pwc-archive/papers-with-abstracts huggingface dataset:
Hand Gesture Real Time Paint Tool - Box
Authors: Vandit Gajjar, Viraj Mavani, Ayesha Gurnani
Date: 2017-09-03 00:00:00
Tasks: BIG-bench Machine Learning

With current development universally in computing, now a days user
interaction approaches with mouse, keyboard, touch-pens etc. are not
sufficient. Directly using of hands or hand gestures as an input device is a
method to attract people with providing the applications, through Machine
Learning and Computer Vision. Human-computer interaction application in which
you can simply draw different shapes, fill the colors, moving the folder from
one place to another place and rotating your image with rotating your hand
gesture all this will be without touching your device only. In this paper
Machine Learning based hand gestures recognition is presented, with the use of
Computer Vision different types of gesture applications have been created.
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification
Authors: Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai Huang, Ji Liu
Date: 2018-03-18 00:00:00
Tasks: EEG, Electroencephalogram (EEG), feature selection, General Classification, Outlier Detection, Seizure Detection, text-classification, Text Classification

The positive-unlabeled (PU) classification is a common scenario in real-world
applications such as healthcare, text classification, and bioinformatics, in
which we only observe a few samples labeled as "positive" together with a large
volume of "unlabeled" samples that may contain both positive and negative
samples. Building robust classifier for the PU problem is very challenging,
especially for complex data where the negative samples overwhelm and mislabeled
samples or corrupted features exist. To address these three issues, we propose
a robust learning framework that unifies AUC maximization (a robust metric for
biased labels), outlier detection (for excluding wrong labels), and feature
selection (for excluding corrupted features). The generalization error bounds
are provided for the proposed model that give valuable insight into the
theoretical performance of the method and lead to useful practical guidance,
e.g., to train a model, we find that the included unlabeled samples are
sufficient as long as the sample size is comparable to the number of positive
samples in the training process. Empirical comparisons and two real-world
applications on surgical site infection (SSI) and EEG seizure detection are
also conducted to show the effectiveness of the proposed model.
Angle-Based Joint and Individual Variation Explained
Authors: Qing Feng, Meilei Jiang, Jan Hannig, J. S. Marron
Date: 2017-04-07 00:00:00

Integrative analysis of disparate data blocks measured on a common set of
experimental subjects is a major challenge in modern data analysis. This data
structure naturally motivates the simultaneous exploration of the joint and
individual variation within each data block resulting in new insights. For
instance, there is a strong desire to integrate the multiple genomic data sets
in The Cancer Genome Atlas to characterize the common and also the unique
aspects of cancer genetics and cell biology for each source. In this paper we
introduce Angle-Based Joint and Individual Variation Explained capturing both
joint and individual variation within each data block. This is a major
improvement over earlier approaches to this challenge in terms of a new
conceptual understanding, much better adaption to data heterogeneity and a fast
linear algebra computation. Important mathematical contributions are the use of
score subspaces as the principal descriptors of variation structure and the use
of perturbation theory as the guide for variation segmentation. This leads to
an exploratory data analysis method which is insensitive to the heterogeneity
among data blocks and does not require separate normalization. An application
to cancer data reveals different behaviors of each type of signal in
characterizing tumor subtypes. An application to a mortality data set reveals
interesting historical lessons. Software and data are available at GitHub
<https://github.com/MeileiJiang/AJIVE_Project>.
Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network
Authors: Tao Hu, Honggang Qi, Jizheng Xu, Qingming Huang
Date: 2018-03-18 00:00:00
Tasks: Face Alignment, regression

Cascaded Regression (CR) based methods have been proposed to solve facial
landmarks detection problem, which learn a series of descent directions by
multiple cascaded regressors separately trained in coarse and fine stages. They
outperform the traditional gradient descent based methods in both accuracy and
running speed. However, cascaded regression is not robust enough because each
regressor's training data comes from the output of previous regressor.
Moreover, training multiple regressors requires lots of computing resources,
especially for deep learning based methods. In this paper, we develop a
Self-Iterative Regression (SIR) framework to improve the model efficiency. Only
one self-iterative regressor is trained to learn the descent directions for
samples from coarse stages to fine stages, and parameters are iteratively
updated by the same regressor. Specifically, we proposed Landmarks-Attention
Network (LAN) as our regressor, which concurrently learns features around each
landmark and obtains the holistic location increment. By doing so, not only the
rest of regressors are removed to simplify the training process, but the number
of model parameters is significantly decreased. The experiments demonstrate
that with only 3.72M model parameters, our proposed method achieves the
state-of-the-art performance.
Early hospital mortality prediction using vital signals
Authors: Reza Sadeghi, Tanvi Banerjee, William Romine
Date: 2018-03-18 00:00:00
Tasks: ICU Admission, Mortality Prediction, Prediction

Early hospital mortality prediction is critical as intensivists strive to
make efficient medical decisions about the severely ill patients staying in
intensive care units. As a result, various methods have been developed to
address this problem based on clinical records. However, some of the laboratory
test results are time-consuming and need to be processed. In this paper, we
propose a novel method to predict mortality using features extracted from the
heart signals of patients within the first hour of ICU admission. In order to
predict the risk, quantitative features have been computed based on the heart
rate signals of ICU patients. Each signal is described in terms of 12
statistical and signal-based features. The extracted features are fed into
eight classifiers: decision tree, linear discriminant, logistic regression,
support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and
K-nearest neighborhood (K-NN). To derive insight into the performance of the
proposed method, several experiments have been conducted using the well-known
clinical dataset named Medical Information Mart for Intensive Care III
(MIMIC-III). The experimental results demonstrate the capability of the
proposed method in terms of precision, recall, F1-score, and area under the
receiver operating characteristic curve (AUC). The decision tree classifier
satisfies both accuracy and interpretability better than the other classifiers,
producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It
indicates that heart rate signals can be used for predicting mortality in
patients in the ICU, achieving a comparable performance with existing
predictions that rely on high dimensional features from clinical records which
need to be processed and may contain missing information.
Regularization and feature selection for large dimensional data
Authors: Nand Sharma, Prathamesh Verlekar, Rehab Ashary, Sui Zhiquan
Date: 2017-12-06 00:00:00
Tasks: feature selection, General Classification, regression, text-classification, Text Classification

Feature selection has evolved to be an important step in several machine
learning paradigms. In domains like bio-informatics and text classification
which involve data of high dimensions, feature selection can help in
drastically reducing the feature space. In cases where it is difficult or
infeasible to obtain sufficient number of training examples, feature selection
helps overcome the curse of dimensionality which in turn helps improve
performance of the classification algorithm. The focus of our research here are
five embedded feature selection methods which use either the ridge regression,
or Lasso regression, or a combination of the two in the regularization part of
the optimization function. We evaluate five chosen methods on five large
dimensional datasets and compare them on the parameters of sparsity and
correlation in the datasets and their execution times.
Structural query-by-committee
Authors: Christopher Tosh, Sanjoy Dasgupta
Date: 2018-03-17 00:00:00
Tasks: Active Learning

In this work, we describe a framework that unifies many different interactive
learning tasks. We present a generalization of the {\it query-by-committee}
active learning algorithm for this setting, and we study its consistency and
rate of convergence, both theoretically and empirically, with and without
noise.
S-Isomap++: Multi Manifold Learning from Streaming Data
Authors: Suchismit Mahapatra, Varun Chandola
Date: 2017-10-17 00:00:00
Tasks: Dimensionality Reduction

Manifold learning based methods have been widely used for non-linear
dimensionality reduction (NLDR). However, in many practical settings, the need
to process streaming data is a challenge for such methods, owing to the high
computational complexity involved. Moreover, most methods operate under the
assumption that the input data is sampled from a single manifold, embedded in a
high dimensional space. We propose a method for streaming NLDR when the
observed data is either sampled from multiple manifolds or irregularly sampled
from a single manifold. We show that existing NLDR methods, such as Isomap,
fail in such situations, primarily because they rely on smoothness and
continuity of the underlying manifold, which is violated in the scenarios
explored in this paper. However, the proposed algorithm is able to learn
effectively in presence of multiple, and potentially intersecting, manifolds,
while allowing for the input data to arrive as a massive stream.
Learning Long Term Dependencies via Fourier Recurrent Units
Authors: Jiong Zhang, Yibo Lin, Zhao Song, Inderjit S. Dhillon
Date: 2018-03-17 00:00:00
Proceeding: ICML 2018 7

It is a known fact that training recurrent neural networks for tasks that
have long term dependencies is challenging. One of the main reasons is the
vanishing or exploding gradient problem, which prevents gradient information
from propagating to early layers. In this paper we propose a simple recurrent
architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients
that arise in its training while giving us stronger expressive power.
Specifically, FRU summarizes the hidden states $h^{(t)}$ along the temporal
dimension with Fourier basis functions. This allows gradients to easily reach
any layer due to FRU's residual learning structure and the global support of
trigonometric functions. We show that FRU has gradient lower and upper bounds
independent of temporal dimension. We also show the strong expressivity of
sparse Fourier basis, from which FRU obtains its strong expressive power. Our
experimental study also demonstrates that with fewer parameters the proposed
architecture outperforms other recurrent architectures on many tasks.
Generative Bridging Network in Neural Sequence Prediction
Authors: Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou
Date: 2017-06-28 00:00:00
Tasks: Abstractive Text Summarization, Language Modeling, Language Modelling, Machine Translation, Prediction, Text Summarization, Translation

In order to alleviate data sparsity and overfitting problems in maximum
likelihood estimation (MLE) for sequence prediction tasks, we propose the
Generative Bridging Network (GBN), in which a novel bridge module is introduced
to assist the training of the sequence prediction model (the generator
network). Unlike MLE directly maximizing the conditional likelihood, the bridge
extends the point-wise ground truth to a bridge distribution conditioned on it,
and the generator is optimized to minimize their KL-divergence. Three different
GBNs, namely uniform GBN, language-model GBN and coaching GBN, are proposed to
penalize confidence, enhance language smoothness and relieve learning burden.
Experiments conducted on two recognized sequence prediction tasks (machine
translation and abstractive text summarization) show that our proposed GBNs can
yield significant improvements over strong baselines. Furthermore, by analyzing
samples drawn from different bridges, expected influences on the generator are
verified.
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents
Authors: Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni
Date: 2018-03-17 00:00:00
Tasks: Anomaly Detection

This paper focuses on multi-sensor anomaly detection for moving cognitive
agents using both external and private first-person visual observations. Both
observation types are used to characterize agents' motion in a given
environment. The proposed method generates locally uniform motion models by
dividing a Gaussian process that approximates agents' displacements on the
scene and provides a Shared Level (SL) self-awareness based on Environment
Centered (EC) models. Such models are then used to train in a semi-unsupervised
way a set of Generative Adversarial Networks (GANs) that produce an estimation
of external and internal parameters of moving agents. Obtained results
exemplify the feasibility of using multi-perspective data for predicting and
analyzing trajectory information.
Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions
Authors: Yining Wang
Date: 2017-10-30 00:00:00
Tasks: Topic Models

In this paper we study the frequentist convergence rate for the Latent
Dirichlet Allocation (Blei et al., 2003) topic models. We show that the maximum
likelihood estimator converges to one of the finitely many equivalent
parameters in Wasserstein's distance metric at a rate of $n^{-1/4}$ without
assuming separability or non-degeneracy of the underlying topics and/or the
existence of more than three words per document, thus generalizing the previous
works of Anandkumar et al. (2012, 2014) from an information-theoretical
perspective. We also show that the $n^{-1/4}$ convergence rate is optimal in
the worst case.
Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS)
Authors: Chen Luo, Anshumali Shrivastava
Date: 2018-02-21 00:00:00

Split-Merge MCMC (Monte Carlo Markov Chain) is one of the essential and
popular variants of MCMC for problems when an MCMC state consists of an unknown
number of components. It is well known that state-of-the-art methods for
split-merge MCMC do not scale well. Strategies for rapid mixing requires smart
and informative proposals to reduce the rejection rate. However, all known
smart proposals involve expensive operations to suggest informative
transitions. As a result, the cost of each iteration is prohibitive for massive
scale datasets. It is further known that uninformative but computationally
efficient proposals, such as random split-merge, leads to extremely slow
convergence. This tradeoff between mixing time and per update cost seems hard
to get around.
  In this paper, we show a sweet spot. We leverage some unique properties of
weighted MinHash, which is a popular LSH, to design a novel class of
split-merge proposals which are significantly more informative than random
sampling but at the same time efficient to compute. Overall, we obtain a
superior tradeoff between convergence and per update cost. As a direct
consequence, our proposals are around 6X faster than the state-of-the-art
sampling methods on two large real datasets KDDCUP and PubMed with several
millions of entities and thousands of clusters.
A Dual Approach to Scalable Verification of Deep Networks
Authors: Krishnamurthy, Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, Pushmeet Kohli
Date: 2018-03-17 00:00:00
Tasks: valid

This paper addresses the problem of formally verifying desirable properties
of neural networks, i.e., obtaining provable guarantees that neural networks
satisfy specifications relating their inputs and outputs (robustness to bounded
norm adversarial perturbations, for example). Most previous work on this topic
was limited in its applicability by the size of the network, network
architecture and the complexity of properties to be verified. In contrast, our
framework applies to a general class of activation functions and specifications
on neural network inputs and outputs. We formulate verification as an
optimization problem (seeking to find the largest violation of the
specification) and solve a Lagrangian relaxation of the optimization problem to
obtain an upper bound on the worst case violation of the specification being
verified. Our approach is anytime i.e. it can be stopped at any time and a
valid bound on the maximum violation can be obtained. We develop specialized
verification algorithms with provable tightness guarantees under special
assumptions and demonstrate the practical significance of our general
verification approach on a variety of verification tasks.
Viewpoint: Artificial Intelligence and Labour
Authors: Spyridon Samothrakis
Date: 2018-03-17 00:00:00

The welfare of modern societies has been intrinsically linked to wage labour.
With some exceptions, the modern human has to sell her labour-power to be able
reproduce biologically and socially. Thus, a lingering fear of technological
unemployment features predominately as a theme among Artificial Intelligence
researchers. In this short paper we show that, if past trends are anything to
go by, this fear is irrational. On the contrary, we argue that the main problem
humanity will be facing is the normalisation of extremely long working hours.
Finite Horizon Throughput Maximization and Sensing Optimization in Wireless Powered Devices over Fading Channels
Authors: Mehdi Salehi Heydar Abad, Ozgur Ercetin
Date: 2018-03-17 00:00:00
Tasks: Bayesian Inference, Reinforcement Learning

Wireless power transfer (WPT) is a promising technology that provides the
network a way to replenish the batteries of the remote devices by utilizing RF
transmissions. We study a class of harvest-first-transmit-later type of WPT
policy, where an access point (AP) first employs RF power transfer to recharge
a wireless powered device (WPD) for a certain period subjected to optimization,
and then, the harvested energy is subsequently used by the WPD to transmit its
data bits back to the AP over a finite horizon. A significant challenge
regarding the studied WPT scenario is the time-varying nature of the wireless
channel linking the WPD to the AP. We first investigate as a benchmark the
offline case where the channel realizations are known non-causally prior to the
starting of the horizon. For the offline case, by finding the optimal WPT
duration and power allocations in the data transmission period, we derive an
upper bound on the throughput of the WPD. We then focus on the online
counterpart of the problem where the channel realizations are known causally.
We prove that the optimal WPT duration obeys a time-dependent threshold form
depending on the energy state of the WPD. In the subsequent data transmission
stage, the optimal transmit power allocation for the WPD is shown to be of a
fractional structure where at each time slot a fraction of energy depending on
the current channel and a measure of future channel state expectations is
allocated for data transmission. We numerically show that the online policy
performs almost identical to the upper bound. We then consider a data sensing
application, where the WPD adjusts the sensing resolution to balance between
the quality of the sensed data and the probability of successfully delivering
it. We use Bayesian inference as a reinforcement learning method to provide a
mean for the WPD in learning to balance the sensing resolution.
Tell Me Why Is It So? Explaining Knowledge Graph Relationships by Finding Descriptive Support Passages
Authors: Sumit Bhatia, Purusharth Dwivedi, Avneet Kaur
Date: 2018-03-17 00:00:00
Tasks: Decision Making, Descriptive, Information Retrieval, Retrieval

We address the problem of finding descriptive explanations of facts stored in
a knowledge graph. This is important in high-risk domains such as healthcare,
intelligence, etc. where users need additional information for decision making
and is especially crucial for applications that rely on automatically
constructed knowledge bases where machine learned systems extract facts from an
input corpus and working of the extractors is opaque to the end-user. We follow
an approach inspired from information retrieval and propose a simple and
efficient, yet effective solution that takes into account passage level as well
as document level properties to produce a ranked list of passages describing a
given input relation. We test our approach using Wikidata as the knowledge base
and Wikipedia as the source corpus and report results of user studies conducted
to study the effectiveness of our proposed model.
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection
Authors: Pan Wei, John E. Ball, Derek T. Anderson
Date: 2018-03-17 00:00:00
Tasks: Image Augmentation, Object, object-detection, Object Detection, Position, Robust Object Detection

A significant challenge in object detection is accurate identification of an
object's position in image space, whereas one algorithm with one set of
parameters is usually not enough, and the fusion of multiple algorithms and/or
parameters can lead to more robust results. Herein, a new computational
intelligence fusion approach based on the dynamic analysis of agreement among
object detection outputs is proposed. Furthermore, we propose an online versus
just in training image augmentation strategy. Experiments comparing the results
both with and without fusion are presented. We demonstrate that the augmented
and fused combination results are the best, with respect to higher accuracy
rates and reduction of outlier influences. The approach is demonstrated in the
context of cone, pedestrian and box detection for Advanced Driver Assistance
Systems (ADAS) applications.
Adaptive strategy for superpixel-based region-growing image segmentation
Authors: Mahaman Sani Chaibou, Pierre-Henri Conze, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub
Date: 2018-03-17 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels

This work presents a region-growing image segmentation approach based on
superpixel decomposition. From an initial contour-constrained over-segmentation
of the input image, the image segmentation is achieved by iteratively merging
similar superpixels into regions. This approach raises two key issues: (1) how
to compute the similarity between superpixels in order to perform accurate
merging and (2) in which order those superpixels must be merged together. In
this perspective, we firstly introduce a robust adaptive multi-scale superpixel
similarity in which region comparisons are made both at content and common
border level. Secondly, we propose a global merging strategy to efficiently
guide the region merging process. Such strategy uses an adpative merging
criterion to ensure that best region aggregations are given highest priorities.
This allows to reach a final segmentation into consistent regions with strong
boundary adherence. We perform experiments on the BSDS500 image dataset to
highlight to which extent our method compares favorably against other
well-known image segmentation algorithms. The obtained results demonstrate the
promising potential of the proposed approach.