pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Loop Closure Verification by Guided Sampling
Authors: Kanji Tanaka
Date: 2015-09-25 00:00:00
Tasks: Image Retrieval, Loop Closure Detection, Retrieval

Loop closure detection, the task of identifying locations revisited by a
robot in a sequence of odometry and perceptual observations, is typically
formulated as a combination of two subtasks: (1) bag-of-words image retrieval
and (2) post-verification using RANSAC geometric verification. The main
contribution of this study is the proposal of a novel post-verification
framework that achieves good precision recall trade-off in loop closure
detection. This study is motivated by the fact that not all loop closure
hypotheses are equally plausible (e.g., owing to mutual consistency between
loop closure constraints) and that if we have evidence that one hypothesis is
more plausible than the others, then it should be verified more frequently. We
demonstrate that the problem of loop closure detection can be viewed as an
instance of a multi-model hypothesize-and-verify framework and build guided
sampling strategies on the framework where loop closures proposed using image
retrieval are verified in a planned order (rather than in a conventional
uniform order) to operate in a constant time. Experimental results using a
stereo SLAM system confirm that the proposed strategy, the use of loop closure
constraints and robot trajectory hypotheses as a guide, achieves promising
results despite the fact that there exists a significant number of false
positive constraints and hypotheses.
A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
Authors: Bishan Yang, Claire Cardie, Peter Frazier
Date: 2015-04-22 00:00:00
Proceeding: TACL 2015 1
Tasks: Clustering, coreference-resolution, Coreference Resolution, Event Coreference Resolution, Nonparametric Clustering

We present a novel hierarchical distance-dependent Bayesian model for event
coreference resolution. While existing generative models for event coreference
resolution are completely unsupervised, our model allows for the incorporation
of pairwise distances between event mentions -- information that is widely used
in supervised coreference models to guide the generative clustering processing
for better event clustering both within and across documents. We model the
distances between event mentions using a feature-rich learnable distance
function and encode them as Bayesian priors for nonparametric clustering.
Experiments on the ECB+ corpus show that our model outperforms state-of-the-art
methods for both within- and cross-document event coreference resolution.
Constructing Abstraction Hierarchies Using a Skill-Symbol Loop
Authors: George Konidaris
Date: 2015-09-25 00:00:00

We describe a framework for building abstraction hierarchies whereby an agent
alternates skill- and representation-acquisition phases to construct a sequence
of increasingly abstract Markov decision processes. Our formulation builds on
recent results showing that the appropriate abstract representation of a
problem is specified by the agent's skills. We describe how such a hierarchy
can be used for fast planning, and illustrate the construction of an
appropriate hierarchy for the Taxi domain.
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Authors: Danica J. Sutherland, Junier B. Oliva, Barnabás Póczos, Jeff Schneider
Date: 2015-09-24 00:00:00
Tasks: BIG-bench Machine Learning

Many interesting machine learning problems are best posed by considering instances that are distributions, or sample sets drawn from distributions. Previous work devoted to machine learning tasks with distributional inputs has done so through pairwise kernel evaluations between pdfs (or sample sets). While such an approach is fine for smaller datasets, the computation of an $N \times N$ Gram matrix is prohibitive in large datasets. Recent scalable estimators that work over pdfs have done so only with kernels that use Euclidean metrics, like the $L_2$ distance. However, there are a myriad of other useful metrics available, such as total variation, Hellinger distance, and the Jensen-Shannon divergence. This work develops the first random features for pdfs whose dot product approximates kernels using these non-Euclidean metrics, allowing estimators using such kernels to scale to large datasets by working in a primal space, without computing large Gram matrices. We provide an analysis of the approximation error in using our proposed random features and show empirically the quality of our approximation both in estimating a Gram matrix and in solving learning tasks in real-world and synthetic data.
On Optimizing Human-Machine Task Assignments
Authors: Andreas Veit, Michael Wilber, Rajan Vaish, Serge Belongie, James Davis, Vishal Anand, Anshu Aviral, Prithvijit Chakrabarty, Yash Chandak, Sidharth Chaturvedi, Chinmaya Devaraj, Ankit Dhall, Utkarsh Dwivedi, Sanket Gupte, Sharath N. Sridhar, Karthik Paga, Anuj Pahuja, Aditya Raisinghani, Ayush Sharma, Shweta Sharma, Darpana Sinha, Nisarg Thakkar, K. Bala Vignesh, Utkarsh Verma, Kanniganti Abhishek, Amod Agrawal, Arya Aishwarya, Aurgho Bhattacharjee, Sarveshwaran Dhanasekar, Venkata Karthik Gullapalli, Shuchita Gupta, Chandana G, Kinjal Jain, Simran Kapur, Meghana Kasula, Shashi Kumar, Parth Kundaliya, Utkarsh Mathur, Alankrit Mishra, Aayush Mudgal, Aditya Nadimpalli, Munakala Sree Nihit, Akanksha Periwal, Ayush Sagar, Ayush Shah, Vikas Sharma, Yashovardhan Sharma, Faizal Siddiqui, Virender Singh, Abhinav S., Anurag. D. Yadav
Date: 2015-09-24 00:00:00

When crowdsourcing systems are used in combination with machine inference
systems in the real world, they benefit the most when the machine system is
deeply integrated with the crowd workers. However, if researchers wish to
integrate the crowd with "off-the-shelf" machine classifiers, this deep
integration is not always possible. This work explores two strategies to
increase accuracy and decrease cost under this setting. First, we show that
reordering tasks presented to the human can create a significant accuracy
improvement. Further, we show that greedily choosing parameters to maximize
machine accuracy is sub-optimal, and joint optimization of the combined system
improves performance.
Description of the Odin Event Extraction Framework and Rule Language
Authors: Marco A. Valenzuela-Escárcega, Gus Hahn-Powell, Mihai Surdeanu
Date: 2015-09-24 00:00:00
Tasks: Event Extraction

This document describes the Odin framework, which is a domain-independent
platform for developing rule-based event extraction models. Odin aims to be
powerful (the rule language allows the modeling of complex syntactic
structures) and robust (to recover from syntactic parsing errors, syntactic
patterns can be freely mixed with surface, token-based patterns), while
remaining simple (some domain grammars can be up and running in minutes), and
fast (Odin processes over 100 sentences/second in a real-world domain with over
200 rules). Here we include a thorough definition of the Odin rule language,
together with a description of the Odin API in the Scala language, which allows
one to apply these rules to arbitrary texts.
Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks
Authors: Zhiguang Wang, Tim Oates
Date: 2015-09-24 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

We propose an off-line approach to explicitly encode temporal patterns
spatially as different types of images, namely, Gramian Angular Fields and
Markov Transition Fields. This enables the use of techniques from computer
vision for feature learning and classification. We used Tiled Convolutional
Neural Networks to learn high-level features from individual GAF, MTF, and
GAF-MTF images on 12 benchmark time series datasets and two real
spatial-temporal trajectory datasets. The classification results of our
approach are competitive with state-of-the-art approaches on both types of
data. An analysis of the features and weights learned by the CNNs explains why
the approach works.
Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
Authors: Andreas Veit, Balazs Kovacs, Sean Bell, Julian McAuley, Kavita Bala, Serge Belongie
Date: 2015-09-24 00:00:00
Proceeding: ICCV 2015 12

With the rapid proliferation of smart mobile devices, users now take millions
of photos every day. These include large numbers of clothing and accessory
images. We would like to answer questions like `What outfit goes well with this
pair of shoes?' To answer these types of questions, one has to go beyond
learning visual similarity and learn a visual notion of compatibility across
categories. In this paper, we propose a novel learning framework to help answer
these types of questions. The main idea of this framework is to learn a feature
transformation from images of items into a latent space that expresses
compatibility. For the feature transformation, we use a Siamese Convolutional
Neural Network (CNN) architecture, where training examples are pairs of items
that are either compatible or incompatible. We model compatibility based on
co-occurrence in large-scale user behavior data; in particular co-purchase data
from Amazon.com. To learn cross-category fit, we introduce a strategic method
to sample training data, where pairs of items are heterogeneous dyads, i.e.,
the two elements of a pair belong to different high-level categories. While
this approach is applicable to a wide variety of settings, we focus on the
representative problem of learning compatible clothing style. Our results
indicate that the proposed framework is capable of learning semantic
information about visual style and is able to generate outfits of clothes, with
items from different categories, that go well together.
Convolutional Channel Features
Authors: Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
Date: 2015-04-28 00:00:00
Proceeding: ICCV 2015 12
Tasks: Edge Detection, Face Detection, Object Proposal Generation, Pedestrian Detection

Deep learning methods are powerful tools but often suffer from expensive
computation and limited flexibility. An alternative is to combine light-weight
models with deep representations. As successful cases exist in several visual
problems, a unified framework is absent. In this paper, we revisit two widely
used approaches in computer vision, namely filtered channel features and
Convolutional Neural Networks (CNN), and absorb merits from both by proposing
an integrated method called Convolutional Channel Features (CCF). CCF transfers
low-level features from pre-trained CNN models to feed the boosting forest
model. With the combination of CNN features and boosting forest, CCF benefits
from the richer capacity in feature representation compared with channel
features, as well as lower cost in computation and storage compared with
end-to-end CNN methods. We show that CCF serves as a good way of tailoring
pre-trained CNN models to diverse tasks without fine-tuning the whole network
to each task by achieving state-of-the-art performances in pedestrian
detection, face detection, edge detection and object proposal generation.
A Review of Feature Selection Methods Based on Mutual Information
Authors: Jorge R. Vergara, Pablo A. Estévez
Date: 2015-09-24 00:00:00
Tasks: feature selection

In this work we present a review of the state of the art of information
theoretic feature selection methods. The concepts of feature relevance,
redundance and complementarity (synergy) are clearly defined, as well as Markov
blanket. The problem of optimal feature selection is defined. A unifying
theoretical framework is described, which can retrofit successful heuristic
criteria, indicating the approximations made by each method. A number of open
problems in the field are presented.
Adaptive Sequential Optimization with Applications to Machine Learning
Authors: Craig Wilson, Venugopal V. Veeravalli
Date: 2015-09-24 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

A framework is introduced for solving a sequence of slowly changing
optimization problems, including those arising in regression and classification
applications, using optimization algorithms such as stochastic gradient descent
(SGD). The optimization problems change slowly in the sense that the minimizers
change at either a fixed or bounded rate. A method based on estimates of the
change in the minimizers and properties of the optimization algorithm is
introduced for adaptively selecting the number of samples needed from the
distributions underlying each problem in order to ensure that the excess risk,
i.e., the expected gap between the loss achieved by the approximate minimizer
produced by the optimization algorithm and the exact minimizer, does not exceed
a target level. Experiments with synthetic and real data are used to confirm
that this approach performs well.
Towards Real-time Customer Experience Prediction for Telecommunication Operators
Authors: Ernesto Diaz-Aviles, Fabio Pinelli, Karol Lynch, Zubair Nabi, Yiannis Gkoufas, Eric Bouillet, Francesco Calabrese, Eoin Coughlan, Peter Holland, Jason Salzwedel
Date: 2015-08-12 00:00:00

Telecommunications operators (telcos) traditional sources of income, voice
and SMS, are shrinking due to customers using over-the-top (OTT) applications
such as WhatsApp or Viber. In this challenging environment it is critical for
telcos to maintain or grow their market share, by providing users with as good
an experience as possible on their network.
  But the task of extracting customer insights from the vast amounts of data
collected by telcos is growing in complexity and scale everey day. How can we
measure and predict the quality of a user's experience on a telco network in
real-time? That is the problem that we address in this paper.
  We present an approach to capture, in (near) real-time, the mobile customer
experience in order to assess which conditions lead the user to place a call to
a telco's customer care center. To this end, we follow a supervised learning
approach for prediction and train our 'Restricted Random Forest' model using,
as a proxy for bad experience, the observed customer transactions in the telco
data feed before the user places a call to a customer care center.
  We evaluate our approach using a rich dataset provided by a major African
telecommunication's company and a novel big data architecture for both the
training and scoring of predictive models. Our empirical study shows our
solution to be effective at predicting user experience by inferring if a
customer will place a call based on his current context.
  These promising results open new possibilities for improved customer service,
which will help telcos to reduce churn rates and improve customer experience,
both factors that directly impact their revenue growth.
A marginal sampler for $σ$-Stable Poisson-Kingman mixture models
Authors: María Lomelí, Stefano Favaro, Yee Whye Teh
Date: 2014-07-16 00:00:00
Tasks: Clustering, Density Estimation

We investigate the class of $\sigma$-stable Poisson-Kingman random
probability measures (RPMs) in the context of Bayesian nonparametric mixture
modeling. This is a large class of discrete RPMs which encompasses most of the
the popular discrete RPMs used in Bayesian nonparametrics, such as the
Dirichlet process, Pitman-Yor process, the normalized inverse Gaussian process
and the normalized generalized Gamma process. We show how certain sampling
properties and marginal characterizations of $\sigma$-stable Poisson-Kingman
RPMs can be usefully exploited for devising a Markov chain Monte Carlo (MCMC)
algorithm for making inference in Bayesian nonparametric mixture modeling.
Specifically, we introduce a novel and efficient MCMC sampling scheme in an
augmented space that has a fixed number of auxiliary variables per iteration.
We apply our sampling scheme for a density estimation and clustering tasks with
unidimensional and multidimensional datasets, and we compare it against
competing sampling schemes.
Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks
Authors: Cristian Jimenez-Romero, David Sousa-Rodrigues, Jeffrey H. Johnson
Date: 2015-09-23 00:00:00

This study explores the design and control of the behaviour of agents and
robots using simple circuits of spiking neurons and Spike Timing Dependent
Plasticity (STDP) as a mechanism of associative and unsupervised learning.
Based on a "reward and punishment" classical conditioning, it is demonstrated
that these robots learnt to identify and avoid obstacles as well as to identify
and look for rewarding stimuli. Using the simulation and programming
environment NetLogo, a software engine for the Integrate and Fire model was
developed, which allowed us to monitor in discrete time steps the dynamics of
each single neuron, synapse and spike in the proposed neural networks. These
spiking neural networks (SNN) served as simple brains for the experimental
robots. The Lego Mindstorms robot kit was used for the embodiment of the
simulated agents. In this paper the topological building blocks are presented
as well as the neural parameters required to reproduce the experiments. This
paper summarizes the resulting behaviour as well as the observed dynamics of
the neural circuits. The Internet-link to the NetLogo code is included in the
annex.
Semantic Image Segmentation via Deep Parsing Network
Authors: Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2015-09-09 00:00:00
Proceeding: ICCV 2015 12
Tasks: GPU, Image Segmentation, Semantic Segmentation

This paper addresses semantic image segmentation by incorporating rich
information into Markov Random Field (MRF), including high-order relations and
mixture of label contexts. Unlike previous works that optimized MRFs using
iterative algorithm, we solve MRF by proposing a Convolutional Neural Network
(CNN), namely Deep Parsing Network (DPN), which enables deterministic
end-to-end computation in a single forward pass. Specifically, DPN extends a
contemporary CNN architecture to model unary terms and additional layers are
carefully devised to approximate the mean field algorithm (MF) for pairwise
terms. It has several appealing properties. First, different from the recent
works that combined CNN and MRF, where many iterations of MF were required for
each training image during back-propagation, DPN is able to achieve high
performance by approximating one iteration of MF. Second, DPN represents
various types of pairwise terms, making many existing works as its special
cases. Third, DPN makes MF easier to be parallelized and speeded up in
Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC
2012 dataset, where a single DPN model yields a new state-of-the-art
segmentation accuracy.
Deep Learning Face Attributes in the Wild
Authors: Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang
Date: 2014-11-28 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Deep Learning, Facial Attribute Classification

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.
Opinion mining from twitter data using evolutionary multinomial mixture models
Authors: Md. Abul Hasnat, Julien Velcin, Stéphane Bonnevay, Julien Jacques
Date: 2015-09-24 00:00:00
Tasks: Clustering, Opinion Mining, Sociology

Image of an entity can be defined as a structured and dynamic representation
which can be extracted from the opinions of a group of users or population.
Automatic extraction of such an image has certain importance in political
science and sociology related studies, e.g., when an extended inquiry from
large-scale data is required. We study the images of two politically
significant entities of France. These images are constructed by analyzing the
opinions collected from a well known social media called Twitter. Our goal is
to build a system which can be used to automatically extract the image of
entities over time.
  In this paper, we propose a novel evolutionary clustering method based on the
parametric link among Multinomial mixture models. First we propose the
formulation of a generalized model that establishes parametric links among the
Multinomial distributions. Afterward, we follow a model-based clustering
approach to explore different parametric sub-models and select the best model.
For the experiments, first we use synthetic temporal data. Next, we apply the
method to analyze the annotated social media data. Results show that the
proposed method is better than the state-of-the-art based on the common
evaluation metrics. Additionally, our method can provide interpretation about
the temporal evolution of the clusters.
An Asymptotically Optimal Policy for Uniform Bandits of Unknown Support
Authors: Wesley Cowan, Michael N. Katehakis
Date: 2015-05-08 00:00:00

Consider the problem of a controller sampling sequentially from a finite
number of $N \geq 2$ populations, specified by random variables $X^i_k$, $ i =
1,\ldots , N,$ and $k = 1, 2, \ldots$; where $X^i_k$ denotes the outcome from
population $i$ the $k^{th}$ time it is sampled. It is assumed that for each
fixed $i$, $\{ X^i_k \}_{k \geq 1}$ is a sequence of i.i.d. uniform random
variables over some interval $[a_i, b_i]$, with the support (i.e., $a_i, b_i$)
unknown to the controller. The objective is to have a policy $\pi$ for
deciding, based on available data, from which of the $N$ populations to sample
from at any time $n=1,2,\ldots$ so as to maximize the expected sum of outcomes
of $n$ samples or equivalently to minimize the regret due to lack on
information of the parameters $\{ a_i \}$ and $\{ b_i \}$. In this paper, we
present a simple inflated sample mean (ISM) type policy that is asymptotically
optimal in the sense of its regret achieving the asymptotic lower bound of
Burnetas and Katehakis (1996). Additionally, finite horizon regret bounds are
given.
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
Authors: Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2015-06-30 00:00:00
Proceeding: CVPR 2015 6
Tasks: Fine-Grained Image Classification, General Classification

Updated on 24/09/2015: This update provides preliminary experiment results
for fine-grained classification on the surveillance data of CompCars. The
train/test splits are provided in the updated dataset. See details in Section
6.