pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Sensor Periocular Biometrics in a Global Pandemic: Comparative Benchmark and Novel Multialgorithmic Approach
Authors: Fernando Alonso-Fernandez, Kiran B. Raja, R. Raghavendra, Cristoph Busch, Josef Bigun, Ruben Vera-Rodriguez, Julian Fierrez
Date: 2019-02-21 00:00:00
Tasks: Decision Making, Face Recognition, Person Recognition

The massive availability of cameras results in a wide variability of imaging conditions, producing large intra-class variations and a significant performance drop if heterogeneous images are compared for person recognition. However, as biometrics is deployed, it is common to replace damaged or obsolete hardware, or to exchange information between heterogeneous applications. Variations in spectral bands can also occur. For example, surveillance face images (typically acquired in the visible spectrum, VIS) may need to be compared against a legacy iris database (typically acquired in near-infrared, NIR). Here, we propose a multialgorithmic approach to cope with periocular images from different sensors. With face masks in the front line against COVID-19, periocular recognition is regaining popularity since it is the only face region that remains visible. We integrate different comparators with a fusion scheme based on linear logistic regression, in which scores are represented by log-likelihood ratios. This allows easy interpretation of scores and the use of Bayes thresholds for optimal decision-making since scores from different comparators are in the same probabilistic range. We evaluate our approach in the context of the Cross-Eyed Competition, whose aim was to compare recognition approaches when NIR and VIS periocular images are matched. Our approach achieves EER=0.2% and FRR of just 0.47% at FAR=0.01%, representing the best overall approach of the competition. Experiments are also reported with a database of VIS images from different smartphones. We also discuss the impact of template size and computation times, with the most computationally heavy comparator playing an important role in the results. Lastly, the proposed method is shown to outperform other popular fusion approaches, such as the average of scores, SVMs or Random Forest.
Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation
Authors: Qikui Zhu, Bo Du, Pingkun Yan
Date: 2019-02-21 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images still faces several challenges. For instance, a lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. With deep learning, especially convolutional neural networks (CNNs), emerging as commonly used methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced that ever before. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss (BWL) is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on the publicly available MICCAI 2012 Prostate MR Image Segmentation (PROMISE12) challenge dataset. Our experimental results demonstrate that the proposed model is more sensitive to boundary information and outperformed other state-of-the-art methods.
Deep CNN-based Speech Balloon Detection and Segmentation for Comic Books
Authors: David Dubray, Jochen Laubrock
Date: 2019-02-21 00:00:00

We develop a method for the automated detection and segmentation of speech
balloons in comic books, including their carrier and tails. Our method is based
on a deep convolutional neural network that was trained on annotated pages of
the Graphic Narrative Corpus. More precisely, we are using a fully
convolutional network approach inspired by the U-Net architecture, combined
with a VGG-16 based encoder. The trained model delivers state-of-the-art
performance with an F1-score of over 0.94. Qualitative results suggest that
wiggly tails, curved corners, and even illusory contours do not pose a major
problem. Furthermore, the model has learned to distinguish speech balloons from
captions. We compare our model to earlier results and discuss some possible
applications.
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering
Authors: Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh
Date: 2019-02-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: counterfactual, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.
Cloud-Based Autonomous Indoor Navigation: A Case Study
Authors: Uthman Baroudi, M. Alharbi, K. Alhouty, H. Baafeef, K. Alofi
Date: 2019-02-21 00:00:00

In this case study, we design, integrate and implement a cloud-enabled
autonomous robotic navigation system. The system has the following features:
map generation and robot coordination via cloud service and video streaming to
allow online monitoring and control in case of emergency. The system has been
tested to generate a map for a long corridor using two modes: manual and
autonomous. The autonomous mode has shown more accurate map. In addition, the
field experiments confirm the benefit of offloading the heavy computation to
the cloud by significantly shortening the time required to build the map.
Domain Partitioning Network
Authors: Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr
Date: 2019-02-21 00:00:00

Standard adversarial training involves two agents, namely a generator and a
discriminator, playing a mini-max game. However, even if the players converge
to an equilibrium, the generator may only recover a part of the target data
distribution, in a situation commonly referred to as mode collapse. In this
work, we present the Domain Partitioning Network (DoPaNet), a new approach to
deal with mode collapse in generative adversarial learning. We employ multiple
discriminators, each encouraging the generator to cover a different part of the
target distribution. To ensure these parts do not overlap and collapse into the
same mode, we add a classifier as a third agent in the game. The classifier
decides which discriminator the generator is trained against for each sample.
Through experiments on toy examples and real images, we show the merits of
DoPaNet in covering the real distribution and its superiority with respect to
the competing methods. Besides, we also show that we can control the modes from
which samples are generated using DoPaNet.
Correspondence Analysis Using Neural Networks
Authors: Hsiang Hsu, Salman Salamatian, Flavio P. Calmon
Date: 2019-02-21 00:00:00
Tasks: Epidemiology

Correspondence analysis (CA) is a multivariate statistical tool used to
visualize and interpret data dependencies. CA has found applications in fields
ranging from epidemiology to social sciences. However, current methods used to
perform CA do not scale to large, high-dimensional datasets. By re-interpreting
the objective in CA using an information-theoretic tool called the principal
inertia components, we demonstrate that performing CA is equivalent to solving
a functional optimization problem over the space of finite variance functions
of two random variable. We show that this optimization problem, in turn, can be
efficiently approximated by neural networks. The resulting formulation, called
the correspondence analysis neural network (CA-NN), enables CA to be performed
at an unprecedented scale. We validate the CA-NN on synthetic data, and
demonstrate how it can be used to perform CA on a variety of datasets,
including food recipes, wine compositions, and images. Our results outperform
traditional methods used in CA, indicating that CA-NN can serve as a new,
scalable tool for interpretability and visualization of complex dependencies
between random variables.
Stable Bayesian Optimisation via Direct Stability Quantification
Authors: Alistair Shilton, Sunil Gupta, Santu Rana, Svetha Venkatesh, Majid Abdolshah, Dang Nguyen
Date: 2019-02-21 00:00:00
Tasks: Bayesian Optimisation

In this paper we consider the problem of finding stable maxima of expensive
(to evaluate) functions. We are motivated by the optimisation of physical and
industrial processes where, for some input ranges, small and unavoidable
variations in inputs lead to unacceptably large variation in outputs. Our
approach uses multiple gradient Gaussian Process models to estimate the
probability that worst-case output variation for specified input perturbation
exceeded the desired maxima, and these probabilities are then used to (a) guide
the optimisation process toward solutions satisfying our stability criteria and
(b) post-filter results to find the best stable solution. We exhibit our
algorithm on synthetic and real-world problems and demonstrate that it is able
to effectively find stable maxima.
Stacking with Neural network for Cryptocurrency investment
Authors: Avinash Barnwal, Hari Pad Bharti, Aasim Ali, Vishal Singh
Date: 2019-02-21 00:00:00
Tasks: Feature Importance, Sentiment Analysis

Predicting the direction of assets have been an active area of study and a
difficult task. Machine learning models have been used to build robust models
to model the above task. Ensemble methods is one of them showing results better
than a single supervised method. In this paper, we have used generative and
discriminative classifiers to create the stack, particularly 3 generative and 6
discriminative classifiers and optimized over one-layer Neural Network to model
the direction of price cryptocurrencies. Features used are technical indicators
used are not limited to trend, momentum, volume, volatility indicators, and
sentiment analysis has also been used to gain useful insight combined with the
above features. For Cross-validation, Purged Walk forward cross-validation has
been used. In terms of accuracy, we have done a comparative analysis of the
performance of Ensemble method with Stacking and Ensemble method with blending.
We have also developed a methodology for combined features importance for the
stacked model. Important indicators are also identified based on feature
importance.
Statistics and Samples in Distributional Reinforcement Learning
Authors: Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney
Date: 2019-02-21 00:00:00
Tasks: Distributional Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a unifying framework for designing and analysing distributional
reinforcement learning (DRL) algorithms in terms of recursively estimating
statistics of the return distribution. Our key insight is that DRL algorithms
can be decomposed as the combination of some statistical estimator and a method
for imputing a return distribution consistent with that set of statistics. With
this new understanding, we are able to provide improved analyses of existing
DRL algorithms as well as construct a new algorithm (EDRL) based upon
estimation of the expectiles of the return distribution. We compare EDRL with
existing methods on a variety of MDPs to illustrate concrete aspects of our
analysis, and develop a deep RL variant of the algorithm, ER-DQN, which we
evaluate on the Atari-57 suite of games.
A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks
Authors: Cassandra Burdziak, Elham Azizi, Sandhya Prabhakaran, Dana Pe'er
Date: 2019-02-21 00:00:00
Tasks: MULTI-VIEW LEARNING

We present a Bayesian hierarchical multi-view mixture model termed Symphony
that simultaneously learns clusters of cells representing cell types and their
underlying gene regulatory networks by integrating data from two views:
single-cell gene expression data and paired epigenetic data, which is
informative of gene-gene interactions. This model improves interpretation of
clusters as cell types with similar expression patterns as well as regulatory
networks driving expression, by explaining gene-gene covariances with the
biological machinery regulating gene expression. We show the theoretical
advantages of the multi-view learning approach and present a Variational EM
inference procedure. We demonstrate superior performance on both synthetic data
and real genomic data with subtypes of peripheral blood cells compared to other
methods.
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Authors: Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama
Date: 2019-02-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Instruction Following, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is a promising framework for solving control problems,
but its use in practical situations is hampered by the fact that reward
functions are often difficult to engineer. Specifying goals and tasks for
autonomous machines, such as robots, is a significant challenge:
conventionally, reward functions and goal states have been used to communicate
objectives. But people can communicate objectives to each other simply by
describing or demonstrating them. How can we build learning algorithms that
will allow us to tell machines what we want them to do? In this work, we
investigate the problem of grounding language commands as reward functions
using inverse reinforcement learning, and argue that language-conditioned
rewards are more transferable than language-conditioned policies to new
environments. We propose language-conditioned reward learning (LC-RL), which
grounds language commands as a reward function represented by a deep neural
network. We demonstrate that our model learns rewards that transfer to novel
tasks and environments on realistic, high-dimensional visual environments with
natural language commands, whereas directly learning a language-conditioned
policy leads to poor performance.
AdaLinUCB: Opportunistic Learning for Contextual Bandits
Authors: Xueying Guo, Xiaoxiao Wang, Xin Liu
Date: 2019-02-20 00:00:00
Tasks: Multi-Armed Bandits

In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations. When the exploration cost is low, so is the actual regret of pulling a sub-optimal arm (e.g., trying a suboptimal recommendation). Therefore, intuitively, we could explore more when the exploration cost is relatively low and exploit more when the exploration cost is relatively high. Inspired by this intuition, for opportunistic contextual bandits with Linear payoffs, we propose an Adaptive Upper-Confidence-Bound algorithm (AdaLinUCB) to adaptively balance the exploration-exploitation trade-off for opportunistic learning. We prove that AdaLinUCB achieves O((log T)^2) problem-dependent regret upper bound, which has a smaller coefficient than that of the traditional LinUCB algorithm. Moreover, based on both synthetic and real-world dataset, we show that AdaLinUCB significantly outperforms other contextual bandit algorithms, under large exploration cost fluctuations.
Stable and Fair Classification
Authors: Lingxiao Huang, Nisheeth K. Vishnoi
Date: 2019-02-21 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification

Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedler et al. observed that fair classification algorithms may not be stable with respect to variations in the training dataset -- a crucial consideration in several real-world applications. Motivated by their work, we study the problem of designing classification algorithms that are both fair and stable. We propose an extended framework based on fair classification algorithms that are formulated as optimization problems, by introducing a stability-focused regularization term. Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework. Our accuracy guarantee can be used to inform the selection of the regularization parameter in our framework. To the best of our knowledge, this is the first work that combines stability and fairness in automated decision-making tasks. We assess the benefits of our approach empirically by extending several fair classification algorithms that are shown to achieve the best balance between fairness and accuracy over the Adult dataset. Our empirical results show that our framework indeed improves the stability at only a slight sacrifice in accuracy.
Certainty Equivalence is Efficient for Linear Quadratic Control
Authors: Horia Mania, Stephen Tu, Benjamin Recht
Date: 2019-02-21 00:00:00
Proceeding: NeurIPS 2019 12

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ) control problems with unknown transition dynamics. We show that for both the fully and partially observed settings, the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system and the cost incurred by using the optimal LQ controller enjoys a fast statistical rate, scaling as the square of the parameter error. To the best of our knowledge, our result is the first sub-optimality guarantee in the partially observed Linear Quadratic Gaussian (LQG) setting. Furthermore, in the fully observed Linear Quadratic Regulator (LQR), our result improves upon recent work by Dean et al. (2017), who present an algorithm achieving a sub-optimality gap linear in the parameter error. A key part of our analysis relies on perturbation bounds for discrete Riccati equations. We provide two new perturbation bounds, one that expands on an existing result from Konstantinov et al. (1993), and another based on a new elementary proof strategy.
Inference of a Multi-Domain Machine Learning Model to Predict Mortality in Hospital Stays for Patients with Cancer upon Febrile Neutropenia Onset
Authors: Xinsong Du, Jae Min, Mattia Prosperi, Rohit Bishnoi, Dominick J. Lemas, Chintan P. Shah
Date: 2019-02-21 00:00:00
Tasks: BIG-bench Machine Learning, Mortality Prediction

Febrile neutropenia (FN) has been associated with high mortality, especially among adults with cancer. Understanding the patient and provider level heterogeneity in FN hospital admissions has potential to inform personalized interventions focused on increasing survival of individuals with FN. We leverage machine learning techniques to disentangling the complex interactions among multi domain risk factors in a population with FN. Data from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample and Nationwide Inpatient Sample (NIS) were used to build machine learning based models of mortality for adult cancer patients who were diagnosed with FN during a hospital admission. In particular, the importance of risk factors from different domains (including demographic, clinical, and hospital associated information) was studied. A set of more interpretable (decision tree, logistic regression) as well as more black box (random forest, gradient boosting, neural networks) models were analyzed and compared via multiple cross validation. Our results demonstrate that a linear prediction score of FN mortality among adults with cancer, based on admission information is effective in classifying high risk patients; clinical diagnoses is the domain with the highest predictive power. A number of the risk variables (e.g. sepsis, kidney failure, etc.) identified in this study are clinically actionable and may inform future studies looking at the patients prior medical history are warranted.
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
Authors: Shuochao Yao, Ailing Piao, Wenjun Jiang, Yiran Zhao, Huajie Shao, Shengzhong Liu, Dongxin Liu, Jinyang Li, Tianshi Wang, Shaohan Hu, Lu Su, Jiawei Han, Tarek Abdelzaher
Date: 2019-02-21 00:00:00
Tasks: speech-recognition, Speech Recognition

Recent advances in deep learning motivate the use of deep neural networks in
Internet-of-Things (IoT) applications. These networks are modelled after signal
processing in the human brain, thereby leading to significant advantages at
perceptual tasks such as vision and speech recognition. IoT applications,
however, often measure physical phenomena, where the underlying physics (such
as inertia, wireless signal propagation, or the natural frequency of
oscillation) are fundamentally a function of signal frequencies, offering
better features in the frequency domain. This observation leads to a
fundamental question: For IoT applications, can one develop a new brand of
neural network structures that synthesize features inspired not only by the
biology of human perception but also by the fundamental nature of physics?
Hence, in this paper, instead of using conventional building blocks (e.g.,
convolutional and recurrent layers), we propose a new foundational neural
network building block, the Short-Time Fourier Neural Network (STFNet). It
integrates a widely-used time-frequency analysis method, the Short-Time Fourier
Transform, into data processing to learn features directly in the frequency
domain, where the physics of underlying phenomena leave better foot-prints.
STFNets bring additional flexibility to time-frequency analysis by offering
novel nonlinear learnable operations that are spectral-compatible. Moreover,
STFNets show that transforming signals to a domain that is more connected to
the underlying physics greatly simplifies the learning process. We demonstrate
the effectiveness of STFNets with extensive experiments. STFNets significantly
outperform the state-of-the-art deep learning models in all experiments. A
STFNet, therefore, demonstrates superior capability as the fundamental building
block of deep neural networks for IoT applications for various sensor inputs.