pwc-archive/papers-with-abstracts huggingface dataset:
Online Active Linear Regression via Thresholding
Authors: Carlos Riquelme, Ramesh Johari, Baosen Zhang
Date: 2016-02-09 00:00:00
Tasks: Active Learning, regression

We consider the problem of online active learning to collect data for
regression modeling. Specifically, we consider a decision maker with a limited
experimentation budget who must efficiently learn an underlying linear
population model. Our main contribution is a novel threshold-based algorithm
for selection of most informative observations; we characterize its performance
and fundamental lower bounds. We extend the algorithm and its guarantees to
sparse linear regression in high-dimensional settings. Simulations suggest the
algorithm is remarkably robust: it provides significant benefits over passive
random sampling in real-world datasets that exhibit high nonlinearity and high
dimensionality --- significantly reducing both the mean and variance of the
squared error.
Stochastic Multidimensional Scaling
Authors: Ketan Rajawat, Sandeep Kumar
Date: 2016-12-21 00:00:00
Tasks: Dimensionality Reduction, Stochastic Optimization

Multidimensional scaling (MDS) is a popular dimensionality reduction
techniques that has been widely used for network visualization and cooperative
localization. However, the traditional stress minimization formulation of MDS
necessitates the use of batch optimization algorithms that are not scalable to
large-sized problems. This paper considers an alternative stochastic stress
minimization framework that is amenable to incremental and distributed
solutions. A novel linear-complexity stochastic optimization algorithm is
proposed that is provably convergent and simple to implement. The applicability
of the proposed algorithm to localization and visualization tasks is also
expounded. Extensive tests on synthetic and real datasets demonstrate the
efficacy of the proposed algorithm.
ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans
Authors: Anna Lukina, Lukas Esterle, Christian Hirsch, Ezio Bartocci, Junxing Yang, Ashish Tiwari, Scott A. Smolka, Radu Grosu
Date: 2016-12-21 00:00:00

We introduce ARES, an efficient approximation algorithm for generating
optimal plans (action sequences) that take an initial state of a Markov
Decision Process (MDP) to a state whose cost is below a specified (convergence)
threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both
the receding horizon and the particle swarm. Inspired by Importance Splitting,
the length of the horizon and the number of particles are chosen such that at
least one particle reaches a next-level state, that is, a state where the cost
decreases by a required delta from the previous-level state. The level relation
on states and the plans constructed by ARES implicitly define a Lyapunov
function and an optimal policy, respectively, both of which could be explicitly
generated by applying ARES to all states of the MDP, up to some topological
equivalence relation. We also assess the effectiveness of ARES by statistically
evaluating its rate of success in generating optimal plans. The ARES algorithm
resulted from our desire to clarify if flying in V-formation is a flocking
policy that optimizes energy conservation, clear view, and velocity alignment.
That is, we were interested to see if one could find optimal plans that bring a
flock from an arbitrary initial state to a state exhibiting a single connected
V-formation. For flocks with 7 birds, ARES is able to generate a plan that
leads to a V-formation in 95% of the 8,000 random initial configurations within
63 seconds, on average. ARES can also be easily customized into a
model-predictive controller (MPC) with an adaptive receding horizon and
statistical guarantees of convergence. To the best of our knowledge, our
adaptive-sizing approach is the first to provide convergence guarantees in
receding-horizon techniques.
A deep learning approach for predicting the quality of online health expert question-answering services
Authors: Ze Hu, Zhan Zhang, Qing Chen, Haiqin Yang, Decheng Zuo
Date: 2016-12-21 00:00:00
Tasks: Question Answering

Currently, a growing number of health consumers are asking health-related
questions online, at any time and from anywhere, which effectively lowers the
cost of health care. The most common approach is using online health expert
question-answering (HQA) services, as health consumers are more willing to
trust answers from professional physicians. However, these answers can be of
varying quality depending on circumstance. In addition, as the available HQA
services grow, how to predict the answer quality of HQA services via machine
learning becomes increasingly important and challenging. In an HQA service,
answers are normally short texts, which are severely affected by the data
sparsity problem. Furthermore, HQA services lack community features such as
best answer and user votes. Therefore, the wisdom of the crowd is not available
to rate answer quality. To address these problems, in this paper, the
prediction of HQA answer quality is defined as a classification task. First,
based on the characteristics of HQA services and feedback from medical experts,
a standard for HQA service answer quality evaluation is defined. Next, based on
the characteristics of HQA services, several novel non-textual features are
proposed, including surface linguistic features and social features. Finally, a
deep belief network (DBN)-based HQA answer quality prediction framework is
proposed to predict the quality of answers by learning the high-level hidden
semantic representation from the physicians' answers. Our results prove that
the proposed framework overcomes the problem of overly sparse textual features
in short text answers and effectively identifies high-quality answers.
Neural Networks Classifier for Data Selection in Statistical Machine Translation
Authors: Álvaro Peris, Mara Chinea-Rios, Francisco Casacuberta
Date: 2016-12-16 00:00:00
Tasks: General Classification, Machine Translation, Translation

We address the data selection problem in statistical machine translation
(SMT) as a classification task. The new data selection method is based on a
neural network classifier. We present a new method description and empirical
results proving that our data selection method provides better translation
quality, compared to a state-of-the-art method (i.e., Cross entropy). Moreover,
the empirical results reported are coherent across different language pairs.
Robust Learning with Kernel Mean p-Power Error Loss
Authors: Badong Chen, Lei Xing, Xin Wang, Jing Qin, Nanning Zheng
Date: 2016-12-21 00:00:00

Correntropy is a second order statistical measure in kernel space, which has
been successfully applied in robust learning and signal processing. In this
paper, we define a nonsecond order statistical measure in kernel space, called
the kernel mean-p power error (KMPE), including the correntropic loss (CLoss)
as a special case. Some basic properties of KMPE are presented. In particular,
we apply the KMPE to extreme learning machine (ELM) and principal component
analysis (PCA), and develop two robust learning algorithms, namely ELM-KMPE and
PCA-KMPE. Experimental results on synthetic and benchmark data show that the
developed algorithms can achieve consistently better performance when compared
with some existing methods.
Latent Tree Models for Hierarchical Topic Detection
Authors: Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K. M. Poon, Zhourong Chen, Farhan Khawar
Date: 2016-05-21 00:00:00
Tasks: Clustering, Topic Models

We present a novel method for hierarchical topic detection where topics are
obtained by clustering documents in multiple ways. Specifically, we model
document collections using a class of graphical models called hierarchical
latent tree models (HLTMs). The variables at the bottom level of an HLTM are
observed binary variables that represent the presence/absence of words in a
document. The variables at other levels are binary latent variables, with those
at the lowest latent level representing word co-occurrence patterns and those
at higher levels representing co-occurrence of patterns at the level below.
Each latent variable gives a soft partition of the documents, and document
clusters in the partitions are interpreted as topics. Latent variables at high
levels of the hierarchy capture long-range word co-occurrence patterns and
hence give thematically more general topics, while those at low levels of the
hierarchy capture short-range word co-occurrence patterns and give thematically
more specific topics. Unlike LDA-based topic models, HLTMs do not refer to a
document generation process and use word variables instead of token variables.
They use a tree structure to model the relationships between topics and words,
which is conducive to the discovery of meaningful topics and topic hierarchies.
A New Statistic Feature of the Short-Time Amplitude Spectrum Values for Human's Unvoiced Pronunciation
Authors: Xiaodong Zhuang
Date: 2016-09-23 00:00:00

In this paper, a new statistic feature of the discrete short-time amplitude
spectrum is discovered by experiments for the signals of unvoiced
pronunciation. For the random-varying short-time spectrum, this feature reveals
the relationship between the amplitude's average and its standard for every
frequency component. On the other hand, the association between the amplitude
distributions for different frequency components is also studied. A new model
representing such association is inspired by the normalized histogram of
amplitude. By mathematical analysis, the new statistic feature discovered is
proved to be necessary evidence which supports the proposed model, and also can
be direct evidence for the widely used hypothesis of "identical distribution of
amplitude for all frequencies".
Online and stochastic Douglas-Rachford splitting method for large scale machine learning
Authors: Ziqiang Shi, Rujie Liu
Date: 2013-08-22 00:00:00
Tasks: BIG-bench Machine Learning

Online and stochastic learning has emerged as powerful tool in large scale
optimization. In this work, we generalize the Douglas-Rachford splitting (DRs)
method for minimizing composite functions to online and stochastic settings (to
our best knowledge this is the first time DRs been generalized to sequential
version). We first establish an $O(1/\sqrt{T})$ regret bound for batch DRs
method. Then we proved that the online DRs splitting method enjoy an $O(1)$
regret bound and stochastic DRs splitting has a convergence rate of
$O(1/\sqrt{T})$. The proof is simple and intuitive, and the results and
technique can be served as a initiate for the research on the large scale
machine learning employ the DRs method. Numerical experiments of the proposed
method demonstrate the effectiveness of the online and stochastic update rule,
and further confirm our regret and convergence analysis.
Convolutional Recurrent Neural Networks for Music Classification
Authors: Keunwoo Choi, George Fazekas, Mark Sandler, Kyunghyun Cho
Date: 2016-09-14 00:00:00
Tasks: General Classification, Music Classification, Music Tagging

We introduce a convolutional recurrent neural network (CRNN) for music
tagging. CRNNs take advantage of convolutional neural networks (CNNs) for local
feature extraction and recurrent neural networks for temporal summarisation of
the extracted features. We compare CRNN with three CNN structures that have
been used for music tagging while controlling the number of parameters with
respect to their performance and training time per sample. Overall, we found
that CRNNs show a strong performance with respect to the number of parameter
and training time, indicating the effectiveness of its hybrid structure in
music feature extraction and feature summarisation.
Development of a Real-time Colorectal Tumor Classification System for Narrow-band Imaging zoom-videoendoscopy
Authors: Tsubasa Hirakawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda, Tetsushi Koide, Shigeto Yoshida, Hiroshi Mieno, Shinji Tanaka
Date: 2016-12-15 00:00:00
Tasks: General Classification

Colorectal endoscopy is important for the early detection and treatment of
colorectal cancer and is used worldwide. A computer-aided diagnosis (CAD)
system that provides an objective measure to endoscopists during colorectal
endoscopic examinations would be of great value. In this study, we describe a
newly developed CAD system that provides real-time objective measures. Our
system captures the video stream from an endoscopic system and transfers it to
a desktop computer. The captured video stream is then classified by a
pretrained classifier and the results are displayed on a monitor. The
experimental results show that our developed system works efficiently in actual
endoscopic examinations and is medically significant.
Unsupervised Place Discovery for Visual Place Classification
Authors: Fei Xiaoxiao, Tanaka Kanji, Inamoto Kouya
Date: 2016-12-21 00:00:00
Tasks: Classification, General Classification, Open-Ended Question Answering

In this study, we explore the use of deep convolutional neural networks
(DCNNs) in visual place classification for robotic mapping and localization. An
open question is how to partition the robot's workspace into places to maximize
the performance (e.g., accuracy, precision, recall) of potential DCNN
classifiers. This is a chicken and egg problem: If we had a well-trained DCNN
classifier, it is rather easy to partition the robot's workspace into places,
but the training of a DCNN classifier requires a set of pre-defined place
classes. In this study, we address this problem and present several strategies
for unsupervised discovery of place classes ("time cue," "location cue,"
"time-appearance cue," and "location-appearance cue"). We also evaluate the
efficacy of the proposed methods using the publicly available University of
Michigan North Campus Long-Term (NCLT) Dataset.
A Statistical Approach to Continuous Self-Calibrating Eye Gaze Tracking for Head-Mounted Virtual Reality Systems
Authors: Subarna Tripathi, Brian Guenter
Date: 2016-12-20 00:00:00
Tasks: Position, regression

We present a novel, automatic eye gaze tracking scheme inspired by smooth
pursuit eye motion while playing mobile games or watching virtual reality
contents. Our algorithm continuously calibrates an eye tracking system for a
head mounted display. This eliminates the need for an explicit calibration step
and automatically compensates for small movements of the headset with respect
to the head. The algorithm finds correspondences between corneal motion and
screen space motion, and uses these to generate Gaussian Process Regression
models. A combination of those models provides a continuous mapping from
corneal position to screen space position. Accuracy is nearly as good as
achieved with an explicit calibration step.
Knowledge Distillation for Small-footprint Highway Networks
Authors: Liang Lu, Michelle Guo, Steve Renals
Date: 2016-08-02 00:00:00
Tasks: Acoustic Modelling, Knowledge Distillation, speech-recognition, Speech Recognition

Deep learning has significantly advanced state-of-the-art of speech
recognition in the past few years. However, compared to conventional Gaussian
mixture acoustic models, neural network models are usually much larger, and are
therefore not very deployable in embedded devices. Previously, we investigated
a compact highway deep neural network (HDNN) for acoustic modelling, which is a
type of depth-gated feedforward neural network. We have shown that HDNN-based
acoustic models can achieve comparable recognition accuracy with much smaller
number of model parameters compared to plain deep neural network (DNN) acoustic
models. In this paper, we push the boundary further by leveraging on the
knowledge distillation technique that is also known as {\it teacher-student}
training, i.e., we train the compact HDNN model with the supervision of a high
accuracy cumbersome model. Furthermore, we also investigate sequence training
and adaptation in the context of teacher-student training. Our experiments were
performed on the AMI meeting speech recognition corpus. With this technique, we
significantly improved the recognition accuracy of the HDNN acoustic model with
less than 0.8 million parameters, and narrowed the gap between this model and
the plain DNN with 30 million parameters.
Fast Domain Adaptation for Neural Machine Translation
Authors: Markus Freitag, Yaser Al-Onaizan
Date: 2016-12-20 00:00:00
Tasks: Domain Adaptation, Machine Translation, NMT, Translation

Neural Machine Translation (NMT) is a new approach for automatic translation
of text from one human language into another. The basic concept in NMT is to
train a large Neural Network that maximizes the translation performance on a
given parallel corpus. NMT is gaining popularity in the research community
because it outperformed traditional SMT approaches in several translation tasks
at WMT and other evaluation tasks/benchmarks at least for some language pairs.
However, many of the enhancements in SMT over the years have not been
incorporated into the NMT framework. In this paper, we focus on one such
enhancement namely domain adaptation. We propose an approach for adapting a NMT
system to a new domain. The main idea behind domain adaptation is that the
availability of large out-of-domain training data and a small in-domain
training data. We report significant gains with our proposed method in both
automatic metrics and a human subjective evaluation metric on two language
pairs. With our adaptation method, we show large improvement on the new domain
while the performance of our general domain only degrades slightly. In
addition, our approach is fast enough to adapt an already trained system to a
new domain within few hours without the need to retrain the NMT model on the
combined data which usually takes several days/weeks depending on the volume of
the data.
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Authors: Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
Date: 2016-12-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diagnostic, Question Answering, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

When building artificial intelligence systems that can reason and answer
questions about visual data, we need diagnostic tests to analyze our progress
and discover shortcomings. Existing benchmarks for visual question answering
can help, but have strong biases that models can exploit to correctly answer
questions without reasoning. They also conflate multiple sources of error,
making it hard to pinpoint model weaknesses. We present a diagnostic dataset
that tests a range of visual reasoning abilities. It contains minimal biases
and has detailed annotations describing the kind of reasoning each question
requires. We use this dataset to analyze a variety of modern visual reasoning
systems, providing novel insights into their abilities and limitations.
A tree-based kernel for graphs with continuous attributes
Authors: Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti
Date: 2015-09-03 00:00:00
Tasks: Computational Efficiency

The availability of graph data with node attributes that can be either
discrete or real-valued is constantly increasing. While existing kernel methods
are effective techniques for dealing with graphs having discrete node labels,
their adaptation to non-discrete or continuous node attributes has been
limited, mainly for computational issues. Recently, a few kernels especially
tailored for this domain, and that trade predictive performance for
computational efficiency, have been proposed. In this paper, we propose a graph
kernel for complex and continuous nodes' attributes, whose features are tree
structures extracted from specific graph visits. The kernel manages to keep the
same complexity of state-of-the-art kernels while implicitly using a larger
feature space. We further present an approximated variant of the kernel which
reduces its complexity significantly. Experimental results obtained on six
real-world datasets show that the kernel is the best performing one on most of
them. Moreover, in most cases the approximated version reaches comparable
performances to current state-of-the-art kernels in terms of classification
accuracy while greatly shortening the running times.
Local Sparse Approximation for Image Restoration with Adaptive Block Size Selection
Authors: Sujit Kumar Sahoo
Date: 2016-12-20 00:00:00
Tasks: Denoising, Image Restoration

In this paper the problem of image restoration (denoising and inpainting) is
approached using sparse approximation of local image blocks. The local image
blocks are extracted by sliding square windows over the image. An adaptive
block size selection procedure for local sparse approximation is proposed,
which affects the global recovery of underlying image. Ideally the adaptive
local block selection yields the minimum mean square error (MMSE) in recovered
image. This framework gives us a clustered image based on the selected block
size, then each cluster is restored separately using sparse approximation. The
results obtained using the proposed framework are very much comparable with the
recently proposed image restoration techniques.
Action-Driven Object Detection with Top-Down Visual Attentions
Authors: Donggeun Yoo, Sunggyun Park, Kyunghyun Paeng, Joon-Young Lee, In So Kweon
Date: 2016-12-20 00:00:00
Tasks: Object, object-detection, Object Detection

A dominant paradigm for deep learning based object detection relies on a
"bottom-up" approach using "passive" scoring of class agnostic proposals. These
approaches are efficient but lack of holistic analysis of scene-level context.
In this paper, we present an "action-driven" detection mechanism using our
"top-down" visual attention model. We localize an object by taking sequential
actions that the attention model provides. The attention model conditioned with
an image region provides required actions to get closer toward a target object.
An action at each time step is weak itself but an ensemble of the sequential
actions makes a bounding-box accurately converge to a target object boundary.
This attention model we call AttentionNet is composed of a convolutional neural
network. During our whole detection procedure, we only utilize the actions from
a single AttentionNet without any modules for object proposals nor post
bounding-box regression. We evaluate our top-down detection mechanism over the
PASCAL VOC series and ILSVRC CLS-LOC dataset, and achieve state-of-the-art
performances compared to the major bottom-up detection methods. In particular,
our detection mechanism shows a strong advantage in elaborate localization by
outperforming Faster R-CNN with a margin of +7.1% over PASCAL VOC 2007 when we
increase the IoU threshold for positive detection to 0.7.
Dynamic Action Recognition: A convolutional neural network model for temporally organized joint location data
Authors: Adhavan Jayabalan, Harish Karunakaran, Shravan Murlidharan, Tesia Shizume
Date: 2016-12-20 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Motivation: Recognizing human actions in a video is a challenging task which
has applications in various fields. Previous works in this area have either
used images from a 2D or 3D camera. Few have used the idea that human actions
can be easily identified by the movement of the joints in the 3D space and
instead used a Recurrent Neural Network (RNN) for modeling. Convolutional
neural networks (CNN) have the ability to recognise even the complex patterns
in data which makes it suitable for detecting human actions. Thus, we modeled a
CNN which can predict the human activity using the joint data. Furthermore,
using the joint data representation has the benefit of lower dimensionality
than image or video representations. This makes our model simpler and faster
than the RNN models. In this study, we have developed a six layer convolutional
network, which reduces each input feature vector of the form 15x1961x4 to an
one dimensional binary vector which gives us the predicted activity. Results:
Our model is able to recognise an activity correctly upto 87% accuracy. Joint
data is taken from the Cornell Activity Datasets which have day to day
activities like talking, relaxing, eating, cooking etc.