pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical Analysis of Active Contours on Graphs
Authors: Christos Sakaridis, Kimon Drakopoulos, Petros Maragos
Date: 2016-10-24 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Active contour models based on partial differential equations have proved
successful in image segmentation, yet the study of their geometric formulation
on arbitrary geometric graphs is still at an early stage. In this paper, we
introduce geometric approximations of gradient and curvature, which are used in
the geodesic active contour model. We prove convergence in probability of our
gradient approximation to the true gradient value and derive an asymptotic
upper bound for the error of this approximation for the class of random
geometric graphs. Two different approaches for the approximation of curvature
are presented and both are also proved to converge in probability in the case
of random geometric graphs. We propose neighborhood-based filtering on graphs
to improve the accuracy of the aforementioned approximations and define two
variants of Gaussian smoothing on graphs which include normalization in order
to adapt to graph non-uniformities. The performance of our active contour
framework on graphs is demonstrated in the segmentation of regular images and
geographical data defined on arbitrary graphs.
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
Authors: Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher
Date: 2016-10-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Bayesian Optimization, Gaussian Processes

We present a new algorithm, truncated variance reduction (TruVaR), that
treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian
processes in a unified fashion. The algorithm greedily shrinks a sum of
truncated variances within a set of potential maximizers (BO) or unclassified
points (LSE), which is updated based on confidence bounds. TruVaR is effective
in several important settings that are typically non-trivial to incorporate
into myopic algorithms, including pointwise costs and heteroscedastic noise. We
provide a general theoretical guarantee for TruVaR covering these aspects, and
use it to recover and strengthen existing results on BO and LSE. Moreover, we
provide a new result for a setting where one can select from a number of noise
levels having associated costs. We demonstrate the effectiveness of the
algorithm on both synthetic and real-world data sets.
Deep Learning with Differential Privacy
Authors: Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Date: 2016-07-01 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Machine learning techniques based on neural networks are achieving remarkable
results in a wide variety of domains. Often, the training of models requires
large, representative datasets, which may be crowdsourced and contain sensitive
information. The models should not expose private information in these
datasets. Addressing this goal, we develop new algorithmic techniques for
learning and a refined analysis of privacy costs within the framework of
differential privacy. Our implementation and experiments demonstrate that we
can train deep neural networks with non-convex objectives, under a modest
privacy budget, and at a manageable cost in software complexity, training
efficiency, and model quality.
Introduction: Cognitive Issues in Natural Language Processing
Authors: Thierry Poibeau, Shravan Vasishth
Date: 2016-10-24 00:00:00
Tasks: Language Modeling, Language Modelling

This special issue is dedicated to get a better picture of the relationships
between computational linguistics and cognitive science. It specifically raises
two questions: "what is the potential contribution of computational language
modeling to cognitive science?" and conversely: "what is the influence of
cognitive science in contemporary computational linguistics?"
Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media
Authors: Arkaitz Zubiaga, Maria Liakata, Rob Procter
Date: 2016-10-24 00:00:00
Tasks: Rumour Detection

Breaking news leads to situations of fast-paced reporting in social media,
producing all kinds of updates related to news stories, albeit with the caveat
that some of those early updates tend to be rumours, i.e., information with an
unverified status at the time of posting. Flagging information that is
unverified can be helpful to avoid the spread of information that may turn out
to be false. Detection of rumours can also feed a rumour tracking system that
ultimately determines their veracity. In this paper we introduce a novel
approach to rumour detection that learns from the sequential dynamics of
reporting during breaking news in social media to detect rumours in new
stories. Using Twitter datasets collected during five breaking news stories, we
experiment with Conditional Random Fields as a sequential classifier that
leverages context learnt during an event for rumour detection, which we compare
with the state-of-the-art rumour detection system as well as other baselines.
In contrast to existing work, our classifier does not need to observe tweets
querying a piece of information to deem it a rumour, but instead we detect
rumours from the tweet alone by exploiting context learnt during the event. Our
classifier achieves competitive performance, beating the state-of-the-art
classifier that relies on querying tweets with improved precision and recall,
as well as outperforming our best baseline with nearly 40% improvement in terms
of F1 score. The scale and diversity of our experiments reinforces the
generalisability of our classifier.
The infochemical core
Authors: Antoni Hernández-Fernández, Ramon Ferrer-i-Cancho
Date: 2016-10-18 00:00:00

Vocalizations and less often gestures have been the object of linguistic
research over decades. However, the development of a general theory of
communication with human language as a particular case requires a clear
understanding of the organization of communication through other means.
Infochemicals are chemical compounds that carry information and are employed by
small organisms that cannot emit acoustic signals of optimal frequency to
achieve successful communication. Here the distribution of infochemicals across
species is investigated when they are ranked by their degree or the number of
species with which it is associated (because they produce or they are sensitive
to it). The quality of the fit of different functions to the dependency between
degree and rank is evaluated with a penalty for the number of parameters of the
function. Surprisingly, a double Zipf (a Zipf distribution with two regimes
with a different exponent each) is the model yielding the best fit although it
is the function with the largest number of parameters. This suggests that the
world wide repertoire of infochemicals contains a chemical nucleus shared by
many species and reminiscent of the core vocabularies found for human language
in dictionaries or large corpora.
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
Authors: Steffen Urban, Stefan Hinz
Date: 2016-10-24 00:00:00
Tasks: Position, Self-Driving Cars, Simultaneous Localization and Mapping

The basis for most vision based applications like robotics, self-driving cars
and potentially augmented and virtual reality is a robust, continuous
estimation of the position and orientation of a camera system w.r.t the
observed environment (scene). In recent years many vision based systems that
perform simultaneous localization and mapping (SLAM) have been presented and
released as open source. In this paper, we extend and improve upon a
state-of-the-art SLAM to make it applicable to arbitrary, rigidly coupled
multi-camera systems (MCS) using the MultiCol model. In addition, we include a
performance evaluation on accurate ground truth and compare the robustness of
the proposed method to a single camera version of the SLAM system. An open
source implementation of the proposed multi-fisheye camera SLAM system can be
found on-line https://github.com/urbste/MultiCol-SLAM.
A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds
Authors: Xiaoshui Huang, Jian Zhang, Qiang Wu, Lixin Fan, Chun Yuan
Date: 2016-10-24 00:00:00

With the development of numerous 3D sensing technologies, object registration
on cross-source point cloud has aroused researchers' interests. When the point
clouds are captured from different kinds of sensors, there are large and
different kinds of variations. In this study, we address an even more
challenging case in which the differently-source point clouds are acquired from
a real street view. One is produced directly by the LiDAR system and the other
is generated by using VSFM software on image sequence captured from RGB
cameras. When it confronts to large scale point clouds, previous methods mostly
focus on point-to-point level registration, and the methods have many
limitations.The reason is that the least mean error strategy shows poor ability
in registering large variable cross-source point clouds. In this paper,
different from previous ICP-based methods, and from a statistic view, we
propose a effective coarse-to-fine algorithm to detect and register a small
scale SFM point cloud in a large scale Lidar point cloud. Seen from the
experimental results, the model can successfully run on LiDAR and SFM point
clouds, hence it can make a contribution to many applications, such as robotics
and smart city development.
ICE: Information Credibility Evaluation on Social Media via Representation Learning
Authors: Qiang Liu, Shu Wu, Feng Yu, Liang Wang, Tieniu Tan
Date: 2016-09-29 00:00:00
Tasks: Feature Engineering, Representation Learning

With the rapid growth of social media, rumors are also spreading widely on
social media and bring harm to people's daily life. Nowadays, information
credibility evaluation has drawn attention from academic and industrial
communities. Current methods mainly focus on feature engineering and achieve
some success. However, feature engineering based methods require a lot of labor
and cannot fully reveal the underlying relations among data. In our viewpoint,
the key elements of user behaviors for evaluating credibility are concluded as
"who", "what", "when", and "how". These existing methods cannot model the
correlation among different key elements during the spreading of microblogs. In
this paper, we propose a novel representation learning method, Information
Credibility Evaluation (ICE), to learn representations of information
credibility on social media. In ICE, latent representations are learnt for
modeling user credibility, behavior types, temporal properties, and comment
attitudes. The aggregation of these factors in the microblog spreading process
yields the representation of a user's behavior, and the aggregation of these
dynamic representations generates the credibility representation of an event
spreading on social media. Moreover, a pairwise learning method is applied to
maximize the credibility difference between rumors and non-rumors. To evaluate
the performance of ICE, we conduct experiments on a Sina Weibo data set, and
the experimental results show that our ICE model outperforms the
state-of-the-art methods.
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
Authors: ZongYuan Ge, Chris McCool, Conrad Sanderson, Peng Wang, Lingqiao Liu, Ian Reid, Peter Corke
Date: 2016-08-01 00:00:00
Tasks: Classification, General Classification

Fine-grained classification is a relatively new field that has concentrated
on using information from a single image, while ignoring the enormous potential
of using video data to improve classification. In this work we present the
novel task of video-based fine-grained object classification, propose a
corresponding new video dataset, and perform a systematic study of several
recent deep convolutional neural network (DCNN) based approaches, which we
specifically adapt to the task. We evaluate three-dimensional DCNNs, two-stream
DCNNs, and bilinear DCNNs. Two forms of the two-stream approach are used, where
spatial and temporal data from two independent DCNNs are fused either via early
fusion (combination of the fully-connected layers) and late fusion
(concatenation of the softmax outputs of the DCNNs). For bilinear DCNNs,
information from the convolutional layers of the spatial and temporal DCNNs is
combined via local co-occurrences. We then fuse the bilinear DCNN and early
fusion of the two-stream approach to combine the spatial and temporal
information at the local and global level (Spatio-Temporal Co-occurrence).
Using the new and challenging video dataset of birds, classification
performance is improved from 23.1% (using single images) to 41.1% when using
the Spatio-Temporal Co-occurrence system. Incorporating automatically detected
bounding box location further improves the classification accuracy to 53.6%.
Search Improves Label for Active Learning
Authors: Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang
Date: 2016-02-23 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Active Learning

We investigate active learning with access to two distinct oracles: Label
(which is standard) and Search (which is not). The Search oracle models the
situation where a human searches a database to seed or counterexample an
existing solution. Search is stronger than Label while being natural to
implement in many situations. We show that an algorithm using both oracles can
provide exponentially large problem-dependent improvements over Label alone.
Bridging Neural Machine Translation and Bilingual Dictionaries
Authors: Jiajun Zhang, Cheng-qing Zong
Date: 2016-10-24 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Neural Machine Translation (NMT) has become the new state-of-the-art in
several language pairs. However, it remains a challenging problem how to
integrate NMT with a bilingual dictionary which mainly contains words rarely or
never seen in the bilingual training data. In this paper, we propose two
methods to bridge NMT and the bilingual dictionaries. The core idea behind is
to design novel models that transform the bilingual dictionaries into adequate
sentence pairs, so that NMT can distil latent bilingual mappings from the ample
and repetitive phenomena. One method leverages a mixed word/character model and
the other attempts at synthesizing parallel sentences guaranteeing massive
occurrence of the translation lexicon. Extensive experiments demonstrate that
the proposed methods can remarkably improve the translation quality, and most
of the rare words in the test sentences can obtain correct translations if they
are covered by the dictionary.
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers
Authors: Wentao Luan, Yezhou Yang, Cornelia Fermuller, John Baras
Date: 2016-09-12 00:00:00
Tasks: Attribute, Decision Making, General Classification, Object Recognition, Vocal Bursts Intensity Prediction

We consider the problem of object recognition in 3D using an ensemble of
attribute-based classifiers. We propose two new concepts to improve
classification in practical situations, and show their implementation in an
approach implemented for recognition from point-cloud data. First, the viewing
conditions can have a strong influence on classification performance. We study
the impact of the distance between the camera and the object and propose an
approach to fuse multiple attribute classifiers, which incorporates distance
into the decision making. Second, lack of representative training samples often
makes it difficult to learn the optimal threshold value for best positive and
negative detection rate. We address this issue, by setting in our attribute
classifiers instead of just one threshold value, two threshold values to
distinguish a positive, a negative and an uncertainty class, and we prove the
theoretical correctness of this approach. Empirical studies demonstrate the
effectiveness and feasibility of the proposed concepts.
Boltzmann-Machine Learning of Prior Distributions of Binarized Natural Images
Authors: Tomoyuki Obuchi, Hirokazu Koma, Muneki Yasuda
Date: 2014-12-16 00:00:00
Tasks: BIG-bench Machine Learning

Prior distributions of binarized natural images are learned by using a
Boltzmann machine. According the results of this study, there emerges a
structure with two sublattices in the interactions, and the nearest-neighbor
and next-nearest-neighbor interactions correspondingly take two discriminative
values, which reflects the individual characteristics of the three sets of
pictures that we process. Meanwhile, in a longer spatial scale, a longer-range,
although still rapidly decaying, ferromagnetic interaction commonly appears in
all cases. The characteristic length scale of the interactions is universally
up to approximately four lattice spacings $\xi \approx 4$. These results are
derived by using the mean-field method, which effectively reduces the
computational time required in a Boltzmann machine. An improved mean-field
method called the Bethe approximation also gives the same results, as well as
the Monte Carlo method does for small size images. These reinforce the validity
of our analysis and findings. Relations to criticality, frustration, and
simple-cell receptive fields are also discussed.
Learning Word Sense Embeddings from Word Sense Definitions
Authors: Qi Li, Tianshi Li, Baobao Chang
Date: 2016-06-15 00:00:00
Tasks: Word Embeddings, Word Sense Disambiguation, Word Similarity

Word embeddings play a significant role in many modern NLP systems. Since
learning one representation per word is problematic for polysemous words and
homonymous words, researchers propose to use one embedding per word sense.
Their approaches mainly train word sense embeddings on a corpus. In this paper,
we propose to use word sense definitions to learn one embedding per word sense.
Experimental results on word similarity tasks and a word sense disambiguation
task show that word sense embeddings produced by our approach are of high
quality.
Proposing Plausible Answers for Open-ended Visual Question Answering
Authors: Omid Bakhshandeh, Trung Bui, Zhe Lin, Walter Chang
Date: 2016-10-20 00:00:00
Tasks: Graph Matching, Open-Ended Question Answering, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Answering open-ended questions is an essential capability for any intelligent
agent. One of the most interesting recent open-ended question answering
challenges is Visual Question Answering (VQA) which attempts to evaluate a
system's visual understanding through its answers to natural language questions
about images. There exist many approaches to VQA, the majority of which do not
exhibit deeper semantic understanding of the candidate answers they produce. We
study the importance of generating plausible answers to a given question by
introducing the novel task of `Answer Proposal': for a given open-ended
question, a system should generate a ranked list of candidate answers informed
by the semantics of the question. We experiment with various models including a
neural generative model as well as a semantic graph matching one. We provide
both intrinsic and extrinsic evaluations for the task of Answer Proposal,
showing that our best model learns to propose plausible answers with a high
recall and performs competitively with some other solutions to VQA.
A Multi-Batch L-BFGS Method for Machine Learning
Authors: Albert S. Berahas, Jorge Nocedal, Martin Takáč
Date: 2016-05-19 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: BIG-bench Machine Learning, Distributed Computing

The question of how to parallelize the stochastic gradient descent (SGD)
method has received much attention in the literature. In this paper, we focus
instead on batch methods that use a sizeable fraction of the training set at
each iteration to facilitate parallelism, and that employ second-order
information. In order to improve the learning process, we follow a multi-batch
approach in which the batch changes at each iteration. This can cause
difficulties because L-BFGS employs gradient differences to update the Hessian
approximations, and when these gradients are computed using different data
points the process can be unstable. This paper shows how to perform stable
quasi-Newton updating in the multi-batch setting, illustrates the behavior of
the algorithm in a distributed computing platform, and studies its convergence
properties for both the convex and nonconvex cases.
SPiKeS: Superpixel-Keypoints Structure for Robust Visual Tracking
Authors: François-Xavier Derue, Guillaume-Alexandre Bilodeau, Robert Bergevin
Date: 2016-10-23 00:00:00
Tasks: Superpixels, Visual Tracking

In visual tracking, part-based trackers are attractive since they are robust
against occlusion and deformation. However, a part represented by a rectangular
patch does not account for the shape of the target, while a superpixel does
thanks to its boundary evidence. Nevertheless, tracking superpixels is
difficult due to their lack of discriminative power. Therefore, to enable
superpixels to be tracked discriminatively as object parts, we propose to
enhance them with keypoints. By combining properties of these two features, we
build a novel element designated as a Superpixel-Keypoints structure (SPiKeS).
Being discriminative, these new object parts can be located efficiently by a
simple nearest neighbor matching process. Then, in a tracking process, each
match votes for the target's center to give its location. In addition, the
interesting properties of our new feature allows the development of an
efficient model update for more robust tracking. According to experimental
results, our SPiKeS-based tracker proves to be robust in many challenging
scenarios by performing favorably against the state-of-the-art.
Template Matching Advances and Applications in Image Analysis
Authors: Nazanin Sadat Hashemi, Roya Babaie Aghdam, Atieh Sadat Bayat Ghiasi, Parastoo Fatemi
Date: 2016-10-23 00:00:00
Tasks: Decision Making, Image Registration, Medical Image Analysis, object-detection, Object Detection, Object Recognition, Template Matching

In most computer vision and image analysis problems, it is necessary to
define a similarity measure between two or more different objects or images.
Template matching is a classic and fundamental method used to score
similarities between objects using certain mathematical algorithms. In this
paper, we reviewed the basic concept of matching, as well as advances in
template matching and applications such as invariant features or novel
applications in medical image analysis. Additionally, deformable models and
templates originating from classic template matching were discussed. These
models have broad applications in image registration, and they are a
fundamental aspect of novel machine vision or deep learning algorithms, such as
convolutional neural networks (CNN), which perform shift and scale invariant
functions followed by classification. In general, although template matching
methods have restrictions which limit their application, they are recommended
for use with other object recognition methods as pre- or post-processing steps.
Combining a template matching technique such as normalized cross-correlation or
dice coefficient with a robust decision-making algorithm yields a significant
improvement in the accuracy rate for object detection and recognition.