pwc-archive/papers-with-abstracts huggingface dataset:
A Projection Pursuit Forest Algorithm for Supervised Classification
Authors: Natalia da Silva, Dianne Cook, Eun-Kyung Lee
Date: 2018-07-19 00:00:00
Tasks: Classification, Ensemble Learning, General Classification

This paper presents a new ensemble learning method for classification
problems called projection pursuit random forest (PPF). PPF uses the PPtree
algorithm introduced in Lee et al. (2013). In PPF, trees are constructed by
splitting on linear combinations of randomly chosen variables. Projection
pursuit is used to choose a projection of the variables that best separates the
classes. Utilizing linear combinations of variables to separate classes takes
the correlation between variables into account which allows PPF to outperform a
traditional random forest when separations between groups occurs in
combinations of variables.
  The method presented here can be used in multi-class problems and is
implemented into an R (R Core Team, 2018) package, PPforest, which is available
on CRAN, with development versions at https://github.com/natydasilva/PPforest.
Few-Shot Adaptation for Multimedia Semantic Indexing
Authors: Nakamasa Inoue, Koichi Shinoda
Date: 2018-07-19 00:00:00
Tasks: Few-Shot Learning, parameter estimation, Zero-Shot Learning

We propose a few-shot adaptation framework, which bridges zero-shot learning
and supervised many-shot learning, for semantic indexing of image and video
data. Few-shot adaptation provides robust parameter estimation with few
training examples, by optimizing the parameters of zero-shot learning and
supervised many-shot learning simultaneously. In this method, first we build a
zero-shot detector, and then update it by using the few examples. Our
experiments show the effectiveness of the proposed framework on three datasets:
TRECVID Semantic Indexing 2010, 2014, and ImageNET. On the ImageNET dataset, we
show that our method outperforms recent few-shot learning methods. On the
TRECVID 2014 dataset, we achieve 15.19% and 35.98% in Mean Average Precision
under the zero-shot condition and the supervised condition, respectively. To
the best of our knowledge, these are the best results on this dataset.
Efficient Training on Very Large Corpora via Gramian Estimation
Authors: Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed Chi, John Anderson
Date: 2018-07-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Network Embedding

We study the problem of learning similarity functions over very large corpora
using neural network embedding models. These models are typically trained using
SGD with sampling of random observed and unobserved pairs, with a number of
samples that grows quadratically with the corpus size, making it expensive to
scale to very large corpora. We propose new efficient methods to train these
models without having to sample unobserved pairs. Inspired by matrix
factorization, our approach relies on adding a global quadratic penalty to all
pairs of examples and expressing this term as the matrix-inner-product of two
generalized Gramians. We show that the gradient of this term can be efficiently
computed by maintaining estimates of the Gramians, and develop variance
reduction schemes to improve the quality of the estimates. We conduct
large-scale experiments that show a significant improvement in training time
and generalization quality compared to traditional sampling methods.
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
Authors: Yadollah Yaghoobzadeh, Katharina Kann, Hinrich Schütze
Date: 2018-07-18 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentence, Word Embeddings

Embedding models typically associate each word with a single real-valued
vector, representing its different properties. Evaluation methods, therefore,
need to analyze the accuracy and completeness of these properties in
embeddings. This requires fine-grained analysis of embedding subspaces.
Multi-label classification is an appropriate way to do so. We propose a new
evaluation method for word embeddings based on multi-label classification given
a word embedding. The task we use is fine-grained name typing: given a large
corpus, find all types that a name can refer to based on the name embedding.
Given the scale of entities in knowledge bases, we can build datasets for this
task that are complementary to the current embedding evaluation datasets in:
they are very large, contain fine-grained classes, and allow the direct
evaluation of embeddings without confounding factors like sentence context
Automatic Identification of Ineffective Online Student Questions in Computing Education
Authors: Qiang Hao, April Galyardt, Bradley Barnes, Robert Maribe Branch, Ewan Wright
Date: 2018-07-18 00:00:00

This Research Full Paper explores automatic identification of ineffective
learning questions in the context of large-scale computer science classes. The
immediate and accurate identification of ineffective learning questions opens
the door to possible automated facilitation on a large scale, such as alerting
learners to revise questions and providing adaptive question revision
suggestions. To achieve this, 983 questions were collected from a question &
answer platform implemented by an introductory programming course over three
semesters in a large research university in the Southeastern United States.
Questions were firstly manually classified into three hierarchical categories:
1) learning-irrelevant questions, 2) effective learning-relevant questions, 3)
ineffective learningrelevant questions. The inter-rater reliability of the
manual classification (Cohen's Kappa) was .88. Four different machine learning
algorithms were then used to automatically classify the questions, including
Naive Bayes Multinomial, Logistic Regression, Support Vector Machines, and
Boosted Decision Tree. Both flat and single path strategies were explored, and
the most effective algorithms under both strategies were identified and
discussed. This study contributes to the automatic determination of learning
question quality in computer science, and provides evidence for the feasibility
of automated facilitation of online question & answer in large scale computer
science classes.
Approximation Schemes for Low-Rank Binary Matrix Approximation Problems
Authors: Fedor V. Fomin, Petr A. Golovach, Daniel Lokshtanov, Fahad Panolan, Saket Saurabh
Date: 2018-07-18 00:00:00
Tasks: Clustering, Constrained Clustering, LEMMA

We provide a randomized linear time approximation scheme for a generic
problem about clustering of binary vectors subject to additional constrains.
The new constrained clustering problem encompasses a number of problems and by
solving it, we obtain the first linear time-approximation schemes for a number
of well-studied fundamental problems concerning clustering of binary vectors
and low-rank approximation of binary matrices. Among the problems solvable by
our approach are \textsc{Low GF(2)-Rank Approximation}, \textsc{Low
Boolean-Rank Approximation}, and various versions of \textsc{Binary
Clustering}. For example, for \textsc{Low GF(2)-Rank Approximation} problem,
where for an $m\times n$ binary matrix $A$ and integer $r>0$, we seek for a
binary matrix $B$ of $GF_2$ rank at most $r$ such that $\ell_0$ norm of matrix
$A-B$ is minimum, our algorithm, for any $\epsilon>0$ in time $
f(r,\epsilon)\cdot n\cdot m$, where $f$ is some computable function, outputs a
$(1+\epsilon)$-approximate solution with probability at least
$(1-\frac{1}{e})$. Our approximation algorithms substantially improve the
running times and approximation factors of previous works. We also give
(deterministic) PTASes for these problems running in time
$n^{f(r)\frac{1}{\epsilon^2}\log \frac{1}{\epsilon}}$, where $f$ is some
function depending on the problem. Our algorithm for the constrained clustering
problem is based on a novel sampling lemma, which is interesting in its own.
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
Authors: Stephen Law, Brooks Paige, Chris Russell
Date: 2018-07-18 00:00:00

When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Authors: Youbao Tang, Jinzheng Cai, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers
Date: 2018-07-18 00:00:00
Tasks: Computed Tomography (CT), Image Enhancement, Lesion Segmentation, Medical Image Analysis, Segmentation, Transfer Learning

Automated lesion segmentation from computed tomography (CT) is an important
and challenging task in medical image analysis. While many advancements have
been made, there is room for continued improvements. One hurdle is that CT
images can exhibit high noise and low contrast, particularly in lower dosages.
To address this, we focus on a preprocessing method for CT images that uses
stacked generative adversarial networks (SGAN) approach. The first GAN reduces
the noise in the CT image and the second GAN generates a higher resolution
image with enhanced boundaries and high contrast. To make up for the absence of
high quality CT images, we detail how to synthesize a large number of low- and
high-quality natural images and use transfer learning with progressively larger
amounts of CT images. We apply both the classic GrabCut method and the modern
holistically nested network (HNN) to lesion segmentation, testing whether SGAN
can yield improved lesion segmentation. Experimental results on the DeepLesion
dataset demonstrate that the SGAN enhancements alone can push GrabCut
performance over HNN trained on original images. We also demonstrate that HNN +
SGAN performs best compared against four other enhancement methods, including
when using only a single GAN.
Representational efficiency outweighs action efficiency in human program induction
Authors: Sophia Sanborn, David D. Bourgin, Michael Chang, Thomas L. Griffiths
Date: 2018-07-18 00:00:00
Tasks: Hierarchical Reinforcement Learning, Program induction, Reinforcement Learning, Reinforcement Learning (RL)

The importance of hierarchically structured representations for tractable
planning has long been acknowledged. However, the questions of how people
discover such abstractions and how to define a set of optimal abstractions
remain open. This problem has been explored in cognitive science in the problem
solving literature and in computer science in hierarchical reinforcement
learning. Here, we emphasize an algorithmic perspective on learning
hierarchical representations in which the objective is to efficiently encode
the structure of the problem, or, equivalently, to learn an algorithm with
minimal length. We introduce a novel problem-solving paradigm that links
problem solving and program induction under the Markov Decision Process (MDP)
framework. Using this task, we target the question of whether humans discover
hierarchical solutions by maximizing efficiency in number of actions they
generate or by minimizing the complexity of the resulting representation and
find evidence for the primacy of representational efficiency.
Newton-ADMM: A Distributed GPU-Accelerated Optimizer for Multiclass Classification Problems
Authors: Chih-Hao Fang, Sudhir B. Kylasa, Fred Roosta, Michael W. Mahoney, Ananth Grama
Date: 2018-07-18 00:00:00
Tasks: General Classification, GPU

First-order optimization methods, such as stochastic gradient descent (SGD) and its variants, are widely used in machine learning applications due to their simplicity and low per-iteration costs. However, they often require larger numbers of iterations, with associated communication costs in distributed environments. In contrast, Newton-type methods, while having higher per-iteration costs, typically require a significantly smaller number of iterations, which directly translates to reduced communication costs. In this paper, we present a novel distributed optimizer for classification problems, which integrates a GPU-accelerated Newton-type solver with the global consensus formulation of Alternating Direction of Method Multipliers (ADMM). By leveraging the communication efficiency of ADMM, GPU-accelerated inexact-Newton solver, and an effective spectral penalty parameter selection strategy, we show that our proposed method (i) yields better generalization performance on several classification problems; (ii) significantly outperforms state-of-the-art methods in distributed time to solution; and (iii) offers better scaling on large distributed platforms.
Semantic Parsing: Syntactic assurance to target sentence using LSTM Encoder CFG-Decoder
Authors: Fabiano Ferreira Luz, Marcelo Finger
Date: 2018-07-18 00:00:00
Tasks: Decoder, Semantic Parsing, Sentence, Translation

Semantic parsing can be defined as the process of mapping natural language
sentences into a machine interpretable, formal representation of its meaning.
Semantic parsing using LSTM encoder-decoder neural networks have become
promising approach. However, human automated translation of natural language
does not provide grammaticality guarantees for the sentences generate such a
guarantee is particularly important for practical cases where a data base query
can cause critical errors if the sentence is ungrammatical. In this work, we
propose an neural architecture called Encoder CFG-Decoder, whose output
conforms to a given context-free grammar. Results are show for any
implementation of such architecture display its correctness and providing
benchmark accuracy levels better than the literature.
Hierarchical Multi Task Learning With CTC
Authors: Ramon Sanabria, Florian Metze
Date: 2018-07-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, Multi-Task Learning, speech-recognition, Speech Recognition

In Automatic Speech Recognition it is still challenging to learn useful
intermediate representations when using high-level (or abstract) target units
such as words. For that reason, character or phoneme based systems tend to
outperform word-based systems when just few hundreds of hours of training data
are being used. In this paper, we first show how hierarchical multi-task
training can encourage the formation of useful intermediate representations. We
achieve this by performing Connectionist Temporal Classification at different
levels of the network with targets of different granularity. Our model thus
performs predictions in multiple scales for the same input. On the standard
300h Switchboard training setup, our hierarchical multi-task architecture
exhibits improvements over single-task architectures with the same number of
parameters. Our model obtains 14.0% Word Error Rate on the Eval2000 Switchboard
subset without any decoder or language model, outperforming the current
state-of-the-art on acoustic-to-word models.
Comparative study of Discrete Wavelet Transforms and Wavelet Tensor Train decomposition to feature extraction of FTIR data of medicinal plants
Authors: Pavel Kharyuk, Dmitry Nazarenko, Ivan Oseledets
Date: 2018-07-18 00:00:00
Tasks: Clustering, General Classification

Fourier-transform infra-red (FTIR) spectra of samples from 7 plant species
were used to explore the influence of preprocessing and feature extraction on
efficiency of machine learning algorithms. Wavelet Tensor Train (WTT) and
Discrete Wavelet Transforms (DWT) were compared as feature extraction
techniques for FTIR data of medicinal plants. Various combinations of signal
processing steps showed different behavior when applied to classification and
clustering tasks. Best results for WTT and DWT found through grid search were
similar, significantly improving quality of clustering as well as
classification accuracy for tuned logistic regression in comparison to original
spectra. Unlike DWT, WTT has only one parameter to be tuned (rank), making it a
more versatile and easier to use as a data processing tool in various signal
processing applications.
Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints
Authors: Kaveh Bastani, Hamed Namavari, Jeffry Shaffer
Date: 2018-07-18 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Decision Making

A text mining approach is proposed based on latent Dirichlet allocation (LDA)
to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints.
The proposed approach aims to extract latent topics in the CFPB complaint
narratives, and explores their associated trends over time. The time trends
will then be used to evaluate the effectiveness of the CFPB regulations and
expectations on financial institutions in creating a consumer oriented culture
that treats consumers fairly and prioritizes consumer protection in their
decision making processes. The proposed approach can be easily operationalized
as a decision support system to automate detection of emerging topics in
consumer complaints. Hence, the technology-human partnership between the
proposed approach and the CFPB team could certainly improve consumer
protections from unfair, deceptive or abusive practices in the financial
markets by providing more efficient and effective investigations of consumer
complaint narratives.
Real-Time Stereo Vision for Road Surface 3-D Reconstruction
Authors: Rui Fan, Yanan Liu, Xingrui Yang, Mohammud Junaid Bocus, Naim Dahnoun, Scott Tancock
Date: 2018-07-18 00:00:00
Tasks: GPU, Stereo Matching, Stereo Matching Hand

Stereo vision techniques have been widely used in civil engineering to
acquire 3-D road data. The two important factors of stereo vision are accuracy
and speed. However, it is very challenging to achieve both of them
simultaneously and therefore the main aim of developing a stereo vision system
is to improve the trade-off between these two factors. In this paper, we
present a real-time stereo vision system used for road surface 3-D
reconstruction. The proposed system is developed from our previously published
3-D reconstruction algorithm where the perspective view of the target image is
first transformed into the reference view, which not only increases the
disparity accuracy but also improves the processing speed. Then, the
correlation cost between each pair of blocks is computed and stored in two 3-D
cost volumes. To adaptively aggregate the matching costs from neighbourhood
systems, bilateral filtering is performed on the cost volumes. This greatly
reduces the ambiguities during stereo matching and further improves the
precision of the estimated disparities. Finally, the subpixel resolution is
achieved by conducting a parabola interpolation and the subpixel disparity map
is used to reconstruct the 3-D road surface. The proposed algorithm is
implemented on an NVIDIA GTX 1080 GPU for the real-time purpose. The
experimental results illustrate that the reconstruction accuracy is around 3
mm.
Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm
Authors: Thomas W. Mitchel, Sipu Ruan, Gregory S. Chirikjian
Date: 2018-07-18 00:00:00
Tasks: Action Recognition, Computational Efficiency, Optical Flow Estimation, Temporal Action Localization

The general ability to analyze and classify the 3D kinematics of the human
form is an essential step in the development of socially adept humanoid robots.
A variety of different types of signals can be used by machines to represent
and characterize actions such as RGB videos, infrared maps, and optical flow.
In particular, skeleton sequences provide a natural 3D kinematic description of
human motions and can be acquired in real time using RGB+D cameras. Moreover,
skeleton sequences are generalizable to characterize the motions of both humans
and humanoid robots. The Globally Optimal Reparameterization Algorithm (GORA)
is a novel, recently proposed algorithm for signal alignment in which signals
are reparameterized to a globally optimal universal standard timescale (UST).
Here, we introduce a variant of GORA for humanoid action recognition with
skeleton sequences, which we call GORA-S. We briefly review the algorithm's
mathematical foundations and contextualize them in the problem of action
recognition with skeleton sequences. Subsequently, we introduce GORA-S and
discuss parameters and numerical techniques for its effective implementation.
We then compare its performance with that of the DTW and FastDTW algorithms, in
terms of computational efficiency and accuracy in matching skeletons. Our
results show that GORA-S attains a complexity that is significantly less than
that of any tested DTW method. In addition, it displays a favorable balance
between speed and accuracy that remains invariant under changes in skeleton
sampling frequency, lending it a degree of versatility that could make it
well-suited for a variety of action recognition tasks.
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Authors: Liang Yao, Chengsheng Mao, Yuan Luo
Date: 2018-07-17 00:00:00
Tasks: Deep Learning, Entity Embeddings, Feature Engineering, General Classification, text-classification, Text Classification, Word Embeddings

Clinical text classification is an important problem in medical natural
language processing. Existing studies have conventionally focused on rules or
knowledge sources-based feature engineering, but only a few have exploited
effective feature learning capability of deep learning methods. In this study,
we propose a novel approach which combines rule-based features and
knowledge-guided deep learning techniques for effective disease classification.
Critical Steps of our method include identifying trigger phrases, predicting
classes with very few examples using trigger phrases and training a
convolutional neural network with word embeddings and Unified Medical Language
System (UMLS) entity embeddings. We evaluated our method on the 2008
Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge.
The results show that our method outperforms the state of the art methods.