
Date: 2016-08-03 00:00:00
Tasks: Clustering

Brown clustering is a hard, hierarchical, bottom-up clustering of words in a
vocabulary. Words are assigned to clusters based on their usage pattern in a
given corpus. The resulting clusters and hierarchical structure can be used in
constructing class-based language models and for generating features to be used
in NLP tasks. Because of its high computational cost, the most-used version of
Brown clustering is a greedy algorithm that uses a window to restrict its
search space. Like other clustering algorithms, Brown clustering finds a
sub-optimal, but nonetheless effective, mapping of words to clusters. Because
of its ability to produce high-quality, human-understandable cluster, Brown
clustering has seen high uptake the NLP research community where it is used in
the preprocessing and feature generation steps.
  Little research has been done towards improving the quality of Brown
clusters, despite the greedy and heuristic nature of the algorithm. The
approaches tried so far have focused on: studying the effect of the
initialisation in a similar algorithm; tuning the parameters used to define the
desired number of clusters and the behaviour of the algorithm; and including a
separate parameter to differentiate the window from the desired number of
clusters. However, some of these approaches have not yielded significant
improvements in cluster quality.
  In this thesis, a close analysis of the Brown algorithm is provided,
revealing important under-specifications and weaknesses in the original
algorithm. These have serious effects on cluster quality and reproducibility of
research using Brown clustering. In the second part of the thesis, two
modifications are proposed. Finally, a thorough evaluation is performed,
considering both the optimization criterion of Brown clustering and the
performance of the resulting class-based language models.
Learning a Driving Simulator
Authors: Eder Santana, George Hotz
Date: 2016-08-03 00:00:00
Tasks: Self-Driving Cars, Video Prediction

Comma.ai's approach to Artificial Intelligence for self-driving cars is based
on an agent that learns to clone driver behaviors and plans maneuvers by
simulating future events in the road. This paper illustrates one of our
research approaches for driving simulation. One where we learn to simulate.
Here we investigate variational autoencoders with classical and learned cost
functions using generative adversarial networks for embedding road frames.
Afterwards, we learn a transition model in the embedded space using action
conditioned Recurrent Neural Networks. We show that our approach can keep
predicting realistic looking video for several frames despite the transition
model being optimized without a cost function in the pixel space.
A Novel Approach for Data-Driven Automatic Site Recommendation and Selection
Authors: Sebastian Baumbach, Frank Wittich, Florian Sachs, Sheraz Ahmed, Andreas Dengel
Date: 2016-08-03 00:00:00

This paper presents a novel, generic, and automatic method for data-driven
site selection. Site selection is one of the most crucial and important
decisions made by any company. Such a decision depends on various factors of
sites, including socio-economic, geographical, ecological, as well as specific
requirements of companies. The existing approaches for site selection (commonly
used by economists) are manual, subjective, and not scalable, especially to Big
Data. The presented method for site selection is robust, efficient, scalable,
and is capable of handling challenges emerging in Big Data. To assess the
effectiveness of the presented method, it is evaluated on real data (collected
from Federal Statistical Office of Germany) of around 200 influencing factors
which are considered by economists for site selection of Supermarkets in
Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big
overlap (86.4 \%) between the sites of existing supermarkets and the sites
recommended by the presented method. In addition, the method also recommends
many sites (328) for supermarket where a store should be opened.
Probabilistic Reasoning via Deep Learning: Neural Association Models
Authors: Quan Liu, Hui Jiang, Andrew Evdokimov, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu
Date: 2016-03-24 00:00:00
Tasks: Deep Learning, Natural Language Inference, Natural Language Understanding, Transfer Learning, Triple Classification

In this paper, we propose a new deep learning approach, called neural
association model (NAM), for probabilistic reasoning in artificial
intelligence. We propose to use neural networks to model association between
any two events in a domain. Neural networks take one event as input and compute
a conditional probability of the other event to model how likely these two
events are to be associated. The actual meaning of the conditional
probabilities varies between applications and depends on how the models are
trained. In this work, as two case studies, we have investigated two NAM
structures, namely deep neural networks (DNN) and relation-modulated neural
nets (RMNN), on several probabilistic reasoning tasks in AI, including
recognizing textual entailment, triple classification in multi-relational
knowledge bases and commonsense reasoning. Experimental results on several
popular datasets derived from WordNet, FreeBase and ConceptNet have all
demonstrated that both DNNs and RMNNs perform equally well and they can
significantly outperform the conventional methods available for these reasoning
tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer,
where a pre-trained model can be quickly extended to an unseen relation after
observing only a few training samples. To further prove the effectiveness of
the proposed models, in this work, we have applied NAMs to solving challenging
Winograd Schema (WS) problems. Experiments conducted on a set of WS problems
prove that the proposed models have the potential for commonsense reasoning.
A balanced k-means algorithm for weighted point sets
Authors: Steffen Borgwardt, Andreas Brieden, Peter Gritzmann
Date: 2013-08-19 00:00:00
Tasks: Clustering

The classical $k$-means algorithm for partitioning $n$ points in
$\mathbb{R}^d$ into $k$ clusters is one of the most popular and widely spread
clustering methods. The need to respect prescribed lower bounds on the cluster
sizes has been observed in many scientific and business applications.
  In this paper, we present and analyze a generalization of $k$-means that is
capable of handling weighted point sets and prescribed lower and upper bounds
on the cluster sizes. We call it weight-balanced $k$-means. The key difference
to existing models lies in the ability to handle the combination of weighted
point sets with prescribed bounds on the cluster sizes. This imposes the need
to perform partial membership clustering, and leads to significant differences.
  For example, while finite termination of all $k$-means variants for
unweighted point sets is a simple consequence of the existence of only finitely
many partitions of a given set of points, the situation is more involved for
weighted point sets, as there are infinitely many partial membership
clusterings. Using polyhedral theory, we show that the number of iterations of
weight-balanced $k$-means is bounded above by $n^{O(dk)}$, so in particular it
is polynomial for fixed $k$ and $d$. This is similar to the known worst-case
upper bound for classical $k$-means for unweighted point sets and unrestricted
cluster sizes, despite the much more general framework. We conclude with the
discussion of some additional favorable properties of our method.
Deep Shading: Convolutional Neural Networks for Screen-Space Shading
Authors: Oliver Nalbach, Elena Arabadzhiyska, Dushyant Mehta, Hans-Peter Seidel, Tobias Ritschel
Date: 2016-03-19 00:00:00
Tasks: Image Generation

In computer vision, convolutional neural networks (CNNs) have recently
achieved new levels of performance for several inverse problems where RGB pixel
appearance is mapped to attributes such as positions, normals or reflectance.
In computer graphics, screen-space shading has recently increased the visual
quality in interactive image synthesis, where per-pixel attributes such as
positions, normals or reflectance of a virtual 3D scene are converted into RGB
pixel appearance, enabling effects like ambient occlusion, indirect light,
scattering, depth-of-field, motion blur, or anti-aliasing. In this paper we
consider the diagonal problem: synthesizing appearance from given per-pixel
attributes using a CNN. The resulting Deep Shading simulates various
screen-space effects at competitive quality and speed while not being
programmed by human experts but learned from example images.
DOLPHIn - Dictionary Learning for Phase Retrieval
Authors: Andreas M. Tillmann, Yonina C. Eldar, Julien Mairal
Date: 2016-02-06 00:00:00
Tasks: Dictionary Learning, Retrieval

We propose a new algorithm to learn a dictionary for reconstructing and
sparsely encoding signals from measurements without phase. Specifically, we
consider the task of estimating a two-dimensional image from squared-magnitude
measurements of a complex-valued linear transformation of the original image.
Several recent phase retrieval algorithms exploit underlying sparsity of the
unknown signal in order to improve recovery performance. In this work, we
consider such a sparse signal prior in the context of phase retrieval, when the
sparsifying dictionary is not known in advance. Our algorithm jointly
reconstructs the unknown signal - possibly corrupted by noise - and learns a
dictionary such that each patch of the estimated image can be sparsely
represented. Numerical experiments demonstrate that our approach can obtain
significantly better reconstructions for phase retrieval problems with noise
than methods that cannot exploit such "hidden" sparsity. Moreover, on the
theoretical side, we provide a convergence result for our method.
Autonomous Grounding of Visual Field Experience through Sensorimotor Prediction
Authors: Alban Laflaquière
Date: 2016-08-03 00:00:00

In a developmental framework, autonomous robots need to explore the world and
learn how to interact with it. Without an a priori model of the system, this
opens the challenging problem of having robots master their interface with the
world: how to perceive their environment using their sensors, and how to act in
it using their motors. The sensorimotor approach of perception claims that a
naive agent can learn to master this interface by capturing regularities in the
way its actions transform its sensory inputs. In this paper, we apply such an
approach to the discovery and mastery of the visual field associated with a
visual sensor. A computational model is formalized and applied to a simulated
system to illustrate the approach.
Proceedings of the Second Summer School on Argumentation: Computational and Linguistic Perspectives (SSA'16)
Authors: Sarah A. Gaggl, Matthias Thimm
Date: 2016-08-03 00:00:00

This volume contains the thesis abstracts presented at the Second Summer
School on Argumentation: Computational and Linguistic Perspectives (SSA'2016)
held on September 8-12 in Potsdam, Germany.
In Love With a Robot: the Dawn of Machine-To-Machine Marketing
Authors: Emil Kotomin
Date: 2013-02-18 00:00:00
Tasks: Marketing

The article looks at mass market artificial intelligence tools in the context
of their ever-growing sophistication, availability and market penetration. The
subject is especially relevant today for these exact reasons - if a few years
ago AI was the subject of high tech research and science fiction novels, today,
we increasingly rely on cloud robotics to cater to our daily needs - to trade
stock, predict weather, manage diaries, find friends and buy presents online.