pwc-archive/papers-with-abstracts huggingface dataset:
A Pareto Front-Based Multiobjective Path Planning Algorithm
Authors: Alexander Lavin
Date: 2015-05-22 00:00:00
Tasks: Multiobjective Optimization

Path planning is one of the most vital elements of mobile robotics. With a
priori knowledge of the environment, global path planning provides a
collision-free route through the workspace. The global path plan can be
calculated with a variety of informed search algorithms, most notably the A*
search method, guaranteed to deliver a complete and optimal solution that
minimizes the path cost. Path planning optimization typically looks to minimize
the distance traversed from start to goal, yet many mobile robot applications
call for additional path planning objectives, presenting a multiobjective
optimization (MOO) problem. Past studies have applied genetic algorithms to MOO
path planning problems, but these may have the disadvantages of computational
complexity and suboptimal solutions. Alternatively, the algorithm in this paper
approaches MOO path planning with the use of Pareto fronts, or finding
non-dominated solutions. The algorithm presented incorporates Pareto optimality
into every step of A* search, thus it is named A*-PO. Results of simulations
show A*-PO outperformed several variations of the standard A* algorithm for MOO
path planning. A planetary exploration rover case study was added to
demonstrate the viability of A*-PO in a real-world application.
Quantum Deep Learning
Authors: Nathan Wiebe, Ashish Kapoor, Krysta M. Svore
Date: 2014-12-10 00:00:00
Tasks: Deep Learning

In recent years, deep learning has had a profound impact on machine learning
and artificial intelligence. At the same time, algorithms for quantum computers
have been shown to efficiently solve some problems that are intractable on
conventional, classical computers. We show that quantum computing not only
reduces the time required to train a deep restricted Boltzmann machine, but
also provides a richer and more comprehensive framework for deep learning than
classical computing and leads to significant improvements in the optimization
of the underlying objective function. Our quantum methods also permit efficient
training of full Boltzmann machines and multi-layer, fully connected models and
do not have well known classical counterparts.
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
Authors: Erroll Wood, Tadas Baltrusaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, Andreas Bulling
Date: 2015-05-21 00:00:00
Proceeding: ICCV 2015 12
Tasks: Gaze Estimation

Images of the eye are key in several computer vision problems, such as shape
registration and gaze estimation. Recent large-scale supervised methods for
these problems require time-consuming data collection and manual annotation,
which can be unreliable. We propose synthesizing perfectly labelled
photo-realistic training data in a fraction of the time. We used computer
graphics techniques to build a collection of dynamic eye-region models from
head scan geometry. These were randomly posed to synthesize close-up eye images
for a wide range of head poses, gaze directions, and illumination conditions.
We used our model's controllability to verify the importance of realistic
illumination and shape variations in eye-region training data. Finally, we
demonstrate the benefits of our synthesized training data (SynthesEyes) by
out-performing state-of-the-art methods for eye-shape registration as well as
cross-dataset appearance-based gaze estimation in the wild.
Qualitatively characterizing neural network optimization problems
Authors: Ian J. Goodfellow, Oriol Vinyals, Andrew M. Saxe
Date: 2014-12-19 00:00:00

Training neural networks involves solving large-scale non-convex optimization
problems. This task has long been believed to be extremely difficult, with fear
of local minima and other obstacles motivating a variety of schemes to improve
optimization, such as unsupervised pretraining. However, modern neural networks
are able to achieve negligible training error on complex tasks, using only
direct training with stochastic gradient descent. We introduce a simple
analysis technique to look for evidence that such networks are overcoming local
optima. We find that, in fact, on a straight path from initialization to
solution, a variety of state of the art neural networks never encounter any
significant obstacles.
The IBM 2015 English Conversational Telephone Speech Recognition System
Authors: George Saon, Hong-Kwang J. Kuo, Steven Rennie, Michael Picheny
Date: 2015-05-21 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We describe the latest improvements to the IBM English conversational
telephone speech recognition system. Some of the techniques that were found
beneficial are: maxout networks with annealed dropout rates; networks with a
very large number of outputs trained on 2000 hours of data; joint modeling of
partially unfolded recurrent neural networks and convolutional nets by
combining the bottleneck and output layers and retraining the resulting model;
and lastly, sophisticated language model rescoring with exponential and neural
network LMs. These techniques result in an 8.0% word error rate on the
Switchboard part of the Hub5-2000 evaluation test set which is 23% relative
better than our previous best published result.
Translation Memory Retrieval Methods
Authors: Michael Bloodgood, Benjamin Strauss
Date: 2015-05-21 00:00:00
Proceeding: EACL 2014 4
Tasks: Retrieval, Translation

Translation Memory (TM) systems are one of the most widely used translation
technologies. An important part of TM systems is the matching algorithm that
determines what translations get retrieved from the bank of available
translations to assist the human translator. Although detailed accounts of the
matching algorithms used in commercial systems can't be found in the
literature, it is widely believed that edit distance algorithms are used. This
paper investigates and evaluates the use of several matching algorithms,
including the edit distance algorithm that is believed to be at the heart of
most modern commercial TM systems. This paper presents results showing how well
various matching algorithms correlate with human judgments of helpfulness
(collected via crowdsourcing with Amazon's Mechanical Turk). A new algorithm
based on weighted n-gram precision that can be adjusted for translator length
preferences consistently returns translations judged to be most helpful by
translators for multiple domains and language pairs.
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret
Authors: Haitham Bou Ammar, Rasul Tutunov, Eric Eaton
Date: 2015-05-21 00:00:00
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Lifelong reinforcement learning provides a promising framework for developing
versatile agents that can accumulate knowledge over a lifetime of experience
and rapidly learn new tasks by building upon prior knowledge. However, current
lifelong learning methods exhibit non-vanishing regret as the amount of
experience increases and include limitations that can lead to suboptimal or
unsafe control policies. To address these issues, we develop a lifelong policy
gradient learner that operates in an adversarial set- ting to learn multiple
tasks online while enforcing safety constraints on the learned policies. We
demonstrate, for the first time, sublinear regret for lifelong policy search,
and validate our algorithm on several benchmark dynamical systems and an
application to quadrotor control.
On distinguishability criteria for estimating generative models
Authors: Ian J. Goodfellow
Date: 2014-12-19 00:00:00
Tasks: Binary Classification

Two recently introduced criteria for estimation of generative models are both
based on a reduction to binary classification. Noise-contrastive estimation
(NCE) is an estimation procedure in which a generative model is trained to be
able to distinguish data samples from noise samples. Generative adversarial
networks (GANs) are pairs of generator and discriminator networks, with the
generator network learning to generate samples by attempting to fool the
discriminator network into believing its samples are real data. Both estimation
procedures use the same function to drive learning, which naturally raises
questions about how they are related to each other, as well as whether this
function is related to maximum likelihood estimation (MLE). NCE corresponds to
training an internal data model belonging to the {\em discriminator} network
but using a fixed generator network. We show that a variant of NCE, with a
dynamic generator network, is equivalent to maximum likelihood estimation.
Since pairing a learned discriminator with an appropriate dynamically selected
generator recovers MLE, one might expect the reverse to hold for pairing a
learned generator with a certain discriminator. However, we show that
recovering MLE for a learned generator requires departing from the
distinguishability game. Specifically:
  (i) The expected gradient of the NCE discriminator can be made to match the
expected gradient of
  MLE, if one is allowed to use a non-stationary noise distribution for NCE,
  (ii) No choice of discriminator network can make the expected gradient for
the GAN generator match that of MLE, and
  (iii) The existing theory does not guarantee that GANs will converge in the
non-convex case.
  This suggests that the key next step in GAN research is to determine whether
GANs converge, and if not, to modify their training algorithm to force
convergence.
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
Authors: Ishan Misra, Abhinav Shrivastava, Martial Hebert
Date: 2015-05-21 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection

We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.
GazeDPM: Early Integration of Gaze Information in Deformable Part Models
Authors: Iaroslav Shcherbatyi, Andreas Bulling, Mario Fritz
Date: 2015-05-21 00:00:00
Tasks: Gaze Estimation, object-detection, Object Detection

An increasing number of works explore collaborative human-computer systems in
which human gaze is used to enhance computer vision systems. For object
detection these efforts were so far restricted to late integration approaches
that have inherent limitations, such as increased precision without increase in
recall. We propose an early integration approach in a deformable part model,
which constitutes a joint formulation over gaze and visual data. We show that
our GazeDPM method improves over the state-of-the-art DPM baseline by 4% and a
recent method for gaze-supported object detection by 3% on the public POET
dataset. Our approach additionally provides introspection of the learnt models,
can reveal salient image structures, and allows us to investigate the interplay
between gaze attracting and repelling areas, the importance of view-specific
models, as well as viewers' personal biases in gaze patterns. We finally study
important practical aspects of our approach, such as the impact of using
saliency maps instead of real fixations, the impact of the number of fixations,
as well as robustness to gaze estimation error.
Weight Uncertainty in Neural Networks
Authors: Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
Date: 2015-05-20 00:00:00
Tasks: Bayesian Inference, General Classification, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a new, efficient, principled and backpropagation-compatible
algorithm for learning a probability distribution on the weights of a neural
network, called Bayes by Backprop. It regularises the weights by minimising a
compression cost, known as the variational free energy or the expected lower
bound on the marginal likelihood. We show that this principled kind of
regularisation yields comparable performance to dropout on MNIST
classification. We then demonstrate how the learnt uncertainty in the weights
can be used to improve generalisation in non-linear regression problems, and
how this weight uncertainty can be used to drive the exploration-exploitation
trade-off in reinforcement learning.
Symmetric low-rank representation for subspace clustering
Authors: Jie Chen, Haixian Zhang, Hua Mao, Yongsheng Sang, Zhang Yi
Date: 2014-10-31 00:00:00
Tasks: Clustering

We propose a symmetric low-rank representation (SLRR) method for subspace
clustering, which assumes that a data set is approximately drawn from the union
of multiple subspaces. The proposed technique can reveal the membership of
multiple subspaces through the self-expressiveness property of the data. In
particular, the SLRR method considers a collaborative representation combined
with low-rank matrix recovery techniques as a low-rank representation to learn
a symmetric low-rank representation, which preserves the subspace structures of
high-dimensional data. In contrast to performing iterative singular value
decomposition in some existing low-rank representation based algorithms, the
symmetric low-rank representation in the SLRR method can be calculated as a
closed form solution by solving the symmetric low-rank optimization problem. By
making use of the angular information of the principal directions of the
symmetric low-rank representation, an affinity graph matrix is constructed for
spectral clustering. Extensive experimental results show that it outperforms
state-of-the-art subspace clustering algorithms.
Graph edit distance : a new binary linear programming formulation
Authors: Julien Lerouge, Zeina Abu-Aisheh, Romain Raveaux, Pierre Héroux, Sébastien Adam
Date: 2015-05-21 00:00:00
Tasks: Graph Matching

Graph edit distance (GED) is a powerful and flexible graph matching paradigm
that can be used to address different tasks in structural pattern recognition,
machine learning, and data mining. In this paper, some new binary linear
programming formulations for computing the exact GED between two graphs are
proposed. A major strength of the formulations lies in their genericity since
the GED can be computed between directed or undirected fully attributed graphs
(i.e. with attributes on both vertices and edges). Moreover, a relaxation of
the domain constraints in the formulations provides efficient lower bound
approximations of the GED. A complete experimental study comparing the proposed
formulations with 4 state-of-the-art algorithms for exact and approximate graph
edit distances is provided. By considering both the quality of the proposed
solution and the efficiency of the algorithms as performance criteria, the
results show that none of the compared methods dominates the others in the
Pareto sense. As a consequence, faced to a given real-world problem, a
trade-off between quality and efficiency has to be chosen w.r.t. the
application constraints. In this context, this paper provides a guide that can
be used to choose the appropriate method.
On the relation between accuracy and fairness in binary classification
Authors: Indre Zliobaite
Date: 2015-05-21 00:00:00
Tasks: Binary Classification, Classification, Fairness, General Classification, Relation

Our study revisits the problem of accuracy-fairness tradeoff in binary
classification. We argue that comparison of non-discriminatory classifiers
needs to account for different rates of positive predictions, otherwise
conclusions about performance may be misleading, because accuracy and
discrimination of naive baselines on the same dataset vary with different rates
of positive predictions. We provide methodological recommendations for sound
comparison of non-discriminatory classifiers, and present a brief theoretical
and empirical analysis of tradeoffs between accuracy and non-discrimination.
A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network
Authors: Chenxi Zhu, Xipeng Qiu, Xinchi Chen, Xuanjing Huang
Date: 2015-05-21 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Dependency Parsing, Re-Ranking

In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different with the original recursive neural network, we introduce the
convolution and pooling layers, which can model a variety of compositions by
the feature maps and choose the most informative compositions by the pooling
layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list
of candidate dependency parsing trees. The experiments show that RCNN is very
effective to improve the state-of-the-art dependency parsing on both English
and Chinese datasets.
Inferring Graphs from Cascades: A Sparse Recovery Framework
Authors: Jean Pouget-Abadie, Thibaut Horel
Date: 2015-05-21 00:00:00

In the Network Inference problem, one seeks to recover the edges of an
unknown graph from the observations of cascades propagating over this graph. In
this paper, we approach this problem from the sparse recovery perspective. We
introduce a general model of cascades, including the voter model and the
independent cascade model, for which we provide the first algorithm which
recovers the graph's edges with high probability and $O(s\log m)$ measurements
where $s$ is the maximum degree of the graph and $m$ is the number of nodes.
Furthermore, we show that our algorithm also recovers the edge weights (the
parameters of the diffusion process) and is robust in the context of
approximate sparsity. Finally we prove an almost matching lower bound of
$\Omega(s\log\frac{m}{s})$ and validate our approach empirically on synthetic
graphs.
Object Modelling with a Handheld RGB-D Camera
Authors: Aitor Aldoma, Johann Prankl, Alexander Svejda, Markus Vincze
Date: 2015-05-21 00:00:00
Tasks: Human-Object Interaction Detection, Object, Object Tracking

This work presents a flexible system to reconstruct 3D models of objects
captured with an RGB-D sensor. A major advantage of the method is that our
reconstruction pipeline allows the user to acquire a full 3D model of the
object. This is achieved by acquiring several partial 3D models in different
sessions that are automatically merged together to reconstruct a full model. In
addition, the 3D models acquired by our system can be directly used by
state-of-the-art object instance recognition and object tracking modules,
providing object-perception capabilities for different applications, such as
human-object interaction analysis or robot grasping. The system does not impose
constraints in the appearance of objects (textured, untextured) nor in the
modelling setup (moving camera with static object or a turn-table setup). The
proposed reconstruction system has been used to model a large number of objects
resulting in metrically accurate and visually appealing 3D models.
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
Authors: Hao Su, Charles R. Qi, Yangyan Li, Leonidas Guibas
Date: 2015-05-21 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Generation, Viewpoint Estimation

Object viewpoint estimation from 2D images is an essential task in computer
vision. However, two issues hinder its progress: scarcity of training data with
viewpoint annotations, and a lack of powerful features. Inspired by the growing
availability of 3D models, we propose a framework to address both issues by
combining render-based image synthesis and CNNs. We believe that 3D models have
the potential in generating a large number of images of high variation, which
can be well exploited by deep CNN with a high learning capacity. Towards this
goal, we propose a scalable and overfit-resistant image synthesis pipeline,
together with a novel CNN specifically tailored for the viewpoint estimation
task. Experimentally, we show that the viewpoint estimation from our pipeline
can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.
Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications
Authors: Christopher M. de Vries, Lance De Vine, Shlomo Geva, Richi Nayak
Date: 2015-05-21 00:00:00
Tasks: Clustering

The proliferation of the web presents an unsolved problem of automatically
analyzing billions of pages of natural language. We introduce a scalable
algorithm that clusters hundreds of millions of web pages into hundreds of
thousands of clusters. It does this on a single mid-range machine using
efficient algorithms and compressed document representations. It is applied to
two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12
contain 500 and 733 million web pages and were clustered into 500,000 to
700,000 clusters. To the best of our knowledge, such fine grained clustering
has not been previously demonstrated. Previous approaches clustered a sample
that limits the maximum number of discoverable clusters. The proposed EM-tree
algorithm uses the entire collection in clustering and produces several orders
of magnitude more clusters than the existing algorithms. Fine grained
clustering is necessary for meaningful clustering in massive collections where
the number of distinct topics grows linearly with collection size. These
fine-grained clusters show an improved cluster quality when assessed with two
novel evaluations using ad hoc search relevance judgments and spam
classifications for external validation. These evaluations solve the problem of
assessing the quality of clusters where categorical labeling is unavailable and
unfeasible.
Unsupervised Segmentation of Overlapping Cervical Cell Cytoplasm
Authors: S. L. Happy, Swarnadip Chatterjee, Debdoot Sheet
Date: 2015-05-21 00:00:00
Tasks: Cell Segmentation, Segmentation

Overlapping of cervical cells and poor contrast of cell cytoplasm are the
major issues in accurate detection and segmentation of cervical cells. An
unsupervised cell segmentation approach is presented here. Cell clump
segmentation was carried out using the extended depth of field (EDF) image
created from the images of different focal planes. A modified Otsu method with
prior class weights is proposed for accurate segmentation of nuclei from the
cell clumps. The cell cytoplasm was further segmented from cell clump depending
upon the number of nucleus detected in that cell clump. Level set model was
used for cytoplasm segmentation.
The development of an information criterion for Change-Point Analysis
Authors: Paul A. Wiggins, Colin H. LaMont
Date: 2015-05-21 00:00:00

Change-point analysis is a flexible and computationally tractable tool for
the analysis of times series data from systems that transition between discrete
states and whose observables are corrupted by noise. The change-point algorithm
is used to identify the time indices (change points) at which the system
transitions between these discrete states. We present a unified
information-based approach to testing for the existence of change points. This
new approach reconciles two previously disparate approaches to Change-Point
Analysis (frequentist and information-based) for testing transitions between
states. The resulting method is statistically principled, parameter and prior
free and widely applicable to a wide range of change-point problems.