pwc-archive/papers-with-abstracts huggingface dataset:
Decision Making Based on Cohort Scores for Speaker Verification
Authors: Lantian Li, Renyu Wang, Gang Wang, Caixia Wang, Thomas Fang Zheng
Date: 2016-09-27 00:00:00
Tasks: Decision Making, Speaker Verification

Decision making is an important component in a speaker verification system.
For the conventional GMM-UBM architecture, the decision is usually conducted
based on the log likelihood ratio of the test utterance against the GMM of the
claimed speaker and the UBM. This single-score decision is simple but tends to
be sensitive to the complex variations in speech signals (e.g. text content,
channel, speaking style, etc.). In this paper, we propose a decision making
approach based on multiple scores derived from a set of cohort GMMs (cohort
scores). Importantly, these cohort scores are not simply averaged as in
conventional cohort methods; instead, we employ a powerful discriminative model
as the decision maker. Experimental results show that the proposed method
delivers substantial performance improvement over the baseline system,
especially when a deep neural network (DNN) is used as the decision maker, and
the DNN input involves some statistical features derived from the cohort
scores.
Reactive Collision Avoidance using Evolutionary Neural Networks
Authors: Hesham Eraqi, Youssef EmadEldin, Mohamed Moustafa
Date: 2016-09-27 00:00:00
Tasks: Collision Avoidance

Collision avoidance systems can play a vital role in reducing the number of
accidents and saving human lives. In this paper, we introduce and validate a
novel method for vehicles reactive collision avoidance using evolutionary
neural networks (ENN). A single front-facing rangefinder sensor is the only
input required by our method. The training process and the proposed method
analysis and validation are carried out using simulation. Extensive experiments
are conducted to analyse the proposed method and evaluate its performance.
Firstly, we experiment the ability to learn collision avoidance in a static
free track. Secondly, we analyse the effect of the rangefinder sensor
resolution on the learning process. Thirdly, we experiment the ability of a
vehicle to individually and simultaneously learn collision avoidance. Finally,
we test the generality of the proposed method. We used a more realistic and
powerful simulation environment (CarMaker), a camera as an alternative input
sensor, and lane keeping as an extra feature to learn. The results are
encouraging; the proposed method successfully allows vehicles to learn
collision avoidance in different scenarios that are unseen during training. It
also generalizes well if any of the input sensor, the simulator, or the task to
be learned is changed.
OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline
Authors: Dong Wang, Zhiyuan Tang, Difei Tang, Qing Chen
Date: 2016-09-27 00:00:00
Tasks: speech-recognition, Speech Recognition

We present the OC16-CE80 Chinese-English mixlingual speech database which was
released as a main resource for training, development and test for the
Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on
O-COCOSDA 2016. This database consists of 80 hours of speech signals recorded
from more than 1,400 speakers, where the utterances are in Chinese but each
involves one or several English words. Based on the database and another two
free data resources (THCHS30 and the CMU dictionary), a speech recognition
(ASR) baseline was constructed with the deep neural network-hidden Markov model
(DNN-HMM) hybrid system. We then report the baseline results following the
MixASR-CHEN evaluation rules and demonstrate that OC16-CE80 is a reasonable
data resource for mixlingual research.
Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks
Authors: Savelie Cornegruta, Robert Bakewell, Samuel Withey, Giovanni Montana
Date: 2016-09-27 00:00:00
Proceeding: WS 2016 11
Tasks: Medical Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Negation, Negation Detection, NER, Word Embeddings

Motivated by the need to automate medical information extraction from
free-text radiological reports, we present a bi-directional long short-term
memory (BiLSTM) neural network architecture for modelling radiological
language. The model has been used to address two NLP tasks: medical
named-entity recognition (NER) and negation detection. We investigate whether
learning several types of word embeddings improves BiLSTM's performance on
those tasks. Using a large dataset of chest x-ray reports, we compare the
proposed model to a baseline dictionary-based NER system and a negation
detection system that leverages the hand-crafted rules of the NegEx algorithm
and the grammatical relations obtained from the Stanford Dependency Parser.
Compared to these more traditional rule-based systems, we argue that BiLSTM
offers a strong alternative for both our tasks.
House price estimation from visual and textual features
Authors: Eman Ahmed, Mohamed Moustafa
Date: 2016-09-27 00:00:00

Most existing automatic house price estimation systems rely only on some
textual data like its neighborhood area and the number of rooms. The final
price is estimated by a human agent who visits the house and assesses it
visually. In this paper, we propose extracting visual features from house
photographs and combining them with the house's textual information. The
combined features are fed to a fully connected multilayer Neural Network (NN)
that estimates the house price as its single output. To train and evaluate our
network, we have collected the first houses dataset (to our knowledge) that
combines both images and textual attributes. The dataset is composed of 535
sample houses from the state of California, USA. Our experiments showed that
adding the visual features increased the R-value by a factor of 3 and decreased
the Mean Square Error (MSE) by one order of magnitude compared with
textual-only features. Additionally, when trained on the benchmark textual-only
features housing dataset, our proposed NN still outperformed the existing model
published results.
Generalization Error Bounds for Optimization Algorithms via Stability
Authors: Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma, Tie-Yan Liu
Date: 2016-09-27 00:00:00
Tasks: BIG-bench Machine Learning

Many machine learning tasks can be formulated as Regularized Empirical Risk
Minimization (R-ERM), and solved by optimization algorithms such as gradient
descent (GD), stochastic gradient descent (SGD), and stochastic variance
reduction (SVRG). Conventional analysis on these optimization algorithms
focuses on their convergence rates during the training process, however, people
in the machine learning community may care more about the generalization
performance of the learned model on unseen test data. In this paper, we
investigate on this issue, by using stability as a tool. In particular, we
decompose the generalization error for R-ERM, and derive its upper bound for
both convex and non-convex cases. In convex cases, we prove that the
generalization error can be bounded by the convergence rate of the optimization
algorithm and the stability of the R-ERM process, both in expectation (in the
order of $\mathcal{O}((1/n)+\mathbb{E}\rho(T))$, where $\rho(T)$ is the
convergence error and $T$ is the number of iterations) and in high probability
(in the order of
$\mathcal{O}\left(\frac{\log{1/\delta}}{\sqrt{n}}+\rho(T)\right)$ with
probability $1-\delta$). For non-convex cases, we can also obtain a similar
expected generalization error bound. Our theorems indicate that 1) along with
the training process, the generalization error will decrease for all the
optimization algorithms under our investigation; 2) Comparatively speaking,
SVRG has better generalization ability than GD and SGD. We have conducted
experiments on both convex and non-convex problems, and the experimental
results verify our theoretical findings.
Semi Automatic Color Segmentation of Document Pages
Authors: Stéphane Bres, Véronique Eglin, Vincent Poulain
Date: 2016-09-27 00:00:00

-This paper presents a semi automatic method used to segment color documents
into different uniform color plans. The practical application is dedicated to
administrative documents segmentation. In these documents, like in many other
cases, color has a semantic meaning: it is then possible to identify some
specific regions like manual annotations, rubber stamps or colored
highlighting. A first step of user-controlled learning of the desired color
plans is made on few sample documents. An automatic process can then be
performed on the much bigger set as a batch. Our experiments show very
interesting results in with a very competitive processing time.
Multiple protein feature prediction with statistical relational learning
Authors: Luca Masera
Date: 2016-09-27 00:00:00
Tasks: Prediction, Relational Reasoning

High throughput sequencing techniques have highly impactedon modern biology,
widening the gap between sequenced andannotated data. Automatic annotation
tools are thereforeof the foremost importance to guide biologists' experiments.
However, most of the state-of-the-art methods rely on annotation transfer,
offering reliable predictions only in homology settings. In this work we
present a novel appraoch to protein feature prediction, which exploits the
Semanti Based Regularization to inject prior knowledge in the learning process.
The experimental results conducted on the yeast genome show that the
introduction of the constraints positively impacts on the overall prediction
quality.
A Hackathon for Classical Tibetan
Authors: Orna Almogi, Lena Dankin, Nachum Dershowitz, Lior Wolf
Date: 2016-09-27 00:00:00
Tasks: General Classification, text-classification, Text Classification

We describe the course of a hackathon dedicated to the development of
linguistic tools for Tibetan Buddhist studies. Over a period of five days, a
group of seventeen scholars, scientists, and students developed and compared
algorithms for intertextual alignment and text classification, along with some
basic language tools, including a stemmer and word segmenter.
Tensor Based Second Order Variational Model for Image Reconstruction
Authors: Jinming Duan, Wil OC Ward, Luke Sibbett, Zhenkuan Pan, Li Bai
Date: 2016-09-27 00:00:00
Tasks: Denoising, Image Inpainting, Image Reconstruction

Second order total variation (SOTV) models have advantages for image
reconstruction over their first order counterparts including their ability to
remove the staircase artefact in the reconstructed image, but they tend to blur
the reconstructed image. To overcome this drawback, we introduce a new Tensor
Weighted Second Order (TWSO) model for image reconstruction. Specifically, we
develop a novel regulariser for the SOTV model that uses the Frobenius norm of
the product of the SOTV Hessian matrix and the anisotropic tensor. We then
adapt the alternating direction method of multipliers (ADMM) to solve the
proposed model by breaking down the original problem into several subproblems.
All the subproblems have closed-forms and can thus be solved efficiently. The
proposed method is compared with a range of state-of-the-art approaches such as
tensor-based anisotropic diffusion, total generalised variation, Euler's
elastica, etc. Numerical experimental results of the method on both synthetic
and real images from the Berkeley database BSDS500 demonstrate that the
proposed method eliminates both the staircase and blurring effects and
outperforms the existing approaches for image inpainting and denoising
applications.
A Kernel Test of Goodness of Fit
Authors: Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
Date: 2016-02-09 00:00:00
Tasks: Density Estimation

We propose a nonparametric statistical test for goodness-of-fit: given a set
of samples, the test determines how likely it is that these were generated from
a target density function. The measure of goodness-of-fit is a divergence
constructed via Stein's method using functions from a Reproducing Kernel
Hilbert Space. Our test statistic is based on an empirical estimate of this
divergence, taking the form of a V-statistic in terms of the log gradients of
the target density and the kernel. We derive a statistical test, both for
i.i.d. and non-i.i.d. samples, where we estimate the null distribution
quantiles using a wild bootstrap procedure. We apply our test to quantifying
convergence of approximate Markov Chain Monte Carlo methods, statistical model
criticism, and evaluating quality of fit vs model complexity in nonparametric
density estimation.
Multi-task Recurrent Model for Speech and Speaker Recognition
Authors: Zhiyuan Tang, Lantian Li, Dong Wang
Date: 2016-03-31 00:00:00
Tasks: Speaker Recognition

Although highly correlated, speech and speaker recognition have been regarded
as two independent tasks and studied by two communities. This is certainly not
the way that people behave: we decipher both speech content and speaker traits
at the same time. This paper presents a unified model to perform speech and
speaker recognition simultaneously and altogether. The model is based on a
unified neural network where the output of one task is fed to the input of the
other, leading to a multi-task recurrent network. Experiments show that the
joint model outperforms the task-specific models on both the two tasks.
Automated Breast Lesion Segmentation in Ultrasound Images
Authors: Ibrahim Sadek, Mohamed Elawady, Viktor Stefanovski
Date: 2016-09-27 00:00:00
Tasks: General Classification, Lesion Segmentation

The main objective of this project is to segment different breast ultrasound
images to find out lesion area by discarding the low contrast regions as well
as the inherent speckle noise. The proposed method consists of three stages
(removing noise, segmentation, classification) in order to extract the correct
lesion. We used normalized cuts approach to segment ultrasound images into
regions of interest where we can possibly finds the lesion, and then K-means
classifier is applied to decide finally the location of the lesion. For every
original image, an annotated ground-truth image is given to perform comparison
with the obtained experimental results, providing accurate evaluation measures.
Fixed-Point Performance Analysis of Recurrent Neural Networks
Authors: Sungho Shin, Kyuyeon Hwang, Wonyong Sung
Date: 2015-12-04 00:00:00
Tasks: Language Modeling, Language Modelling, Phoneme Recognition, Quantization, Sensitivity

Recurrent neural networks have shown excellent performance in many
applications, however they require increased complexity in hardware or software
based implementations. The hardware complexity can be much lowered by
minimizing the word-length of weights and signals. This work analyzes the
fixed-point performance of recurrent neural networks using a retrain based
quantization method. The quantization sensitivity of each layer in RNNs is
studied, and the overall fixed-point optimization results minimizing the
capacity of weights while not sacrificing the performance are presented. A
language model and a phoneme recognition examples are used.
A Wild Bootstrap for Degenerate Kernel Tests
Authors: Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton
Date: 2014-08-23 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Benchmarking, Time Series, Time Series Analysis

A wild bootstrap method for nonparametric hypothesis tests based on kernel
distribution embeddings is proposed. This bootstrap method is used to construct
provably consistent tests that apply to random processes, for which the naive
permutation-based bootstrap fails. It applies to a large group of kernel tests
based on V-statistics, which are degenerate under the null hypothesis, and
non-degenerate elsewhere. To illustrate this approach, we construct a
two-sample test, an instantaneous independence test and a multiple lag
independence test for time series. In experiments, the wild bootstrap gives
strong performance on synthetic examples, on audio data, and in performance
benchmarking for the Gibbs sampler.
Multi-task Recurrent Model for True Multilingual Speech Recognition
Authors: Zhiyuan Tang, Lantian Li, Dong Wang
Date: 2016-09-27 00:00:00
Tasks: speech-recognition, Speech Recognition

Research on multilingual speech recognition remains attractive yet
challenging. Recent studies focus on learning shared structures under the
multi-task paradigm, in particular a feature sharing structure. This approach
has been found effective to improve performance on each individual language.
However, this approach is only useful when the deployed system supports just
one language. In a true multilingual scenario where multiple languages are
allowed, performance will be significantly reduced due to the competition among
languages in the decoding space. This paper presents a multi-task recurrent
model that involves a multilingual speech recognition (ASR) component and a
language recognition (LR) component, and the ASR component is informed of the
language information by the LR component, leading to a language-aware
recognition. We tested the approach on an English-Chinese bilingual recognition
task. The results show that the proposed multi-task recurrent model can improve
performance of multilingual recognition systems.
Analysis of Massive Heterogeneous Temporal-Spatial Data with 3D Self-Organizing Map and Time Vector
Authors: Yu Ding
Date: 2016-09-27 00:00:00
Tasks: Clustering

Self-organizing map(SOM) have been widely applied in clustering, this paper
focused on centroids of clusters and what they reveal. When the input vectors
consists of time, latitude and longitude, the map can be strongly linked to
physical world, providing valuable information. Beyond basic clustering, a
novel approach to address the temporal element is developed, enabling 3D SOM to
track behaviors in multiple periods concurrently. Combined with adaptations
targeting to process heterogeneous data relating to distribution in time and
space, the paper offers a fresh scope for business and services based on
temporal-spatial pattern.
The Effects of Data Size and Frequency Range on Distributional Semantic Models
Authors: Magnus Sahlgren, Alessandro Lenci
Date: 2016-09-27 00:00:00
Proceeding: EMNLP 2016 11

This paper investigates the effects of data size and frequency range on
distributional semantic models. We compare the performance of a number of
representative models for several test settings over data of varying sizes, and
over test items of various frequency. Our results show that neural
network-based models underperform when the data is small, and that the most
reliable model over data of varying sizes and frequency ranges is the inverted
factorized model.
Image Retrieval with Fisher Vectors of Binary Features
Authors: Yusuke Uchida, Shigeyuki Sakazawa, Shin'ichi Satoh
Date: 2016-09-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

Recently, the Fisher vector representation of local features has attracted
much attention because of its effectiveness in both image classification and
image retrieval. Another trend in the area of image retrieval is the use of
binary features such as ORB, FREAK, and BRISK. Considering the significant
performance improvement for accuracy in both image classification and retrieval
by the Fisher vector of continuous feature descriptors, if the Fisher vector
were also to be applied to binary features, we would receive similar benefits
in binary feature based image retrieval and classification. In this paper, we
derive the closed-form approximation of the Fisher vector of binary features
modeled by the Bernoulli mixture model. We also propose accelerating the Fisher
vector by using the approximate value of posterior probability. Experiments
show that the Fisher vector representation significantly improves the accuracy
of image retrieval compared with a bag of binary words approach.
Online Unsupervised Multi-view Feature Selection
Authors: Weixiang Shao, Lifang He, Chun-Ta Lu, Xiaokai Wei, Philip S. Yu
Date: 2016-09-27 00:00:00
Tasks: Clustering, feature selection, Sparse Learning

In the era of big data, it is becoming common to have data with multiple
modalities or coming from multiple sources, known as "multi-view data".
Multi-view data are usually unlabeled and come from high-dimensional spaces
(such as language vocabularies), unsupervised multi-view feature selection is
crucial to many applications. However, it is nontrivial due to the following
challenges. First, there are too many instances or the feature dimensionality
is too large. Thus, the data may not fit in memory. How to select useful
features with limited memory space? Second, how to select features from
streaming data and handles the concept drift? Third, how to leverage the
consistent and complementary information from different views to improve the
feature selection in the situation when the data are too big or come in as
streams? To the best of our knowledge, none of the previous works can solve all
the challenges simultaneously. In this paper, we propose an Online unsupervised
Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming
multi-view data in an online fashion. OMVFS embeds unsupervised feature
selection into a clustering algorithm via NMF with sparse learning. It further
incorporates the graph regularization to preserve the local structure
information and help select discriminative features. Instead of storing all the
historical data, OMVFS processes the multi-view data chunk by chunk and
aggregates all the necessary information into several small matrices. By using
the buffering technique, the proposed OMVFS can reduce the computational and
storage cost while taking advantage of the structure information. Furthermore,
OMVFS can capture the concept drifts in the data streams. Extensive experiments
on four real-world datasets show the effectiveness and efficiency of the
proposed OMVFS method. More importantly, OMVFS is about 100 times faster than
the off-line methods.
Top-N Recommendation on Graphs
Authors: Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng
Date: 2016-09-27 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender systems play an increasingly important role in online
applications to help users find what they need or prefer. Collaborative
filtering algorithms that generate predictions by analyzing the user-item
rating matrix perform poorly when the matrix is sparse. To alleviate this
problem, this paper proposes a simple recommendation algorithm that fully
exploits the similarity information among users and items and intrinsic
structural information of the user-item matrix. The proposed method constructs
a new representation which preserves affinity and structure information in the
user-item rating matrix and then performs recommendation task. To capture
proximity information about users and items, two graphs are constructed.
Manifold learning idea is used to constrain the new representation to be smooth
on these graphs, so as to enforce users and item proximities. Our model is
formulated as a convex optimization problem, for which we need to solve the
well-known Sylvester equation only. We carry out extensive empirical
evaluations on six benchmark datasets to show the effectiveness of this
approach.