pwc-archive/papers-with-abstracts huggingface dataset:
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
Authors: Yu Liu, Jianlong Fu, Tao Mei, Chang Wen Chen
Date: 2016-06-02 00:00:00
Tasks: Video Captioning, Visual Storytelling

Visual storytelling aims to generate human-level narrative language (i.e., a
natural paragraph with multiple sentences) from a photo streams. A typical
photo story consists of a global timeline with multi-thread local storylines,
where each storyline occurs in one different scene. Such complex structure
leads to large content gaps at scene transitions between consecutive photos.
Most existing image/video captioning methods can only achieve limited
performance, because the units in traditional recurrent neural networks (RNN)
tend to "forget" the previous state when the visual sequence is inconsistent.
In this paper, we propose a novel visual storytelling approach with
Bidirectional Multi-thread Recurrent Neural Network (BMRNN). First, based on
the mined local storylines, a skip gated recurrent unit (sGRU) with delay
control is proposed to maintain longer range visual information. Second, by
using sGRU as basic units, the BMRNN is trained to align the local storylines
into the global sequential timeline. Third, a new training scheme with a
storyline-constrained objective function is proposed by jointly considering
both global and local matches. Experiments on three standard storytelling
datasets show that the BMRNN model outperforms the state-of-the-art methods.
Bayesian Learning of Kernel Embeddings
Authors: Seth Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi
Date: 2016-03-07 00:00:00
Tasks: Bayesian Inference

Kernel methods are one of the mainstays of machine learning, but the problem
of kernel learning remains challenging, with only a few heuristics and very
little theory. This is of particular importance in methods based on estimation
of kernel mean embeddings of probability measures. For characteristic kernels,
which include most commonly used ones, the kernel mean embedding uniquely
determines its probability measure, so it can be used to design a powerful
statistical testing framework, which includes nonparametric two-sample and
independence tests. In practice, however, the performance of these tests can be
very sensitive to the choice of kernel and its lengthscale parameters. To
address this central issue, we propose a new probabilistic model for kernel
mean embeddings, the Bayesian Kernel Embedding model, combining a Gaussian
process prior over the Reproducing Kernel Hilbert Space containing the mean
embedding with a conjugate likelihood function, thus yielding a closed form
posterior over the mean embedding. The posterior mean of our model is closely
related to recently proposed shrinkage estimators for kernel mean embeddings,
while the posterior uncertainty is a new, interesting feature with various
possible applications. Critically for the purposes of kernel learning, our
model gives a simple, closed form marginal pseudolikelihood of the observed
data given the kernel hyperparameters. This marginal pseudolikelihood can
either be optimized to inform the hyperparameter choice or fully Bayesian
inference can be used.
On the performance of different mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems
Authors: Chun Liu, Andreas Kroll
Date: 2016-06-02 00:00:00
Tasks: Combinatorial Optimization

The performance of different mutation operators is usually evaluated in
conjunc-tion with specific parameter settings of genetic algorithms and target
problems. Most studies focus on the classical genetic algorithm with different
parameters or on solving unconstrained combinatorial optimization problems such
as the traveling salesman problems. In this paper, a subpopulation-based
genetic al-gorithm that uses only mutation and selection is developed to solve
multi-robot task allocation problems. The target problems are constrained
combinatorial optimization problems, and are more complex if cooperative tasks
are involved as these introduce additional spatial and temporal constraints.
The proposed genetic algorithm can obtain better solutions than classical
genetic algorithms with tournament selection and partially mapped crossover.
The performance of different mutation operators in solving problems
without/with cooperative tasks is evaluated. The results imply that inversion
mutation performs better than others when solving problems without cooperative
tasks, and the swap-inversion combination performs better than others when
solving problems with cooperative tasks.
Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection
Authors: Katharina Kann, Hinrich Schütze
Date: 2016-06-02 00:00:00
Proceeding: ACL 2016 8
Tasks: Decoder, Form, TAG

Morphological reinflection is the task of generating a target form given a
source form, a source tag and a target tag. We propose a new way of modeling
this task with neural encoder-decoder models. Our approach reduces the amount
of required training data for this architecture and achieves state-of-the-art
results, making encoder-decoder models applicable to morphological reinflection
even for low-resource languages. We further present a new automatic correction
method for the outputs based on edit trees.
Fast Saddle-Point Algorithm for Generalized Dantzig Selector and FDR Control with the Ordered l1-Norm
Authors: Sangkyun Lee, Damian Brzyski, Malgorzata Bogdan
Date: 2015-11-18 00:00:00
Tasks: Variable Selection

In this paper we propose a primal-dual proximal extragradient algorithm to
solve the generalized Dantzig selector (GDS) estimation problem, based on a new
convex-concave saddle-point (SP) reformulation. Our new formulation makes it
possible to adopt recent developments in saddle-point optimization, to achieve
the optimal $O(1/k)$ rate of convergence. Compared to the optimal non-SP
algorithms, ours do not require specification of sensitive parameters that
affect algorithm performance or solution quality. We also provide a new
analysis showing a possibility of local acceleration to achieve the rate of
$O(1/k^2)$ in special cases even without strong convexity or strong smoothness.
As an application, we propose a GDS equipped with the ordered $\ell_1$-norm,
showing its false discovery rate control properties in variable selection.
Algorithm performance is compared between ours and other alternatives,
including the linearized ADMM, Nesterov's smoothing, Nemirovski's mirror-prox,
and the accelerated hybrid proximal extragradient techniques.
Mining Software Components from Object-Oriented APIs
Authors: Anas Shatnawi, Abdelhak Seriai, Houari Sahraoui, Zakarea Al-Shara
Date: 2016-06-02 00:00:00
Tasks: Object

Object-oriented Application Programing Interfaces (APIs) support software
reuse by providing pre-implemented functionalities. Due to the huge number of
included classes, reusing and understanding large APIs is a complex task.
Otherwise, software components are admitted to be more reusable and
understandable entities than object-oriented ones. Thus, in this paper, we
propose an approach for reengineering object-oriented APIs into component-based
ones. We mine components as a group of classes based on the frequency they are
used together and their ability to form a quality-centric component. To
validate our approach, we experimented on 100 Java applications that used
Android APIs.
On collapsed representation of hierarchical Completely Random Measures
Authors: Gaurav Pandey, Ambedkar Dukkipati
Date: 2015-09-06 00:00:00

The aim of the paper is to provide an exact approach for generating a Poisson
process sampled from a hierarchical CRM, without having to instantiate the
infinitely many atoms of the random measures. We use completely random
measures~(CRM) and hierarchical CRM to define a prior for Poisson processes. We
derive the marginal distribution of the resultant point process, when the
underlying CRM is marginalized out. Using well known properties unique to
Poisson processes, we were able to derive an exact approach for instantiating a
Poisson process with a hierarchical CRM prior. Furthermore, we derive Gibbs
sampling strategies for hierarchical CRM models based on Chinese restaurant
franchise sampling scheme. As an example, we present the sum of generalized
gamma process (SGGP), and show its application in topic-modelling. We show that
one can determine the power-law behaviour of the topics and words in a Bayesian
fashion, by defining a prior on the parameters of SGGP.
Fixed Point Quantization of Deep Convolutional Networks
Authors: Darryl D. Lin, Sachin S. Talathi, V. Sreekanth Annapureddy
Date: 2015-11-19 00:00:00
Tasks: Quantization

In recent years increasingly complex architectures for deep convolution
networks (DCNs) have been proposed to boost the performance on image
recognition tasks. However, the gains in performance have come at a cost of
substantial increase in computation and model storage resources. Fixed point
implementation of DCNs has the potential to alleviate some of these
complexities and facilitate potential deployment on embedded hardware. In this
paper, we propose a quantizer design for fixed point implementation of DCNs. We
formulate and solve an optimization problem to identify optimal fixed point
bit-width allocation across DCN layers. Our experiments show that in comparison
to equal bit-width settings, the fixed point DCNs with optimized bit width
allocation offer >20% reduction in the model size without any loss in accuracy
on CIFAR-10 benchmark. We also demonstrate that fine-tuning can further enhance
the accuracy of fixed point DCNs beyond that of the original floating point
model. In doing so, we report a new state-of-the-art fixed point performance of
6.78% error-rate on CIFAR-10 benchmark.
Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity
Authors: Florian Ziel, Carsten Croonenbroeck, Daniel Ambach
Date: 2016-06-02 00:00:00

In this article we present an approach that enables joint wind speed and wind
power forecasts for a wind park. We combine a multivariate seasonal time
varying threshold autoregressive moving average (TVARMA) model with a power
threshold generalized autoregressive conditional heteroscedastic (power-TGARCH)
model. The modeling framework incorporates diurnal and annual periodicity
modeling by periodic B-splines, conditional heteroscedasticity and a complex
autoregressive structure with non-linear impacts. In contrast to usually
time-consuming estimation approaches as likelihood estimation, we apply a
high-dimensional shrinkage technique. We utilize an iteratively re-weighted
least absolute shrinkage and selection operator (lasso) technique. It allows
for conditional heteroscedasticity, provides fast computing times and
guarantees a parsimonious and regularized specification, even though the
parameter space may be vast. We are able to show that our approach provides
accurate forecasts of wind power at a turbine-specific level for forecasting
horizons of up to 48 h (short- to medium-term forecasts).
Multi-pretrained Deep Neural Network
Authors: Zhen Hu, Zhuyin Xue, Tong Cui, Shiqiang Zong, Chenglong He
Date: 2016-06-02 00:00:00
Tasks: Denoising

Pretraining is widely used in deep neutral network and one of the most famous
pretraining models is Deep Belief Network (DBN). The optimization formulas are
different during the pretraining process for different pretraining models. In
this paper, we pretrained deep neutral network by different pretraining models
and hence investigated the difference between DBN and Stacked Denoising
Autoencoder (SDA) when used as pretraining model. The experimental results show
that DBN get a better initial model. However the model converges to a
relatively worse model after the finetuning process. Yet after pretrained by
SDA for the second time the model converges to a better model if finetuned.
Dictionary Learning for Robotic Grasp Recognition and Detection
Authors: Ludovic Trottier, Philippe Giguère, Brahim Chaib-Draa
Date: 2016-06-02 00:00:00
Tasks: Dictionary Learning

The ability to grasp ordinary and potentially never-seen objects is an
important feature in both domestic and industrial robotics. For a system to
accomplish this, it must autonomously identify grasping locations by using
information from various sensors, such as Microsoft Kinect 3D camera. Despite
numerous progress, significant work still remains to be done in this field. To
this effect, we propose a dictionary learning and sparse representation (DLSR)
framework for representing RGBD images from 3D sensors in the context of
determining such good grasping locations. In contrast to previously proposed
approaches that relied on sophisticated regularization or very large datasets,
the derived perception system has a fast training phase and can work with small
datasets. It is also theoretically founded for dealing with masked-out entries,
which are common with 3D sensors. We contribute by presenting a comparative
study of several DLSR approach combinations for recognizing and detecting grasp
candidates on the standard Cornell dataset. Importantly, experimental results
show a performance improvement of 1.69% in detection and 3.16% in recognition
over current state-of-the-art convolutional neural network (CNN). Even though
nowadays most popular vision-based approach is CNN, this suggests that DLSR is
also a viable alternative with interesting advantages that CNN has not.
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Authors: Liang-Chieh Chen, Jonathan T. Barron, George Papandreou, Kevin Murphy, Alan L. Yuille
Date: 2015-11-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Edge Detection, Image Segmentation, Object Localization, Segmentation, Semantic Segmentation

Deep convolutional neural networks (CNNs) are the backbone of state-of-art
semantic image segmentation systems. Recent work has shown that complementing
CNNs with fully-connected conditional random fields (CRFs) can significantly
enhance their object localization accuracy, yet dense CRF inference is
computationally expensive. We propose replacing the fully-connected CRF with
domain transform (DT), a modern edge-preserving filtering method in which the
amount of smoothing is controlled by a reference edge map. Domain transform
filtering is several times faster than dense CRF inference and we show that it
yields comparable semantic segmentation results, accurately capturing object
boundaries. Importantly, our formulation allows learning the reference edge map
from intermediate CNN features instead of using the image gradient magnitude as
in standard DT filtering. This produces task-specific edges in an end-to-end
trainable system optimizing the target semantic segmentation quality.
Attention to Scale: Scale-aware Semantic Image Segmentation
Authors: Liang-Chieh Chen, Yi Yang, Jiang Wang, Wei Xu, Alan L. Yuille
Date: 2015-11-10 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Incorporating multi-scale features in fully convolutional neural networks
(FCNs) has been a key element to achieving state-of-the-art performance on
semantic image segmentation. One common way to extract multi-scale features is
to feed multiple resized input images to a shared deep network and then merge
the resulting features for pixelwise classification. In this work, we propose
an attention mechanism that learns to softly weight the multi-scale features at
each pixel location. We adapt a state-of-the-art semantic image segmentation
model, which we jointly train with multi-scale input images and the attention
model. The proposed attention model not only outperforms average- and
max-pooling, but allows us to diagnostically visualize the importance of
features at different positions and scales. Moreover, we show that adding extra
supervision to the output at each scale is essential to achieving excellent
performance when merging multi-scale features. We demonstrate the effectiveness
of our model with extensive experiments on three challenging datasets,
including PASCAL-Person-Part, PASCAL VOC 2012 and a subset of MS-COCO 2014.
Robust and Scalable Bayes via a Median of Subset Posterior Measures
Authors: Stanislav Minsker, Sanvesh Srivastava, Lizhen Lin, David B. Dunson
Date: 2014-03-11 00:00:00

We propose a novel approach to Bayesian analysis that is provably robust to
outliers in the data and often has computational advantages over standard
methods. Our technique is based on splitting the data into non-overlapping
subgroups, evaluating the posterior distribution given each independent
subgroup, and then combining the resulting measures. The main novelty of our
approach is the proposed aggregation step, which is based on the evaluation of
a median in the space of probability measures equipped with a suitable
collection of distances that can be quickly and efficiently evaluated in
practice. We present both theoretical and numerical evidence illustrating the
improvements achieved by our method.
Training Deep Neural Networks via Direct Loss Minimization
Authors: Yang Song, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun
Date: 2015-11-19 00:00:00
Tasks: Action Classification, General Classification, object-detection, Object Detection

Supervised training of deep neural nets typically relies on minimizing
cross-entropy. However, in many domains, we are interested in performing well
on metrics specific to the application. In this paper we propose a direct loss
minimization approach to train deep neural networks, which provably minimizes
the application-specific loss function. This is often non-trivial, since these
functions are neither smooth nor decomposable and thus are not amenable to
optimization with standard gradient-based methods. We demonstrate the
effectiveness of our approach in the context of maximizing average precision
for ranking problems. Towards this goal, we develop a novel dynamic programming
algorithm that can efficiently compute the weight updates. Our approach proves
superior to a variety of baselines in the context of action classification and
object detection, especially in the presence of label noise.
Adaptive Learning with Robust Generalization Guarantees
Authors: Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
Date: 2016-02-24 00:00:00

The traditional notion of generalization---i.e., learning a hypothesis whose
empirical error is close to its true error---is surprisingly brittle. As has
recently been noted in [DFH+15b], even if several algorithms have this
guarantee in isolation, the guarantee need not hold if the algorithms are
composed adaptively. In this paper, we study three notions of
generalization---increasing in strength---that are robust to postprocessing and
amenable to adaptive composition, and examine the relationships between them.
We call the weakest such notion Robust Generalization. A second, intermediate,
notion is the stability guarantee known as differential privacy. The strongest
guarantee we consider we call Perfect Generalization. We prove that every
hypothesis class that is PAC learnable is also PAC learnable in a robustly
generalizing fashion, with almost the same sample complexity. It was previously
known that differentially private algorithms satisfy robust generalization. In
this paper, we show that robust generalization is a strictly weaker concept,
and that there is a learning task that can be carried out subject to robust
generalization guarantees, yet cannot be carried out subject to differential
privacy. We also show that perfect generalization is a strictly stronger
guarantee than differential privacy, but that, nevertheless, many learning
tasks can be carried out subject to the guarantees of perfect generalization.
On the equivalence between Kolmogorov-Smirnov and ROC curve metrics for binary classification
Authors: Paulo J. L. Adeodato, Sílvio B. Melo
Date: 2016-06-01 00:00:00
Tasks: Binary Classification, General Classification

Binary decisions are very common in artificial intelligence. Applying a
threshold on the continuous score gives the human decider the power to control
the operating point to separate the two classes. The classifier,s
discriminating power is measured along the continuous range of the score by the
Area Under the ROC curve (AUC_ROC) in most application fields. Only finances
uses the poor single point metric maximum Kolmogorov-Smirnov (KS) distance.
This paper proposes the Area Under the KS curve (AUC_KS) for performance
assessment and proves AUC_ROC = 0.5 + AUC_KS, as a simpler way to calculate the
AUC_ROC. That is even more important for ROC averaging in ensembles of
classifiers or n fold cross-validation. The proof is geometrically inspired on
rotating all KS curve to make it lie on the top of the ROC chance diagonal. On
the practical side, the independent variable on the abscissa on the KS curve
simplifies the calculation of the AUC_ROC. On the theoretical side, this
research gives insights on probabilistic interpretations of classifiers
assessment and integrates the existing body of knowledge of the information
theoretical ROC approach with the proposed statistical approach based on the
thoroughly known KS distribution.
A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment
Authors: Michael Grupp, Philipp Kopp, Patrik Huber, Matthias Rätsch
Date: 2016-06-01 00:00:00
Tasks: 3D Face Modelling, Face Recognition, Robust Face Recognition

Face analysis techniques have become a crucial component of human-machine
interaction in the fields of assistive and humanoid robotics. However, the
variations in head-pose that arise naturally in these environments are still a
great challenge. In this paper, we present a real-time capable 3D face
modelling framework for 2D in-the-wild images that is applicable for robotics.
The fitting of the 3D Morphable Model is based exclusively on automatically
detected landmarks. After fitting, the face can be corrected in pose and
transformed back to a frontal 2D representation that is more suitable for face
recognition. We conduct face recognition experiments with non-frontal images
from the MUCT database and uncontrolled, in the wild images from the PaSC
database, the most challenging face recognition database to date, showing an
improved performance. Finally, we present our SCITOS G5 robot system, which
incorporates our framework as a means of image pre-processing for face
analysis.
On the satisfiability problem for SPARQL patterns
Authors: Xiaowang Zhang, Jan Van den Bussche, François Picalausa
Date: 2014-06-05 00:00:00

The satisfiability problem for SPARQL patterns is undecidable in general,
since the expressive power of SPARQL 1.0 is comparable with that of the
relational algebra. The goal of this paper is to delineate the boundary of
decidability of satisfiability in terms of the constraints allowed in filter
conditions. The classes of constraints considered are bound-constraints,
negated bound-constraints, equalities, nonequalities, constant-equalities, and
constant-nonequalities. The main result of the paper can be summarized by
saying that, as soon as inconsistent filter conditions can be formed,
satisfiability is undecidable. The key insight in each case is to find a way to
emulate the set difference operation. Undecidability can then be obtained from
a known undecidability result for the algebra of binary relations with union,
composition, and set difference. When no inconsistent filter conditions can be
formed, satisfiability is efficiently decidable by simple checks on bound
variables and on the use of literals. The paper also points out that
satisfiability for the so-called `well-designed' patterns can be decided by a
check on bound variables and a check for inconsistent filter conditions.