pwc-archive/papers-with-abstracts huggingface dataset:
Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor
Authors: Wongun Choi
Date: 2015-04-09 00:00:00
Proceeding: ICCV 2015 12
Tasks: Multiple Object Tracking

In this paper, we focus on the two key aspects of multiple target tracking
problem: 1) designing an accurate affinity measure to associate detections and
2) implementing an efficient and accurate (near) online multiple target
tracking algorithm. As the first contribution, we introduce a novel Aggregated
Local Flow Descriptor (ALFD) that encodes the relative motion pattern between a
pair of temporally distant detections using long term interest point
trajectories (IPTs). Leveraging on the IPTs, the ALFD provides a robust
affinity measure for estimating the likelihood of matching detections
regardless of the application scenarios. As another contribution, we present a
Near-Online Multi-target Tracking (NOMT) algorithm. The tracking problem is
formulated as a data-association between targets and detections in a temporal
window, that is performed repeatedly at every frame. While being efficient,
NOMT achieves robustness via integrating multiple cues including ALFD metric,
target dynamics, appearance similarity, and long term trajectory regularization
into the model. Our ablative analysis verifies the superiority of the ALFD
metric over the other conventional affinity metrics. We run a comprehensive
experimental evaluation on two challenging tracking datasets, KITTI and MOT
datasets. The NOMT method combined with ALFD metric achieves the best accuracy
in both datasets with significant margins (about 10% higher MOTA) over the
state-of-the-arts.
Techniques for Learning Binary Stochastic Feedforward Neural Networks
Authors: Tapani Raiko, Mathias Berglund, Guillaume Alain, Laurent Dinh
Date: 2014-06-11 00:00:00
Tasks: Structured Prediction

Stochastic binary hidden units in a multi-layer perceptron (MLP) network give
at least three potential benefits when compared to deterministic MLP networks.
(1) They allow to learn one-to-many type of mappings. (2) They can be used in
structured prediction problems, where modeling the internal structure of the
output is important. (3) Stochasticity has been shown to be an excellent
regularizer, which makes generalization performance potentially better in
general. However, training stochastic networks is considerably more difficult.
We study training using M samples of hidden activations per input. We show that
the case M=1 leads to a fundamentally different behavior where the network
tries to avoid stochasticity. We propose two new estimators for the training
gradient and propose benchmark tests for comparing training algorithms. Our
experiments confirm that training stochastic networks is difficult and show
that the proposed two estimators perform favorably among all the five known
estimators.
An Optimized Hybrid Approach for Path Finding
Authors: Ahlam Ansari, Mohd Amin Sayyed, Khatija Ratlamwala, Parvin Shaikh
Date: 2015-04-09 00:00:00

Path finding algorithm addresses problem of finding shortest path from source
to destination avoiding obstacles. There exist various search algorithms namely
A*, Dijkstra's and ant colony optimization. Unlike most path finding algorithms
which require destination co-ordinates to compute path, the proposed algorithm
comprises of a new method which finds path using backtracking without requiring
destination co-ordinates. Moreover, in existing path finding algorithm, the
number of iterations required to find path is large. Hence, to overcome this,
an algorithm is proposed which reduces number of iterations required to
traverse the path. The proposed algorithm is hybrid of backtracking and a new
technique(modified 8- neighbor approach). The proposed algorithm can become
essential part in location based, network, gaming applications. grid traversal,
navigation, gaming applications, mobile robot and Artificial Intelligence.
On mining complex sequential data by means of FCA and pattern structures
Authors: Aleksey Buzmakov, Elias Egho, Nicolas Jay, Sergei O. Kuznetsov, Amedeo Napoli, Chedy Raïssi
Date: 2015-04-09 00:00:00
Tasks: Marketing

Nowadays data sets are available in very complex and heterogeneous ways.
Mining of such data collections is essential to support many real-world
applications ranging from healthcare to marketing. In this work, we focus on
the analysis of "complex" sequential data by means of interesting sequential
patterns. We approach the problem using the elegant mathematical framework of
Formal Concept Analysis (FCA) and its extension based on "pattern structures".
Pattern structures are used for mining complex data (such as sequences or
graphs) and are based on a subsumption operation, which in our case is defined
with respect to the partial order on sequences. We show how pattern structures
along with projections (i.e., a data reduction of sequential structures), are
able to enumerate more meaningful patterns and increase the computing
efficiency of the approach. Finally, we show the applicability of the presented
method for discovering and analyzing interesting patient patterns from a French
healthcare data set on cancer. The quantitative and qualitative results (with
annotations and analysis from a physician) are reported in this use case which
is the main motivation for this work.
  Keywords: data mining; formal concept analysis; pattern structures;
projections; sequences; sequential data.
Extraction of Protein Sequence Motif Information using PSO K-Means
Authors: R. Gowri, R. Rathipriya
Date: 2015-04-09 00:00:00
Tasks: Clustering

The main objective of the paper is to find the motif information.The
functionalities of the proteins are ideally found from their motif information
which is extracted using various techniques like clustering with k-means,
hybrid k-means, self-organising maps, etc., in the literature. In this work
protein sequence information is extracted using optimised k-means algorithm.
The particle swarm optimisation technique is one of the frequently used
optimisation method. In the current work the PSO k-means is used for motif
information extraction. This paper also deals with the comparison between the
motif information obtained from clusters and biclustersusing PSO k-means
algorithm. The motif information acquired is based on the structure homogeneity
of the protein sequence.
Phrase-based Image Captioning
Authors: Rémi Lebret, Pedro O. Pinheiro, Ronan Collobert
Date: 2015-02-12 00:00:00
Tasks: Descriptive, Image Captioning, Language Modeling, Language Modelling

Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We train a purely bilinear model that learns a metric between an
image representation (generated from a previously trained Convolutional Neural
Network) and phrases that are used to described them. The system is then able
to infer phrases from a given image sample. Based on caption syntax statistics,
we propose a simple language model that can produce relevant descriptions for a
given test image using the phrases inferred. Our approach, which is
considerably simpler than state-of-the-art models, achieves comparable results
in two popular datasets for the task: Flickr30k and the recently proposed
Microsoft COCO.
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet
Authors: Matthias Kümmerer, Lucas Theis, Matthias Bethge
Date: 2014-11-04 00:00:00
Tasks: Object Recognition, Point Processes, Saliency Prediction

Recent results suggest that state-of-the-art saliency models perform far from
optimal in predicting fixations. This lack in performance has been attributed
to an inability to model the influence of high-level image features such as
objects. Recent seminal advances in applying deep neural networks to tasks like
object recognition suggests that they are able to capture this kind of
structure. However, the enormous amount of training data necessary to train
these networks makes them difficult to apply directly to saliency prediction.
We present a novel way of reusing existing neural networks that have been
pretrained on the task of object recognition in models of fixation prediction.
Using the well-known network of Krizhevsky et al. (2012), we come up with a new
saliency model that significantly outperforms all state-of-the-art models on
the MIT Saliency Benchmark. We show that the structure of this network allows
new insights in the psychophysics of fixation selection and potentially their
neural implementation. To train our network, we build on recent work on the
modeling of saliency as point processes.
Bayesian estimation of the multifractality parameter for image texture using a Whittle approximation
Authors: Sébastien Combrexelle, Herwig Wendt, Nicolas Dobigeon, Jean-Yves Tourneret, Steve McLaughlin, Patrice Abry
Date: 2014-10-17 00:00:00
Tasks: parameter estimation

Texture characterization is a central element in many image processing
applications. Multifractal analysis is a useful signal and image processing
tool, yet, the accurate estimation of multifractal parameters for image texture
remains a challenge. This is due in the main to the fact that current
estimation procedures consist of performing linear regressions across frequency
scales of the two-dimensional (2D) dyadic wavelet transform, for which only a
few such scales are computable for images. The strongly non-Gaussian nature of
multifractal processes, combined with their complicated dependence structure,
makes it difficult to develop suitable models for parameter estimation. Here,
we propose a Bayesian procedure that addresses the difficulties in the
estimation of the multifractality parameter. The originality of the procedure
is threefold: The construction of a generic semi-parametric statistical model
for the logarithm of wavelet leaders; the formulation of Bayesian estimators
that are associated with this model and the set of parameter values admitted by
multifractal theory; the exploitation of a suitable Whittle approximation
within the Bayesian model which enables the otherwise infeasible evaluation of
the posterior distribution associated with the model. Performance is assessed
numerically for several 2D multifractal processes, for several image sizes and
a large range of process parameters. The procedure yields significant benefits
over current benchmark estimators in terms of estimation performance and
ability to discriminate between the two most commonly used classes of
multifractal process models. The gains in performance are particularly
pronounced for small image sizes, notably enabling for the first time the
analysis of image patches as small as 64x64 pixels.
Sample Complexity of Dictionary Learning and other Matrix Factorizations
Authors: Rémi Gribonval, Rodolphe Jenatton, Francis Bach, Martin Kleinsteuber, Matthias Seibert
Date: 2013-12-13 00:00:00
Tasks: Clustering, Dictionary Learning, Generalization Bounds

Many modern tools in machine learning and signal processing, such as sparse
dictionary learning, principal component analysis (PCA), non-negative matrix
factorization (NMF), $K$-means clustering, etc., rely on the factorization of a
matrix obtained by concatenating high-dimensional vectors from a training
collection. While the idealized task would be to optimize the expected quality
of the factors over the underlying distribution of training vectors, it is
achieved in practice by minimizing an empirical average over the considered
collection. The focus of this paper is to provide sample complexity estimates
to uniformly control how much the empirical average deviates from the expected
cost function. Standard arguments imply that the performance of the empirical
predictor also exhibit such guarantees. The level of genericity of the approach
encompasses several possible constraints on the factors (tensor product
structure, shift-invariance, sparsity \ldots), thus providing a unified
perspective on the sample complexity of several widely used matrix
factorization schemes. The derived generalization bounds behave proportional to
$\sqrt{\log(n)/n}$ w.r.t.\ the number of samples $n$ for the considered matrix
factorization techniques.
`local' vs. `global' parameters -- breaking the gaussian complexity barrier
Authors: Shahar Mendelson
Date: 2015-04-09 00:00:00

We show that if $F$ is a convex class of functions that is $L$-subgaussian,
the error rate of learning problems generated by independent noise is
equivalent to a fixed point determined by `local' covering estimates of the
class, rather than by the gaussian averages. To that end, we establish new
sharp upper and lower estimates on the error rate for such problems.
Mid-level Deep Pattern Mining
Authors: Yao Li, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel
Date: 2014-11-24 00:00:00
Proceeding: CVPR 2015 6

Mid-level visual element discovery aims to find clusters of image patches
that are both representative and discriminative. In this work, we study this
problem from the prospective of pattern mining while relying on the recently
popularized Convolutional Neural Networks (CNNs). Specifically, we find that
for an image patch, activations extracted from the first fully-connected layer
of CNNs have two appealing properties which enable its seamless integration
with pattern mining. Patterns are then discovered from a large number of CNN
activations of image patches through the well-known association rule mining.
When we retrieve and visualize image patches with the same pattern,
surprisingly, they are not only visually similar but also semantically
consistent. We apply our approach to scene and object classification tasks, and
demonstrate that our approach outperforms all previous works on mid-level
visual element discovery by a sizeable margin with far fewer elements being
used. Our approach also outperforms or matches recent works using CNN for these
tasks. Source code of the complete system is available online.
Generative Class-conditional Autoencoders
Authors: Jan Rudy, Graham Taylor
Date: 2014-12-22 00:00:00
Tasks: Denoising

Recent work by Bengio et al. (2013) proposes a sampling procedure for
denoising autoencoders which involves learning the transition operator of a
Markov chain. The transition operator is typically unimodal, which limits its
capacity to model complex data. In order to perform efficient sampling from
conditional distributions, we extend this work, both theoretically and
algorithmically, to gated autoencoders (Memisevic, 2013), The proposed model is
able to generate convincing class-conditional samples when trained on both the
MNIST and TFD datasets.
Understanding Locally Competitive Networks
Authors: Rupesh Kumar Srivastava, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber
Date: 2014-10-05 00:00:00
Tasks: Retrieval

Recently proposed neural network activation functions such as rectified
linear, maxout, and local winner-take-all have allowed for faster and more
effective training of deep neural architectures on large and complex datasets.
The common trait among these functions is that they implement local competition
between small groups of computational units within a layer, so that only part
of the network is activated for any given input pattern. In this paper, we
attempt to visualize and understand this self-modularization, and suggest a
unified explanation for the beneficial properties of such networks. We also
show how our insights can be directly useful for efficiently performing
retrieval over large datasets using neural networks.
Exploring Lexical, Syntactic, and Semantic Features for Chinese Textual Entailment in NTCIR RITE Evaluation Tasks
Authors: Wei-Jie Huang, Chao-Lin Liu
Date: 2015-04-08 00:00:00
Tasks: Binary Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Negation

We computed linguistic information at the lexical, syntactic, and semantic
levels for Recognizing Inference in Text (RITE) tasks for both traditional and
simplified Chinese in NTCIR-9 and NTCIR-10. Techniques for syntactic parsing,
named-entity recognition, and near synonym recognition were employed, and
features like counts of common words, statement lengths, negation words, and
antonyms were considered to judge the entailment relationships of two
statements, while we explored both heuristics-based functions and
machine-learning approaches. The reported systems showed robustness by
simultaneously achieving second positions in the binary-classification subtasks
for both simplified and traditional Chinese in NTCIR-10 RITE-2. We conducted
more experiments with the test data of NTCIR-9 RITE, with good results. We also
extended our work to search for better configurations of our classifiers and
investigated contributions of individual features. This extended work showed
interesting results and should encourage further discussion.
Mining and discovering biographical information in Difangzhi with a language-model-based approach
Authors: Peter K. Bol, Chao-Lin Liu, Hongsu Wang
Date: 2015-04-08 00:00:00
Tasks: Language Modeling, Language Modelling

We present results of expanding the contents of the China Biographical
Database by text mining historical local gazetteers, difangzhi. The goal of the
database is to see how people are connected together, through kinship, social
connections, and the places and offices in which they served. The gazetteers
are the single most important collection of names and offices covering the Song
through Qing periods. Although we begin with local officials we shall
eventually include lists of local examination candidates, people from the
locality who served in government, and notable local figures with biographies.
The more data we collect the more connections emerge. The value of doing
systematic text mining work is that we can identify relevant connections that
are either directly informative or can become useful without deep historical
research. Academia Sinica is developing a name database for officials in the
central governments of the Ming and Qing dynasties.
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Authors: Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre
Date: 2015-04-08 00:00:00
Tasks: Distributed Computing

Recent approaches to distributed model fitting rely heavily on consensus
ADMM, where each node solves small sub-problems using only local data. We
propose iterative methods that solve {\em global} sub-problems over an entire
distributed dataset. This is possible using transpose reduction strategies that
allow a single node to solve least-squares over massive datasets without
putting all the data in one place. This results in simple iterative methods
that avoid the expensive inner loops required for consensus methods. To
demonstrate the efficiency of this approach, we fit linear classifiers and
sparse linear models to datasets over 5 Tb in size using a distributed
implementation with over 7000 cores in far less time than previous approaches.
Residential Demand Response Applications Using Batch Reinforcement Learning
Authors: Frederik Ruelens, Bert Claessens, Stijn Vandael, Bart De Schutter, Robert Babuska, Ronnie Belmans
Date: 2015-04-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Driven by recent advances in batch Reinforcement Learning (RL), this paper
contributes to the application of batch RL to demand response. In contrast to
conventional model-based approaches, batch RL techniques do not require a
system identification step, which makes them more suitable for a large-scale
implementation. This paper extends fitted Q-iteration, a standard batch RL
technique, to the situation where a forecast of the exogenous data is provided.
In general, batch RL techniques do not rely on expert knowledge on the system
dynamics or the solution. However, if some expert knowledge is provided, it can
be incorporated by using our novel policy adjustment method. Finally, we tackle
the challenge of finding an open-loop schedule required to participate in the
day-ahead market. We propose a model-free Monte-Carlo estimator method that
uses a metric to construct artificial trajectories and we illustrate this
method by finding the day-ahead schedule of a heat-pump thermostat. Our
experiments show that batch RL techniques provide a valuable alternative to
model-based controllers and that they can be used to construct both closed-loop
and open-loop policies.
Rehabilitation of Count-based Models for Word Vector Representations
Authors: Rémi Lebret, Ronan Collobert
Date: 2014-12-16 00:00:00
Tasks: Dimensionality Reduction, Word Embeddings, Word Similarity

Recent works on word representations mostly rely on predictive models.
Distributed word representations (aka word embeddings) are trained to optimally
predict the contexts in which the corresponding words tend to appear. Such
models have succeeded in capturing word similarties as well as semantic and
syntactic regularities. Instead, we aim at reviving interest in a model based
on counts. We present a systematic study of the use of the Hellinger distance
to extract semantic representations from the word co-occurence statistics of
large text corpora. We show that this distance gives good performance on word
similarity and analogy tasks, with a proper type and size of context, and a
dimensionality reduction based on a stochastic low-rank approximation. Besides
being both simple and intuitive, this method also provides an encoding function
which can be used to infer unseen words or phrases. This becomes a clear
advantage compared to predictive models which must train these new words.
Supporting Language Learners with the Meanings Of Closed Class Items
Authors: Hayat Alrefaie, Allan Ramsay
Date: 2015-04-08 00:00:00
Tasks: Diagnostic, Translation

The process of language learning involves the mastery of countless tasks:
making the constituent sounds of the language being learned, learning the
grammatical patterns, and acquiring the requisite vocabulary for reception and
production. While a plethora of computational tools exist to facilitate the
first and second of these tasks, a number of challenges arise with respect to
enabling the third. This paper describes a tool that has been designed to
support language learners with the challenge of understanding the use of
closed-class lexical items. The process of learning the Arabic for office is
(mktb) is relatively simple and should be possible by means of simple
repetition of the word. However, it is much more difficult to learn and
correctly use the Arabic equivalent of the word on. The current paper describes
a mechanism for the delivery of diagnostic information regarding specific
lexical examples, with the aim of clearly demonstrating why a particular
translation of a given closed-class item may be appropriate in certain
situations but not others, thereby helping learners to understand and use the
term correctly.