pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task
Authors: Nan Ding, Sebastian Goodman, Fei Sha, Radu Soricut
Date: 2016-12-22 00:00:00
Tasks: Image Captioning, Multi-Task Learning, Reading Comprehension

We introduce a new multi-modal task for computer systems, posed as a combined
vision-language comprehension challenge: identifying the most suitable text
describing a scene, given several similar options. Accomplishing the task
entails demonstrating comprehension beyond just recognizing "keywords" (or
key-phrases) and their corresponding visual concepts. Instead, it requires an
alignment between the representations of the two modalities that achieves a
visually-grounded "understanding" of various linguistic elements and their
dependencies. This new task also admits an easy-to-compute and well-studied
metric: the accuracy in detecting the true target among the decoys.
  The paper makes several contributions: an effective and extensible mechanism
for generating decoys from (human-created) image captions; an instance of
applying this mechanism, yielding a large-scale machine comprehension dataset
(based on the COCO images and captions) that we make publicly available; human
evaluation results on this dataset, informing a performance upper-bound; and
several baseline and competitive learning approaches that illustrate the
utility of the proposed task and dataset in advancing both image and language
comprehension. We also show that, in a multi-task learning setting, the
performance on the proposed task is positively correlated with the end-to-end
task of image captioning.
Robust Contextual Outlier Detection: Where Context Meets Sparsity
Authors: Jiongqian Liang, Srinivasan Parthasarathy
Date: 2016-07-28 00:00:00
Tasks: Outlier Detection

Outlier detection is a fundamental data science task with applications
ranging from data cleaning to network security. Given the fundamental nature of
the task, this has been the subject of much research. Recently, a new class of
outlier detection algorithms has emerged, called {\it contextual outlier
detection}, and has shown improved performance when studying anomalous behavior
in a specific context. However, as we point out in this article, such
approaches have limited applicability in situations where the context is sparse
(i.e. lacking a suitable frame of reference). Moreover, approaches developed to
date do not scale to large datasets. To address these problems, here we propose
a novel and robust approach alternative to the state-of-the-art called RObust
Contextual Outlier Detection (ROCOD). We utilize a local and global behavioral
model based on the relevant contexts, which is then integrated in a natural and
robust fashion. We also present several optimizations to improve the
scalability of the approach. We run ROCOD on both synthetic and real-world
datasets and demonstrate that it outperforms other competitive baselines on the
axes of efficacy and efficiency (40X speedup compared to modern contextual
outlier detection methods). We also drill down and perform a fine-grained
analysis to shed light on the rationale for the performance gains of ROCOD and
reveal its effectiveness when handling objects with sparse contexts.
Temporal Feature Selection on Networked Time Series
Authors: Haishuai Wang, Jia Wu, Peng Zhang, Chengqi Zhang
Date: 2016-12-20 00:00:00
Tasks: feature selection, Time Series, Time Series Analysis, Time Series Classification

This paper formulates the problem of learning discriminative features
(\textit{i.e.,} segments) from networked time series data considering the
linked information among time series. For example, social network users are
considered to be social sensors that continuously generate social signals
(tweets) represented as a time series. The discriminative segments are often
referred to as \emph{shapelets} in a time series. Extracting shapelets for time
series classification has been widely studied. However, existing works on
shapelet selection assume that the time series are independent and identically
distributed (i.i.d.). This assumption restricts their applications to social
networked time series analysis, since a user's actions can be correlated to
his/her social affiliations. In this paper we propose a new Network Regularized
Least Squares (NetRLS) feature selection model that combines typical time
series data and user network data for analysis. Experiments on real-world
networked time series Twitter and DBLP data demonstrate the performance of the
proposed method. NetRLS performs better than LTS, the state-of-the-art time
series feature selection approach, on real-world data.
BaTFLED: Bayesian Tensor Factorization Linked to External Data
Authors: Nathan H Lazar, Mehmet Gönen, Kemal Sönmez
Date: 2016-12-09 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

The vast majority of current machine learning algorithms are designed to
predict single responses or a vector of responses, yet many types of response
are more naturally organized as matrices or higher-order tensor objects where
characteristics are shared across modes. We present a new machine learning
algorithm BaTFLED (Bayesian Tensor Factorization Linked to External Data) that
predicts values in a three-dimensional response tensor using input features for
each of the dimensions. BaTFLED uses a probabilistic Bayesian framework to
learn projection matrices mapping input features for each mode into latent
representations that multiply to form the response tensor. By utilizing a
Tucker decomposition, the model can capture weights for interactions between
latent factors for each mode in a small core tensor. Priors that encourage
sparsity in the projection matrices and core tensor allow for feature selection
and model regularization. This method is shown to far outperform elastic net
and neural net models on 'cold start' tasks from data simulated in a three-mode
structure. Additionally, we apply the model to predict dose-response curves in
a panel of breast cancer cell lines treated with drug compounds that was used
as a Dialogue for Reverse Engineering Assessments and Methods (DREAM)
challenge.
Boosting Joint Models for Longitudinal and Time-to-Event Data
Authors: Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein, Thomas Kneib, Tania Pressler, Matthias Schmid, Andreas Mayr
Date: 2016-09-09 00:00:00
Tasks: Variable Selection

Joint Models for longitudinal and time-to-event data have gained a lot of
attention in the last few years as they are a helpful technique to approach
common a data structure in clinical studies where longitudinal outcomes are
recorded alongside event times. Those two processes are often linked and the
two outcomes should thus be modeled jointly in order to prevent the potential
bias introduced by independent modelling. Commonly, joint models are estimated
in likelihood based expectation maximization or Bayesian approaches using
frameworks where variable selection is problematic and which do not immediately
work for high-dimensional data. In this paper, we propose a boosting algorithm
tackling these challenges by being able to simultaneously estimate predictors
for joint models and automatically select the most influential variables even
in high-dimensional data situations. We analyse the performance of the new
algorithm in a simulation study and apply it to the Danish cystic fibrosis
registry which collects longitudinal lung function data on patients with cystic
fibrosis together with data regarding the onset of pulmonary infections. This
is the first approach to combine state-of-the art algorithms from the field of
machine-learning with the model class of joint models, providing a fully
data-driven mechanism to select variables and predictor effects in a unified
framework of boosting joint models.
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
Authors: Jiwei Li, Will Monroe, Dan Jurafsky
Date: 2016-11-25 00:00:00
Tasks: Abstractive Text Summarization, Diversity, Machine Translation, Reinforcement Learning, Reinforcement Learning (RL), Reranking, Response Generation, Translation

In this paper, we propose a simple, fast decoding algorithm that fosters
diversity in neural generation. The algorithm modifies the standard beam search
algorithm by adding an inter-sibling ranking penalty, favoring choosing
hypotheses from diverse parents. We evaluate the proposed model on the tasks of
dialogue response generation, abstractive summarization and machine
translation. We find that diverse decoding helps across all tasks, especially
those for which reranking is needed.
  We further propose a variation that is capable of automatically adjusting its
diversity decoding rates for different inputs using reinforcement learning
(RL). We observe a further performance boost from this RL technique. This paper
includes material from the unpublished script "Mutual Information and Diverse
Decoding Improve Neural Machine Translation" (Li and Jurafsky, 2016).
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Authors: Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson
Date: 2016-12-22 00:00:00
Tasks: Language Modeling, Language Modelling

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep
learning model able to predict structured sequences of data. Precisely, GCRN is
a generalization of classical recurrent neural networks (RNN) to data
structured by an arbitrary graph. Such structured sequences can represent
series of frames in videos, spatio-temporal measurements on a network of
sensors, or random walks on a vocabulary graph for natural language modeling.
The proposed model combines convolutional neural networks (CNN) on graphs to
identify spatial structures and RNN to find dynamic patterns. We study two
possible architectures of GCRN, and apply the models to two practical problems:
predicting moving MNIST data, and modeling natural language with the Penn
Treebank dataset. Experiments show that exploiting simultaneously graph spatial
and dynamic information about data can improve both precision and learning
speed.
A Riemannian Network for SPD Matrix Learning
Authors: Zhiwu Huang, Luc van Gool
Date: 2016-08-15 00:00:00

Symmetric Positive Definite (SPD) matrix learning methods have become popular
in many image and video processing tasks, thanks to their ability to learn
appropriate statistical representations while respecting Riemannian geometry of
underlying SPD manifolds. In this paper we build a Riemannian network
architecture to open up a new direction of SPD matrix non-linear learning in a
deep model. In particular, we devise bilinear mapping layers to transform input
SPD matrices to more desirable SPD matrices, exploit eigenvalue rectification
layers to apply a non-linear activation function to the new SPD matrices, and
design an eigenvalue logarithm layer to perform Riemannian computing on the
resulting SPD matrices for regular output layers. For training the proposed
deep network, we exploit a new backpropagation with a variant of stochastic
gradient descent on Stiefel manifolds to update the structured connection
weights and the involved SPD matrix data. We show through experiments that the
proposed SPD matrix network can be simply trained and outperform existing SPD
matrix learning and state-of-the-art methods in three typical visual
classification tasks.
Dependency Parsing as Head Selection
Authors: Xingxing Zhang, Jianpeng Cheng, Mirella Lapata
Date: 2016-06-03 00:00:00
Proceeding: EACL 2017 4
Tasks: Dependency Parsing, Sentence

Conventional graph-based dependency parsers guarantee a tree structure both
during training and inference. Instead, we formalize dependency parsing as the
problem of independently selecting the head of each word in a sentence. Our
model which we call \textsc{DeNSe} (as shorthand for {\bf De}pendency {\bf
N}eural {\bf Se}lection) produces a distribution over possible heads for each
word using features obtained from a bidirectional recurrent neural network.
Without enforcing structural constraints during training, \textsc{DeNSe}
generates (at inference time) trees for the overwhelming majority of sentences,
while non-tree outputs can be adjusted with a maximum spanning tree algorithm.
We evaluate \textsc{DeNSe} on four languages (English, Chinese, Czech, and
German) with varying degrees of non-projectivity. Despite the simplicity of the
approach, our parsers are on par with the state of the art.
Counting Answer Sets via Dynamic Programming
Authors: Johannes Fichte, Markus Hecher, Michael Morak, Stefan Woltran
Date: 2016-12-22 00:00:00

While the solution counting problem for propositional satisfiability (#SAT)
has received renewed attention in recent years, this research trend has not
affected other AI solving paradigms like answer set programming (ASP). Although
ASP solvers are designed to enumerate all solutions, and counting can therefore
be easily done, the involved materialization of all solutions is a clear
bottleneck for the counting problem of ASP (#ASP). In this paper we propose
dynamic programming-based #ASP algorithms that exploit the structure of the
underlying (ground) ASP program. Experimental results for a prototype
implementation show promise when compared to existing solvers.
Re-evaluating Automatic Metrics for Image Captioning
Authors: Mert Kilickaya, Aykut Erdem, Nazli Ikizler-Cinbis, Erkut Erdem
Date: 2016-12-22 00:00:00
Proceeding: EACL 2017 4
Tasks: Image Captioning

The task of generating natural language descriptions from images has received
a lot of attention in recent years. Consequently, it is becoming increasingly
important to evaluate such image captioning approaches in an automatic manner.
In this paper, we provide an in-depth evaluation of the existing image
captioning metrics through a series of carefully designed experiments.
Moreover, we explore the utilization of the recently proposed Word Mover's
Distance (WMD) document metric for the purpose of image captioning. Our
findings outline the differences and/or similarities between metrics and their
relative robustness by means of extensive correlation, accuracy and distraction
based evaluations. Our results also demonstrate that WMD provides strong
advantages over other metrics.
Solving Set Optimization Problems by Cardinality Optimization via Weak Constraints with an Application to Argumentation
Authors: Wolfgang Faber, Mauro Vallati, Federico Cerutti, Massimiliano Giacomin
Date: 2016-12-22 00:00:00
Tasks: Abstract Argumentation

Optimization - minimization or maximization - in the lattice of subsets is a
frequent operation in Artificial Intelligence tasks. Examples are
subset-minimal model-based diagnosis, nonmonotonic reasoning by means of
circumscription, or preferred extensions in abstract argumentation. Finding the
optimum among many admissible solutions is often harder than finding admissible
solutions with respect to both computational complexity and methodology. This
paper addresses the former issue by means of an effective method for finding
subset-optimal solutions. It is based on the relationship between
cardinality-optimal and subset-optimal solutions, and the fact that many
logic-based declarative programming systems provide constructs for finding
cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or
weak constraints in Answer Set Programming (ASP). Clearly each
cardinality-optimal solution is also a subset-optimal one, and if the language
also allows for the addition of particular restricting constructs (both MaxSAT
and ASP do) then all subset-optimal solutions can be found by an iterative
computation of cardinality-optimal solutions. As a showcase, the computation of
preferred extensions of abstract argumentation frameworks using the proposed
method is studied.
Active skeleton for bacteria modeling
Authors: Jean-Pascal Jacob, Mariella Dimiccoli, Lionel Moisan
Date: 2015-07-23 00:00:00

The investigation of spatio-temporal dynamics of bacterial cells and their
molecular components requires automated image analysis tools to track cell
shape properties and molecular component locations inside the cells. In the
study of bacteria aging, the molecular components of interest are protein
aggregates accumulated near bacteria boundaries. This particular location makes
very ambiguous the correspondence between aggregates and cells, since computing
accurately bacteria boundaries in phase-contrast time-lapse imaging is a
challenging task. This paper proposes an active skeleton formulation for
bacteria modeling which provides several advantages: an easy computation of
shape properties (perimeter, length, thickness, orientation), an improved
boundary accuracy in noisy images, and a natural bacteria-centered coordinate
system that permits the intrinsic location of molecular components inside the
cell. Starting from an initial skeleton estimate, the medial axis of the
bacterium is obtained by minimizing an energy function which incorporates
bacteria shape constraints. Experimental results on biological images and
comparative evaluation of the performances validate the proposed approach for
modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the
proposed method can be found online at http://fluobactracker.inrialpes.fr.
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
Authors: Mariella Dimiccoli, Jean-Pascal Jacob, Lionel Moisan
Date: 2015-07-22 00:00:00

This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.
Role of Simplicity in Creative Behaviour: The Case of the Poietic Generator
Authors: Antoine Saillenfest, Jean-Louis Dessalles, Olivier Auber
Date: 2016-12-22 00:00:00

We propose to apply Simplicity Theory (ST) to model interest in creative
situations. ST has been designed to describe and predict interest in
communication. Here we use ST to derive a decision rule that we apply to a
simplified version of a creative game, the Poietic Generator. The decision rule
produces what can be regarded as an elementary form of creativity. This study
is meant as a proof of principle. It suggests that some creative actions may be
motivated by the search for unexpected simplicity.
Zero-shot object prediction using semantic scene knowledge
Authors: Rene Grzeszick, Gernot A. Fink
Date: 2016-04-27 00:00:00
Tasks: Object, Object Recognition, Prediction

This work focuses on the semantic relations between scenes and objects for
visual object recognition. Semantic knowledge can be a powerful source of
information especially in scenarios with few or no annotated training samples.
These scenarios are referred to as zero-shot or few-shot recognition and often
build on visual attributes. Here, instead of relying on various visual
attributes, a more direct way is pursued: after recognizing the scene that is
depicted in an image, semantic relations between scenes and objects are used
for predicting the presence of objects in an unsupervised manner. Most
importantly, relations between scenes and objects can easily be obtained from
external sources such as large scale text corpora from the web and, therefore,
do not require tremendous manual labeling efforts. It will be shown that in
cluttered scenes, where visual recognition is difficult, scene knowledge is an
important cue for predicting objects.
Optimistic and Pessimistic Neural Networks for Scene and Object Recognition
Authors: Rene Grzeszick, Sebastian Sudholt, Gernot A. Fink
Date: 2016-09-26 00:00:00
Tasks: Attribute, Object Recognition

In this paper the application of uncertainty modeling to convolutional neural
networks is evaluated. A novel method for adjusting the network's predictions
based on uncertainty information is introduced. This allows the network to be
either optimistic or pessimistic in its prediction scores. The proposed method
builds on the idea of applying dropout at test time and sampling a predictive
mean and variance from the network's output. Besides the methodological
aspects, implementation details allowing for a fast evaluation are presented.
Furthermore, a multilabel network architecture is introduced that strongly
benefits from the presented approach. In the evaluation it will be shown that
modeling uncertainty allows for improving the performance of a given model
purely at test time without any further training steps. The evaluation
considers several applications in the field of computer vision, including
object classification and detection as well as scene attribute recognition.
The SP Theory of Intelligence as a Foundation for the Development of a General, Human-Level Thinking Machine
Authors: J. Gerard Wolff
Date: 2016-12-22 00:00:00
Tasks: Medical Diagnosis

This paper summarises how the "SP theory of intelligence" and its realisation
in the "SP computer model" simplifies and integrates concepts across artificial
intelligence and related areas, and thus provides a promising foundation for
the development of a general, human-level thinking machine, in accordance with
the main goal of research in artificial general intelligence.
  The key to this simplification and integration is the powerful concept of
"multiple alignment", borrowed and adapted from bioinformatics. This concept
has the potential to be the "double helix" of intelligence, with as much
significance for human-level intelligence as has DNA for biological sciences.
  Strengths of the SP system include: versatility in the representation of
diverse kinds of knowledge; versatility in aspects of intelligence (including:
strengths in unsupervised learning; the processing of natural language; pattern
recognition at multiple levels of abstraction that is robust in the face of
errors in data; several kinds of reasoning (including: one-step `deductive'
reasoning; chains of reasoning; abductive reasoning; reasoning with
probabilistic networks and trees; reasoning with 'rules'; nonmonotonic
reasoning and reasoning with default values; Bayesian reasoning with
'explaining away'; and more); planning; problem solving; and more); seamless
integration of diverse kinds of knowledge and diverse aspects of intelligence
in any combination; and potential for application in several areas (including:
helping to solve nine problems with big data; helping to develop human-level
intelligence in autonomous robots; serving as a database with intelligence and
with versatility in the representation and integration of several forms of
knowledge; serving as a vehicle for medical knowledge and as an aid to medical
diagnosis; and several more).
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
Authors: Wendelin Böhmer, Rong Guo, Klaus Obermayer
Date: 2016-12-22 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper investigates a type of instability that is linked to the greedy
policy improvement in approximated reinforcement learning. We show empirically
that non-deterministic policy improvement can stabilize methods like LSPI by
controlling the improvements' stochasticity. Additionally we show that a
suitable representation of the value function also stabilizes the solution to
some degree. The presented approach is simple and should also be easily
transferable to more sophisticated algorithms like deep reinforcement learning.