pwc-archive/papers-with-abstracts huggingface dataset:
Structural Analysis of Hindi Phonetics and A Method for Extraction of Phonetically Rich Sentences from a Very Large Hindi Text Corpus
Authors: Shrikant Malviya, Rohit Mishra, Uma Shanker Tiwary
Date: 2017-01-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sentence, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

Automatic speech recognition (ASR) and Text to speech (TTS) are two prominent
area of research in human computer interaction nowadays. A set of phonetically
rich sentences is in a matter of importance in order to develop these two
interactive modules of HCI. Essentially, the set of phonetically rich sentences
has to cover all possible phone units distributed uniformly. Selecting such a
set from a big corpus with maintaining phonetic characteristic based similarity
is still a challenging problem. The major objective of this paper is to devise
a criteria in order to select a set of sentences encompassing all phonetic
aspects of a corpus with size as minimum as possible. First, this paper
presents a statistical analysis of Hindi phonetics by observing the structural
characteristics. Further a two stage algorithm is proposed to extract
phonetically rich sentences with a high variety of triphones from the EMILLE
Hindi corpus. The algorithm consists of a distance measuring criteria to select
a sentence in order to improve the triphone distribution. Moreover, a special
preprocessing method is proposed to score each triphone in terms of inverse
probability in order to fasten the algorithm. The results show that the
approach efficiently build uniformly distributed phonetically-rich corpus with
optimum number of sentences.
Learning Optimized Risk Scores
Authors: Berk Ustun, Cynthia Rudin
Date: 2016-10-01 00:00:00
Tasks: Seizure prediction

Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey application-specific operational constraints. In this paper, we present a new machine learning approach to learn risk scores. We formulate the risk score problem as a mixed integer nonlinear program, and present a cutting plane algorithm for non-convex settings to efficiently recover its optimal solution. We improve our algorithm with specialized techniques to generate feasible solutions, narrow the optimality gap, and reduce data-related computation. Our approach can fit risk scores in a way that scales linearly in the number of samples, provides a certificate of optimality, and obeys real-world constraints without parameter tuning or post-processing. We benchmark the performance benefits of this approach through an extensive set of numerical experiments, comparing to risk scores built using heuristic approaches. We also discuss its practical benefits through a real-world application where we build a customized risk score for ICU seizure prediction in collaboration with the Massachusetts General Hospital.
Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption
Authors: Mostafa Rahmani, George Atia
Date: 2017-02-07 00:00:00

We study a data model in which the data matrix D can be expressed as D = L +
S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a
matrix whose non-zero columns are outlying data points. To date, robust PCA
algorithms have solely considered models with either S or C, but not both. As
such, existing algorithms cannot account for simultaneous element-wise and
column-wise corruptions. In this paper, a new robust PCA algorithm that is
robust to simultaneous types of corruption is proposed. Our approach hinges on
the sparse approximation of a sparsely corrupted column so that the sparse
expansion of a column with respect to the other data points is used to
distinguish a sparsely corrupted inlier column from an outlying data point. We
also develop a randomized design which provides a scalable implementation of
the proposed approach. The core idea of sparse approximation is analyzed
analytically where we show that the underlying ell_1-norm minimization can
obtain the representation of an inlier in presence of sparse corruptions.
Regression with n$\to$1 by Expert Knowledge Elicitation
Authors: Marta Soare, Muhammad Ammad-Ud-Din, Samuel Kaski
Date: 2016-05-20 00:00:00
Tasks: regression

We consider regression under the "extremely small $n$ large $p$" condition,
where the number of samples $n$ is so small compared to the dimensionality $p$
that predictors cannot be estimated without prior knowledge. This setup occurs
in personalized medicine, for instance, when predicting treatment outcomes for
an individual patient based on noisy high-dimensional genomics data. A
remaining source of information is expert knowledge, which has received
relatively little attention in recent years. We formulate the inference problem
of asking expert feedback on features on a budget, propose an elicitation
strategy for a simple "small $n$" setting, and derive conditions under which
the elicitation strategy is optimal. Experiments on simulated experts, both on
synthetic and genomics data, demonstrate that the proposed strategy can
drastically improve prediction accuracy.
Landmark-Based Plan Recognition
Authors: Ramon Fraga Pereira, Felipe Meneguzzi
Date: 2016-04-05 00:00:00

Recognition of goals and plans using incomplete evidence from action
execution can be done efficiently by using planning techniques. In many
applications it is important to recognize goals and plans not only accurately,
but also quickly. In this paper, we develop a heuristic approach for
recognizing plans based on planning techniques that rely on ordering
constraints to filter candidate goals from observations. These ordering
constraints are called landmarks in the planning literature, which are facts or
actions that cannot be avoided to achieve a goal. We show the applicability of
planning landmarks in two settings: first, we use it directly to develop a
heuristic-based plan recognition approach; second, we refine an existing
planning-based plan recognition approach by pre-filtering its candidate goals.
Our empirical evaluation shows that our approach is not only substantially more
accurate than the state-of-the-art in all available datasets, it is also an
order of magnitude faster.
Trimming the Independent Fat: Sufficient Statistics, Mutual Information, and Predictability from Effective Channel States
Authors: Ryan G. James, John R. Mahoney, James P. Crutchfield
Date: 2017-02-07 00:00:00

One of the most fundamental questions one can ask about a pair of random
variables X and Y is the value of their mutual information. Unfortunately, this
task is often stymied by the extremely large dimension of the variables. We
might hope to replace each variable by a lower-dimensional representation that
preserves the relationship with the other variable. The theoretically ideal
implementation is the use of minimal sufficient statistics, where it is
well-known that either X or Y can be replaced by their minimal sufficient
statistic about the other while preserving the mutual information. While
intuitively reasonable, it is not obvious or straightforward that both
variables can be replaced simultaneously. We demonstrate that this is in fact
possible: the information X's minimal sufficient statistic preserves about Y is
exactly the information that Y's minimal sufficient statistic preserves about
X. As an important corollary, we consider the case where one variable is a
stochastic process' past and the other its future and the present is viewed as
a memoryful channel. In this case, the mutual information is the channel
transmission rate between the channel's effective states. That is, the
past-future mutual information (the excess entropy) is the amount of
information about the future that can be predicted using the past. Translating
our result about minimal sufficient statistics, this is equivalent to the
mutual information between the forward- and reverse-time causal states of
computational mechanics. We close by discussing multivariate extensions to this
use of minimal sufficient statistics.
Deep Learning Models of the Retinal Response to Natural Scenes
Authors: Lane T. McIntosh, Niru Maheswaranathan, Aran Nayebi, Surya Ganguli, Stephen A. Baccus
Date: 2017-02-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Deep Learning

A central challenge in neuroscience is to understand neural computations and
circuit mechanisms that underlie the encoding of ethologically relevant,
natural stimuli. In multilayered neural circuits, nonlinear processes such as
synaptic transmission and spiking dynamics present a significant obstacle to
the creation of accurate computational models of responses to natural stimuli.
Here we demonstrate that deep convolutional neural networks (CNNs) capture
retinal responses to natural scenes nearly to within the variability of a
cell's response, and are markedly more accurate than linear-nonlinear (LN)
models and Generalized Linear Models (GLMs). Moreover, we find two additional
surprising properties of CNNs: they are less susceptible to overfitting than
their LN counterparts when trained on small amounts of data, and generalize
better when tested on stimuli drawn from a different distribution (e.g. between
natural scenes and white noise). Examination of trained CNNs reveals several
properties. First, a richer set of feature maps is necessary for predicting the
responses to natural scenes compared to white noise. Second, temporally precise
responses to slowly varying inputs originate from feedforward inhibition,
similar to known retinal mechanisms. Third, the injection of latent noise
sources in intermediate layers enables our model to capture the sub-Poisson
spiking variability observed in retinal ganglion cells. Fourth, augmenting our
CNNs with recurrent lateral connections enables them to capture contrast
adaptation as an emergent property of accurately describing retinal responses
to natural scenes. These methods can be readily generalized to other sensory
modalities and stimulus ensembles. Overall, this work demonstrates that CNNs
not only accurately capture sensory circuit responses to natural scenes, but
also yield information about the circuit's internal structure and function.
Predicting Pairwise Relations with Neural Similarity Encoders
Authors: Franziska Horn, Klaus-Robert Müller
Date: 2017-02-06 00:00:00
Tasks: Collaborative Filtering, Dimensionality Reduction, Missing Values, Recommendation Systems

Matrix factorization is at the heart of many machine learning algorithms, for
example, dimensionality reduction (e.g. kernel PCA) or recommender systems
relying on collaborative filtering. Understanding a singular value
decomposition (SVD) of a matrix as a neural network optimization problem
enables us to decompose large matrices efficiently while dealing naturally with
missing values in the given matrix. But most importantly, it allows us to learn
the connection between data points' feature vectors and the matrix containing
information about their pairwise relations. In this paper we introduce a novel
neural network architecture termed Similarity Encoder (SimEc), which is
designed to simultaneously factorize a given target matrix while also learning
the mapping to project the data points' feature vectors into a similarity
preserving embedding space. This makes it possible to, for example, easily
compute out-of-sample solutions for new data points. Additionally, we
demonstrate that SimEc can preserve non-metric similarities and even predict
multiple pairwise relations between data points at once.
Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks
Authors: David Ledbetter, Long Ho, Kevin V Lemley
Date: 2017-02-06 00:00:00
Tasks: Kidney Function

A Convolutional Neural Network was used to predict kidney function in
patients with chronic kidney disease from high-resolution digital pathology
scans of their kidney biopsies. Kidney biopsies were taken from participants of
the NEPTUNE study, a longitudinal cohort study whose goal is to set up
infrastructure for observing the evolution of 3 forms of idiopathic nephrotic
syndrome, including developing predictors for progression of kidney disease.
The knowledge of future kidney function is desirable as it can identify
high-risk patients and influence treatment decisions, reducing the likelihood
of irreversible kidney decline.
Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks
Authors: Mohammad Javad Shafiee, Akshaya Mishra, Alexander Wong
Date: 2016-06-14 00:00:00
Tasks: Deep Learning

Taking inspiration from biological evolution, we explore the idea of "Can
deep neural networks evolve naturally over successive generations into highly
efficient deep neural networks?" by introducing the notion of synthesizing new
highly efficient, yet powerful deep neural networks over successive generations
via an evolutionary process from ancestor deep neural networks. The
architectural traits of ancestor deep neural networks are encoded using
synaptic probability models, which can be viewed as the `DNA' of these
networks. New descendant networks with differing network architectures are
synthesized based on these synaptic probability models from the ancestor
networks and computational environmental factor models, in a random manner to
mimic heredity, natural selection, and random mutation. These offspring
networks are then trained into fully functional networks, like one would train
a newborn, and have more efficient, more diverse network architectures than
their ancestor networks, while achieving powerful modeling capabilities.
Experimental results for the task of visual saliency demonstrated that the
synthesized `evolved' offspring networks can achieve state-of-the-art
performance while having network architectures that are significantly more
efficient (with a staggering $\sim$48-fold decrease in synapses by the fourth
generation) compared to the original ancestor network.
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data
Authors: Adedotun Akintayo, Soumik Sarkar
Date: 2017-02-06 00:00:00
Tasks: Change Detection, Time Series, Time Series Analysis

This paper proposes a hierarchical feature extractor for non-stationary
streaming time series based on the concept of switching observable Markov chain
models. The slow time-scale non-stationary behaviors are considered to be a
mixture of quasi-stationary fast time-scale segments that are exhibited by
complex dynamical systems. The idea is to model each unique stationary
characteristic without a priori knowledge (e.g., number of possible unique
characteristics) at a lower logical level, and capture the transitions from one
low-level model to another at a higher level. In this context, the concepts in
the recently developed Symbolic Dynamic Filtering (SDF) is extended, to build
an online algorithm suited for handling quasi-stationary data at a lower level
and a non-stationary behavior at a higher level without a priori knowledge. A
key observation made in this study is that the rate of change of data
likelihood seems to be a better indicator of change in data characteristics
compared to the traditional methods that mostly consider data likelihood for
change detection. The algorithm minimizes model complexity and captures data
likelihood. Efficacy demonstration and comparative evaluation of the proposed
algorithm are performed using time series data simulated from systems that
exhibit nonlinear dynamics. We discuss results that show that the proposed
hierarchical SDF algorithm can identify underlying features with significantly
high degree of accuracy, even under very noisy conditions. Algorithm is
demonstrated to perform better than the baseline Hierarchical Dirichlet
Process-Hidden Markov Models (HDP-HMM). The low computational complexity of
algorithm makes it suitable for on-board, real time operations.
Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming
Authors: Andrew Sohn, Randal S. Olson, Jason H. Moore
Date: 2017-02-06 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

Machine learning has been gaining traction in recent years to meet the demand
for tools that can efficiently analyze and make sense of the ever-growing
databases of biomedical data in health care systems around the world. However,
effectively using machine learning methods requires considerable domain
expertise, which can be a barrier of entry for bioinformaticians new to
computational data science methods. Therefore, off-the-shelf tools that make
machine learning more accessible can prove invaluable for bioinformaticians. To
this end, we have developed an open source pipeline optimization tool
(TPOT-MDR) that uses genetic programming to automatically design machine
learning pipelines for bioinformatics studies. In TPOT-MDR, we implement
Multifactor Dimensionality Reduction (MDR) as a feature construction method for
modeling higher-order feature interactions, and combine it with a new expert
knowledge-guided feature selector for large biomedical data sets. We
demonstrate TPOT-MDR's capabilities using a combination of simulated and real
world data sets from human genetics and find that TPOT-MDR significantly
outperforms modern machine learning methods such as logistic regression and
eXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline
discovered by TPOT-MDR for a real world problem and highlight TPOT-MDR's
ability to produce a high-accuracy solution that is also easily interpretable.
Clearing the Skies: A deep network architecture for single-image rain removal
Authors: Xueyang Fu, Jia-Bin Huang, Xinghao Ding, Yinghao Liao, John Paisley
Date: 2016-09-07 00:00:00
Tasks: Image Enhancement, Rain Removal, Single Image Deraining

We introduce a deep network architecture called DerainNet for removing rain
streaks from an image. Based on the deep convolutional neural network (CNN), we
directly learn the mapping relationship between rainy and clean image detail
layers from data. Because we do not possess the ground truth corresponding to
real-world rainy images, we synthesize images with rain for training. In
contrast to other common strategies that increase depth or breadth of the
network, we use image processing domain knowledge to modify the objective
function and improve deraining with a modestly-sized CNN. Specifically, we
train our DerainNet on the detail (high-pass) layer rather than in the image
domain. Though DerainNet is trained on synthetic data, we find that the learned
network translates very effectively to real-world images for testing. Moreover,
we augment the CNN framework with image enhancement to improve the visual
results. Compared with state-of-the-art single image de-raining methods, our
method has improved rain removal and much faster computation time after network
training.
Neural Photo Editing with Introspective Adversarial Networks
Authors: Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston
Date: 2016-09-22 00:00:00
Tasks: Image Generation

The increasingly photorealistic sample quality of generative image models
suggests their feasibility in applications beyond image generation. We present
the Neural Photo Editor, an interface that leverages the power of generative
neural networks to make large, semantically coherent changes to existing
images. To tackle the challenge of achieving accurate reconstructions without
loss of feature quality, we introduce the Introspective Adversarial Network, a
novel hybridization of the VAE and GAN. Our model efficiently captures
long-range dependencies through use of a computational block based on
weight-shared dilated convolutions, and improves generalization performance
with Orthogonal Regularization, a novel weight regularization method. We
validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples
and reconstructions with high visual fidelity.
A Deep Convolutional Neural Network for Background Subtraction
Authors: Mohammadreza Babaee, Duc Tung Dinh, Gerhard Rigoll
Date: 2017-02-06 00:00:00
Tasks: Change Detection, Feature Engineering

In this work, we present a novel background subtraction system that uses a
deep Convolutional Neural Network (CNN) to perform the segmentation. With this
approach, feature engineering and parameter tuning become unnecessary since the
network parameters can be learned from data by training a single CNN that can
handle various video scenes. Additionally, we propose a new approach to
estimate background model from video. For the training of the CNN, we employed
randomly 5 percent video frames and their ground truth segmentations taken from
the Change Detection challenge 2014(CDnet 2014). We also utilized
spatial-median filtering as the post-processing of the network outputs. Our
method is evaluated with different data-sets, and the network outperforms the
existing algorithms with respect to the average ranking over different
evaluation metrics. Furthermore, due to the network architecture, our CNN is
capable of real time processing.
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
Authors: Afshin Dehghan, Syed Zain Masood, Guang Shu, Enrique. G. Ortiz
Date: 2017-02-06 00:00:00

This paper describes the details of Sighthound's fully automated vehicle
make, model and color recognition system. The backbone of our system is a deep
convolutional neural network that is not only computationally inexpensive, but
also provides state-of-the-art results on several competitive benchmarks.
Additionally, our deep network is trained on a large dataset of several million
images which are labeled through a semi-automated process. Finally we test our
system on several public datasets as well as our own internal test dataset. Our
results show that we outperform other methods on all benchmarks by significant
margins. Our model is available to developers through the Sighthound Cloud API
at https://www.sighthound.com/products/cloud
Search Intelligence: Deep Learning For Dominant Category Prediction
Authors: Zeeshan Khawar Malik, Mo Kobrosli, Peter Maas
Date: 2017-02-06 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification, Prediction, speech-recognition, Speech Recognition

Deep Neural Networks, and specifically fully-connected convolutional neural
networks are achieving remarkable results across a wide variety of domains.
They have been trained to achieve state-of-the-art performance when applied to
problems such as speech recognition, image classification, natural language
processing and bioinformatics. Most of these deep learning models when applied
to classification employ the softmax activation function for prediction and aim
to minimize cross-entropy loss. In this paper, we have proposed a supervised
model for dominant category prediction to improve search recall across all eBay
classifieds platforms. The dominant category label for each query in the last
90 days is first calculated by summing the total number of collaborative clicks
among all categories. The category having the highest number of collaborative
clicks for the given query will be considered its dominant category. Second,
each query is transformed to a numeric vector by mapping each unique word in
the query document to a unique integer value; all padded to equal length based
on the maximum document length within the pre-defined vocabulary size. A
fully-connected deep convolutional neural network (CNN) is then applied for
classification. The proposed model achieves very high classification accuracy
compared to other state-of-the-art machine learning techniques.