pwc-archive/papers-with-abstracts huggingface dataset:
A Linear Dynamical System Model for Text
Authors: David Belanger, Sham Kakade
Date: 2015-02-13 00:00:00
Tasks: Language Modeling, Language Modelling, model, Word Embeddings

Low dimensional representations of words allow accurate NLP models to be
trained on limited annotated data. While most representations ignore words'
local context, a natural way to induce context-dependent representations is to
perform inference in a probabilistic latent-variable sequence model. Given the
recent success of continuous vector space word representations, we provide such
an inference procedure for continuous states, where words' representations are
given by the posterior mean of a linear dynamical system. Here, efficient
inference can be performed using Kalman filtering. Our learning algorithm is
extremely scalable, operating on simple cooccurrence counts for both parameter
initialization using the method of moments and subsequent iterations of EM. In
our experiments, we employ our inferred word embeddings as features in standard
tagging tasks, obtaining significant accuracy improvements. Finally, the Kalman
filter updates can be seen as a linear recurrent neural network. We demonstrate
that using the parameters of our model to initialize a non-linear recurrent
neural network language model reduces its training time by a day and yields
lower perplexity.
Visual Madlibs: Fill in the blank Image Generation and Question Answering
Authors: Licheng Yu, Eunbyung Park, Alexander C. Berg, Tamara L. Berg
Date: 2015-05-31 00:00:00
Tasks: Image Generation, Multiple-choice, Question Answering

In this paper, we introduce a new dataset consisting of 360,001 focused
natural language descriptions for 10,738 images. This dataset, the Visual
Madlibs dataset, is collected using automatically produced fill-in-the-blank
templates designed to gather targeted descriptions about: people and objects,
their appearances, activities, and interactions, as well as inferences about
the general scene or its broader context. We provide several analyses of the
Visual Madlibs dataset and demonstrate its applicability to two new description
generation tasks: focused description generation, and multiple-choice
question-answering for images. Experiments using joint-embedding and deep
learning methods show promising results on these tasks.
Spectral Convergence of the connection Laplacian from random samples
Authors: Amit Singer, Hau-Tieng Wu
Date: 2013-06-07 00:00:00
Tasks: Dimensionality Reduction

Spectral methods that are based on eigenvectors and eigenvalues of discrete
graph Laplacians, such as Diffusion Maps and Laplacian Eigenmaps are often used
for manifold learning and non-linear dimensionality reduction. It was
previously shown by Belkin and Niyogi \cite{belkin_niyogi:2007} that the
eigenvectors and eigenvalues of the graph Laplacian converge to the
eigenfunctions and eigenvalues of the Laplace-Beltrami operator of the manifold
in the limit of infinitely many data points sampled independently from the
uniform distribution over the manifold. Recently, we introduced Vector
Diffusion Maps and showed that the connection Laplacian of the tangent bundle
of the manifold can be approximated from random samples. In this paper, we
present a unified framework for approximating other connection Laplacians over
the manifold by considering its principle bundle structure. We prove that the
eigenvectors and eigenvalues of these Laplacians converge in the limit of
infinitely many independent random samples. We generalize the spectral
convergence results to the case where the data points are sampled from a
non-uniform distribution, and for manifolds with and without boundary.
Parallel Spectral Clustering Algorithm Based on Hadoop
Authors: Yajun Cui, Yang Zhao, Kafei Xiao, Chenglong Zhang, Lei Wang
Date: 2015-05-31 00:00:00
Tasks: Cloud Computing, Clustering

Spectral clustering and cloud computing is emerging branch of computer
science or related discipline. It overcome the shortcomings of some traditional
clustering algorithm and guarantee the convergence to the optimal solution,
thus have to the widespread attention. This article first introduced the
parallel spectral clustering algorithm research background and significance,
and then to Hadoop the cloud computing Framework has carried on the detailed
introduction, then has carried on the related to spectral clustering is
introduced, then introduces the spectral clustering arithmetic Method of
parallel and relevant steps, finally made the related experiments, and the
experiment are summarized.
Recurrent Neural Networks with External Memory for Language Understanding
Authors: Baolin Peng, Kaisheng Yao
Date: 2015-05-31 00:00:00
Tasks: Memorization

Recurrent Neural Networks (RNNs) have become increasingly popular for the
task of language understanding. In this task, a semantic tagger is deployed to
associate a semantic label to each word in an input sequence. The success of
RNN may be attributed to its ability to memorize long-term dependence that
relates the current-time semantic label prediction to the observations many
time instances away. However, the memory capacity of simple RNNs is limited
because of the gradient vanishing and exploding problem. We propose to use an
external memory to improve memorization capability of RNNs. We conducted
experiments on the ATIS dataset, and observed that the proposed model was able
to achieve the state-of-the-art results. We compare our proposed model with
alternative models and report analysis results that may provide insights for
future research.
An Open Source Testing Tool for Evaluating Handwriting Input Methods
Authors: Liquan Qiu, Lianwen Jin, Ruifen Dai, Yuxiang Zhang, Lei LI
Date: 2015-05-30 00:00:00
Tasks: Handwriting Recognition

This paper presents an open source tool for testing the recognition accuracy
of Chinese handwriting input methods. The tool consists of two modules, namely
the PC and Android mobile client. The PC client reads handwritten samples in
the computer, and transfers them individually to the Android client in
accordance with the socket communication protocol. After the Android client
receives the data, it simulates the handwriting on screen of client device, and
triggers the corresponding handwriting recognition method. The recognition
accuracy is recorded by the Android client. We present the design principles
and describe the implementation of the test platform. We construct several test
datasets for evaluating different handwriting recognition systems, and conduct
an objective and comprehensive test using six Chinese handwriting input methods
with five datasets. The test results for the recognition accuracy are then
compared and analyzed.
Proximal Algorithms in Statistics and Machine Learning
Authors: Nicholas G. Polson, James G. Scott, Brandon T. Willard
Date: 2015-02-11 00:00:00
Tasks: BIG-bench Machine Learning, regression

In this paper we develop proximal methods for statistical learning. Proximal
point algorithms are useful in statistics and machine learning for obtaining
optimization solutions for composite functions. Our approach exploits
closed-form solutions of proximal operators and envelope representations based
on the Moreau, Forward-Backward, Douglas-Rachford and Half-Quadratic envelopes.
Envelope representations lead to novel proximal algorithms for statistical
optimisation of composite objective functions which include both non-smooth and
non-convex objectives. We illustrate our methodology with regularized Logistic
and Poisson regression and non-convex bridge penalties with a fused lasso norm.
We provide a discussion of convergence of non-descent algorithms with
acceleration and for non-convex functions. Finally, we provide directions for
future research.
Using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels
Authors: Sagi Eppel
Date: 2015-05-30 00:00:00

The recognition of materials and objects inside transparent containers using
computer vision has a wide range of applications, ranging from industrial
bottles filling to the automation of chemistry laboratory. One of the main
challenges in such recognition is the ability to distinguish between image
features resulting from the vessels surface and image features resulting from
the material inside the vessel. Reflections and the functional parts of a
vessels surface can create strong edges that can be mistakenly identified as
corresponding to the vessel contents, and cause recognition errors. The ability
to evaluate whether a specific edge in an image stems from the vessels surface
or from its contents can considerably improve the ability to identify materials
inside transparent vessels. This work will suggest a method for such
evaluation, based on the following two assumptions: 1) Areas of high curvature
on the vessel surface are likely to cause strong edges due to changes in
reflectivity, as is the appearance of functional parts (e.g. corks or valves).
2) Most transparent vessels (bottles, glasses) have high symmetry
(cylindrical). As a result the curvature angle of the vessels surface at each
point of the image is similar to the curvature angle of the contour line of the
vessel in the same row in the image. These assumptions, allow the
identification of image regions with strong edges corresponding to the vessel
surface reflections. Combining this method with existing image analysis methods
for detecting materials inside transparent containers allows considerable
improvement in accuracy.
Addressing the Rare Word Problem in Neural Machine Translation
Authors: Minh-Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba
Date: 2014-10-30 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Machine Translation, NMT, Sentence, Translation, Word Alignment

Neural Machine Translation (NMT) is a new approach to machine translation
that has shown promising results that are comparable to traditional approaches.
A significant weakness in conventional NMT systems is their inability to
correctly translate very rare words: end-to-end NMTs tend to have relatively
small vocabularies with a single unk symbol that represents every possible
out-of-vocabulary (OOV) word. In this paper, we propose and implement an
effective technique to address this problem. We train an NMT system on data
that is augmented by the output of a word alignment algorithm, allowing the NMT
system to emit, for each OOV word in the target sentence, the position of its
corresponding word in the source sentence. This information is later utilized
in a post-processing step that translates every OOV word using a dictionary.
Our experiments on the WMT14 English to French translation task show that this
method provides a substantial improvement of up to 2.8 BLEU points over an
equivalent NMT system that does not use this technique. With 37.5 BLEU points,
our NMT system is the first to surpass the best result achieved on a WMT14
contest task.
Deep Roto-Translation Scattering for Object Classification
Authors: Edouard Oyallon, Stéphane Mallat
Date: 2014-12-30 00:00:00
Proceeding: CVPR 2015 6
Tasks: Classification, Dictionary Learning, General Classification, image-classification, Image Classification, Object, Translation

Dictionary learning algorithms or supervised deep convolution networks have
considerably improved the efficiency of predefined feature representations such
as SIFT. We introduce a deep scattering convolution network, with predefined
wavelet filters over spatial and angular variables. This representation brings
an important improvement to results previously obtained with predefined
features over object image databases such as Caltech and CIFAR. The resulting
accuracy is comparable to results obtained with unsupervised deep learning and
dictionary based representations. This shows that refining image
representations by using geometric priors is a promising direction to improve
image classification and its understanding.
Robust Anomaly Detection Using Semidefinite Programming
Authors: Jose A. Lopez, Octavia Camps, Mario Sznaier
Date: 2015-04-03 00:00:00
Tasks: Anomaly Detection

This paper presents a new approach, based on polynomial optimization and the
method of moments, to the problem of anomaly detection. The proposed technique
only requires information about the statistical moments of the normal-state
distribution of the features of interest and compares favorably with existing
approaches (such as Parzen windows and 1-class SVM). In addition, it provides a
succinct description of the normal state. Thus, it leads to a substantial
simplification of the the anomaly detection problem when working with higher
dimensional datasets.
Efficient combination of pairswise feature networks
Authors: Pau Bellot, Patrick E. Meyer
Date: 2015-05-30 00:00:00
Tasks: Time Series, Time Series Analysis

This paper presents a novel method for the reconstruction of a neural network
connectivity using calcium fluorescence data. We introduce a fast unsupervised
method to integrate different networks that reconstructs structural
connectivity from neuron activity. Our method improves the state-of-the-art
reconstruction method General Transfer Entropy (GTE). We are able to better
eliminate indirect links, improving therefore the quality of the network via a
normalization and ensemble process of GTE and three new informative features.
The approach is based on a simple combination of networks, which is remarkably
fast. The performance of our approach is benchmarked on simulated time series
provided at the connectomics challenge and also submitted at the public
competition.
A Novel Energy Aware Node Clustering Algorithm for Wireless Sensor Networks Using a Modified Artificial Fish Swarm Algorithm
Authors: Reza Azizi, Hasan Sedghi, Hamid Shoja, Alireza Sepas-Moghaddam
Date: 2015-05-30 00:00:00
Tasks: Clustering, Evolutionary Algorithms, Node Clustering

Clustering problems are considered amongst the prominent challenges in
statistics and computational science. Clustering of nodes in wireless sensor
networks which is used to prolong the life-time of networks is one of the
difficult tasks of clustering procedure. In order to perform nodes clustering,
a number of nodes are determined as cluster heads and other ones are joined to
one of these heads, based on different criteria e.g. Euclidean distance. So
far, different approaches have been proposed for this process, where swarm and
evolutionary algorithms contribute in this regard. In this study, a novel
algorithm is proposed based on Artificial Fish Swarm Algorithm (AFSA) for
clustering procedure. In the proposed method, the performance of the standard
AFSA is improved by increasing balance between local and global searches.
Furthermore, a new mechanism has been added to the base algorithm for improving
convergence speed in clustering problems. Performance of the proposed technique
is compared to a number of state-of-the-art techniques in this field and the
outcomes indicate the supremacy of the proposed technique.
A Review of Feature and Data Fusion with Medical Images
Authors: Alex Pappachen James, Belur Dasarathy
Date: 2015-05-30 00:00:00
Tasks: Form

The fusion techniques that utilize multiple feature sets to form new features
that are often more robust and contain useful information for future processing
are referred to as feature fusion. The term data fusion is applied to the class
of techniques used for combining decisions obtained from multiple feature sets
to form global decisions. Feature and data fusion interchangeably represent two
important classes of techniques that have proved to be of practical importance
in a wide range of medical imaging problems
Sistem penunjang keputusan kelayakan pemberian pinjaman dengna metode fuzzy tsukamoto
Authors: Tri Murti, Leon Andretti Abdillah, Muhammad Sobri
Date: 2015-05-30 00:00:00
Tasks: Decision Making

Decision support systems (DSS) can be used to help settlement issues or
decisions that are semi-structured or structured. The method used is Fuzzy
Tsukamoto. PT Triprima Finance is a company engaged in the service sector
lending with collateral in the form of Motor Vehicle Owner Book or car (reg).
PT. Triprima Finance should consider borrowing from its customers with the
consent of the head manager. Such approval requires a long time because they
have to pass through many stages of the reporting procedure. Decision-making
activities at PT Triprima Finance carried out by the analysis process manually.
To help overcome these problems, the need for completion method in accuracy and
speed of decision making feasibility of lending. To overcome this need to
develop a new system that is a decision support system Tsukamoto fuzzy method.
is expected to facilitate kaposko to determine the decisions to be taken.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Authors: Kai Sheng Tai, Richard Socher, Christopher D. Manning
Date: 2015-02-28 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: General Classification, Semantic Similarity, Sentiment Analysis, Sentiment Classification

Because of their superior ability to preserve sequence information over time,
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with
a more complex computational unit, have obtained strong results on a variety of
sequence modeling tasks. The only underlying LSTM structure that has been
explored so far is a linear chain. However, natural language exhibits syntactic
properties that would naturally combine words to phrases. We introduce the
Tree-LSTM, a generalization of LSTMs to tree-structured network topologies.
Tree-LSTMs outperform all existing systems and strong LSTM baselines on two
tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task
1) and sentiment classification (Stanford Sentiment Treebank).
Recognition of convolutional neural network based on CUDA Technology
Authors: Yi-bin Huang, Kang Li, Ge Wang, Min Cao, Pin Li, Yu-jia Zhang
Date: 2015-05-30 00:00:00
Tasks: CPU, GPU

For the problem whether Graphic Processing Unit(GPU),the stream processor
with high performance of floating-point computing is applicable to neural
networks, this paper proposes the parallel recognition algorithm of
Convolutional Neural Networks(CNNs).It adopts Compute Unified Device
Architecture(CUDA)technology, definite the parallel data structures, and
describes the mapping mechanism for computing tasks on CUDA. It compares the
parallel recognition algorithm achieved on GPU of GTX200 hardware architecture
with the serial algorithm on CPU. It improves speed by nearly 60 times. Result
shows that GPU based the stream processor architecture ate more applicable to
some related applications about neural networks than CPU.
A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)
Authors: Xiaohao Cai, Raymond Chan, Mila Nikolova, Tieyong Zeng
Date: 2015-05-30 00:00:00
Tasks: CPU, Segmentation

In this paper, we propose a SLaT (Smoothing, Lifting and Thresholding) method
with three stages for multiphase segmentation of color images corrupted by
different degradations: noise, information loss, and blur. At the first stage,
a convex variant of the Mumford-Shah model is applied to each channel to obtain
a smooth image. We show that the model has unique solution under the different
degradations. In order to properly handle the color information, the second
stage is dimension lifting where we consider a new vector-valued image composed
of the restored image and its transform in the secondary color space with
additional information. This ensures that even if the first color space has
highly correlated channels, we can still have enough information to give good
segmentation results. In the last stage, we apply multichannel thresholding to
the combined vector-valued image to find the segmentation. The number of phases
is only required in the last stage, so users can choose or change it all
without the need of solving the previous stages again. Experiments demonstrate
that our SLaT method gives excellent results in terms of segmentation quality
and CPU time in comparison with other state-of-the-art segmentation methods.
Bag of Genres for Video Retrieval
Authors: Leonardo A. Duarte, Otávio A. B. Penatti, Jurandy Almeida
Date: 2015-05-30 00:00:00
Tasks: Retrieval, Video Retrieval

Often, videos are composed of multiple concepts or even genres. For instance, news videos may contain sports, action, nature, etc. Therefore, encoding the distribution of such concepts/genres in a compact and effective representation is a challenging task. In this sense, we propose the Bag of Genres representation, which is based on a visual dictionary defined by a genre classifier. Each visual word corresponds to a region in the classification space. The Bag of Genres video vector contains a summary of the activations of each genre in the video content. We evaluate the proposed method for video genre retrieval using the dataset of MediaEval Tagging Task of 2012 and for video event retrieval using the EVVE dataset. Results show that the proposed method achieves results comparable or superior to state-of-the-art methods, with the advantage of providing a much more compact representation than existing features.
Using Syntactic Features for Phishing Detection
Authors: Gilchan Park, Julia M. Taylor
Date: 2015-05-29 00:00:00
Tasks: Object

This paper reports on the comparison of the subject and object of verbs in
their usage between phishing emails and legitimate emails. The purpose of this
research is to explore whether the syntactic structures and subjects and
objects of verbs can be distinguishable features for phishing detection. To
achieve the objective, we have conducted two series of experiments: the
syntactic similarity for sentences, and the subject and object of verb
comparison. The results of the experiments indicated that both features can be
used for some verbs, but more work has to be done for others.
Feature Representation for Online Signature Verification
Authors: Mohsen Fayyaz, Mohammad Hajizadeh_Saffar, Mohammad Sabokrou, Mahmood Fathy
Date: 2015-05-29 00:00:00
Tasks: Deep Learning, feature selection

Biometrics systems have been used in a wide range of applications and have
improved people authentication. Signature verification is one of the most
common biometric methods with techniques that employ various specifications of
a signature. Recently, deep learning has achieved great success in many fields,
such as image, sounds and text processing. In this paper, deep learning method
has been used for feature extraction and feature selection.
Learning to count with deep object features
Authors: Santi Seguí, Oriol Pujol, Jordi Vitrià
Date: 2015-05-29 00:00:00
Tasks: Object

Learning to count is a learning strategy that has been recently proposed in
the literature for dealing with problems where estimating the number of object
instances in a scene is the final objective. In this framework, the task of
learning to detect and localize individual object instances is seen as a harder
task that can be evaded by casting the problem as that of computing a
regression value from hand-crafted image features. In this paper we explore the
features that are learned when training a counting convolutional neural network
in order to understand their underlying representation. To this end we define a
counting problem for MNIST data and show that the internal representation of
the network is able to classify digits in spite of the fact that no direct
supervision was provided for them during training. We also present preliminary
results about a deep network that is able to count the number of pedestrians in
a scene.