Paper Group ANR 300
A Model-Based, Decision-Theoretic Perspective on Automated Cyber Response. Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Using Machine Learning to predict extreme events in the Hénon map. Wasserste …
A Model-Based, Decision-Theoretic Perspective on Automated Cyber Response
| Title | A Model-Based, Decision-Theoretic Perspective on Automated Cyber Response |
| Authors | Lashon B. Booker, Scott A. Musman |
| Abstract | Cyber-attacks can occur at machine speeds that are far too fast for human-in-the-loop (or sometimes on-the-loop) decision making to be a viable option. Although human inputs are still important, a defensive Artificial Intelligence (AI) system must have considerable autonomy in these circumstances. When the AI system is model-based, its behavior responses can be aligned with risk-aware cost/benefit tradeoffs that are defined by user-supplied preferences that capture the key aspects of how human operators understand the system, the adversary and the mission. This paper describes an approach to automated cyber response that is designed along these lines. We combine a simulation of the system to be defended with an anytime online planner to solve cyber defense problems characterized as partially observable Markov decision problems (POMDPs). |
| Tasks | Decision Making |
| Published | 2020-02-20 |
| URL | https://arxiv.org/abs/2002.08957v1 |
| https://arxiv.org/pdf/2002.08957v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-model-based-decision-theoretic-perspective |
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Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory
| Title | Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory |
| Authors | Berat Mert Albaba, Yildiray Yildiz |
| Abstract | In this paper, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The need for a modeling framework that can address multiple human-human and human-automation interactions, where all the agents can be modeled as decision makers simultaneously, is the main motivation behind this work. Such a modeling framework may be utilized for the validation and verification of autonomous vehicles: It is estimated that for an autonomous vehicle to reach the same safety level of cars with drivers, millions of miles of driving tests are required. The modeling framework presented in this paper may be used in a high-fidelity traffic simulator consisting of multiple human decision makers to reduce the time and effort spent for testing by allowing safe and quick assessment of self-driving algorithms. To demonstrate the fidelity of the proposed modeling framework, game theoretical driver models are compared with real human driver behavior patterns extracted from traffic data. |
| Tasks | Autonomous Vehicles |
| Published | 2020-03-24 |
| URL | https://arxiv.org/abs/2003.11071v1 |
| https://arxiv.org/pdf/2003.11071v1.pdf | |
| PWC | https://paperswithcode.com/paper/driver-modeling-through-deep-reinforcement |
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Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
| Title | Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue |
| Authors | Yijie Zhang, Kevin de Haan, Yair Rivenson, Jingxi Li, Apostolos Delis, Aydogan Ozcan |
| Abstract | Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson’s Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining methods. |
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| Published | 2020-01-20 |
| URL | https://arxiv.org/abs/2001.07267v1 |
| https://arxiv.org/pdf/2001.07267v1.pdf | |
| PWC | https://paperswithcode.com/paper/digital-synthesis-of-histological-stains |
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Using Machine Learning to predict extreme events in the Hénon map
| Title | Using Machine Learning to predict extreme events in the Hénon map |
| Authors | Martin Lellep, Jonathan Prexl, Moritz Linkmann, Bruno Eckhardt |
| Abstract | Machine Learning (ML) inspired algorithms provide a flexible set of tools for analyzing and forecasting chaotic dynamical systems. We here analyze the performance of one algorithm for the prediction of extreme events in the two-dimensional H'enon map at the classical parameters. The task is to determine whether a trajectory will exceed a threshold after a set number of time steps into the future. This task has a geometric interpretation within the dynamics of the H'enon map, which we use to gauge the performance of the neural networks that are used in this work. We analyze the dependence of the success rate of the ML models on the prediction time $T$ , the number of training samples $N_T$ and the size of the network $N_p$. We observe that in order to maintain a certain accuracy, $N_T \propto exp(2 h T)$ and $N_p \propto exp(hT)$, where $h$ is the topological entropy. Similar relations between the intrinsic chaotic properties of the dynamics and ML parameters might be observable in other systems as well. |
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| Published | 2020-02-20 |
| URL | https://arxiv.org/abs/2002.10268v1 |
| https://arxiv.org/pdf/2002.10268v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-machine-learning-to-predict-extreme |
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Wasserstein Control of Mirror Langevin Monte Carlo
| Title | Wasserstein Control of Mirror Langevin Monte Carlo |
| Authors | Kelvin Shuangjian Zhang, Gabriel Peyré, Jalal Fadili, Marcelo Pereyra |
| Abstract | Discretized Langevin diffusions are efficient Monte Carlo methods for sampling from high dimensional target densities that are log-Lipschitz-smooth and (strongly) log-concave. In particular, the Euclidean Langevin Monte Carlo sampling algorithm has received much attention lately, leading to a detailed understanding of its non-asymptotic convergence properties and of the role that smoothness and log-concavity play in the convergence rate. Distributions that do not possess these regularity properties can be addressed by considering a Riemannian Langevin diffusion with a metric capturing the local geometry of the log-density. However, the Monte Carlo algorithms derived from discretizations of such Riemannian Langevin diffusions are notoriously difficult to analyze. In this paper, we consider Langevin diffusions on a Hessian-type manifold and study a discretization that is closely related to the mirror-descent scheme. We establish for the first time a non-asymptotic upper-bound on the sampling error of the resulting Hessian Riemannian Langevin Monte Carlo algorithm. This bound is measured according to a Wasserstein distance induced by a Riemannian metric ground cost capturing the Hessian structure and closely related to a self-concordance-like condition. The upper-bound implies, for instance, that the iterates contract toward a Wasserstein ball around the target density whose radius is made explicit. Our theory recovers existing Euclidean results and can cope with a wide variety of Hessian metrics related to highly non-flat geometries. |
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| Published | 2020-02-11 |
| URL | https://arxiv.org/abs/2002.04363v1 |
| https://arxiv.org/pdf/2002.04363v1.pdf | |
| PWC | https://paperswithcode.com/paper/wasserstein-control-of-mirror-langevin-monte |
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Error-feedback Stochastic Configuration Strategy on Convolutional Neural Networks for Time Series Forecasting
| Title | Error-feedback Stochastic Configuration Strategy on Convolutional Neural Networks for Time Series Forecasting |
| Authors | Xinze Zhang, Kun He, Yukun Bao |
| Abstract | Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture as well as the tuning of the hyper-parameters. Inspired by the iterative construction strategy for building a random multilayer perceptron, we propose a novel Error-feedback Stochastic Configuration (ESC) strategy to construct a random Convolutional Neural Network (ESC-CNN) for time series forecasting task, which builds the network architecture adaptively. The ESC strategy suggests that random filters and neurons of the error-feedback fully connected layer are incrementally added in a manner that they can steadily compensate the prediction error during the construction process, and a filter selection strategy is introduced to secure that ESC-CNN holds the universal approximation property, providing helpful information at each iterative process for the prediction. The performance of ESC-CNN is justified on its prediction accuracy for one-step-ahead and multi-step-ahead forecasting tasks. Comprehensive experiments on a synthetic dataset and two real-world datasets show that the proposed ESC-CNN not only outperforms the state-of-art random neural networks, but also exhibits strong predictive power in comparison to trained Convolution Neural Networks and Long Short-Term Memory models, demonstrating the effectiveness of ESC-CNN in time series forecasting. |
| Tasks | Time Series, Time Series Forecasting |
| Published | 2020-02-03 |
| URL | https://arxiv.org/abs/2002.00717v1 |
| https://arxiv.org/pdf/2002.00717v1.pdf | |
| PWC | https://paperswithcode.com/paper/error-feedback-stochastic-configuration |
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Discriminative Viewer Identification using Generative Models of Eye Gaze
| Title | Discriminative Viewer Identification using Generative Models of Eye Gaze |
| Authors | Silvia Makowski, Lena A. Jäger, Lisa Schwetlick, Hans Trukenbrod, Ralf Engbert, Tobias Scheffer |
| Abstract | We study the problem of identifying viewers of arbitrary images based on their eye gaze. Psychological research has derived generative stochastic models of eye movements. In order to exploit this background knowledge within a discriminatively trained classification model, we derive Fisher kernels from different generative models of eye gaze. Experimentally, we find that the performance of the classifier strongly depends on the underlying generative model. Using an SVM with Fisher kernel improves the classification performance over the underlying generative model. |
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| Published | 2020-03-25 |
| URL | https://arxiv.org/abs/2003.11399v1 |
| https://arxiv.org/pdf/2003.11399v1.pdf | |
| PWC | https://paperswithcode.com/paper/discriminative-viewer-identification-using |
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Improving automated segmentation of radio shows with audio embeddings
| Title | Improving automated segmentation of radio shows with audio embeddings |
| Authors | Oberon Berlage, Klaus-Michael Lux, David Graus |
| Abstract | Audio features have been proven useful for increasing the performance of automated topic segmentation systems. This study explores the novel task of using audio embeddings for automated, topically coherent segmentation of radio shows. We created three different audio embedding generators using multi-class classification tasks on three datasets from different domains. We evaluate topic segmentation performance of the audio embeddings and compare it against a text-only baseline. We find that a set-up including audio embeddings generated through a non-speech sound event classification task significantly outperforms our text-only baseline by 32.3% in F1-measure. In addition, we find that different classification tasks yield audio embeddings that vary in segmentation performance. |
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| Published | 2020-02-12 |
| URL | https://arxiv.org/abs/2002.05194v1 |
| https://arxiv.org/pdf/2002.05194v1.pdf | |
| PWC | https://paperswithcode.com/paper/improving-automated-segmentation-of-radio |
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The empirical duality gap of constrained statistical learning
| Title | The empirical duality gap of constrained statistical learning |
| Authors | Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro |
| Abstract | This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information processing. Accounting for constraints, however, is paramount to incorporate prior knowledge and impose desired structural and statistical properties on the solutions. Still, solving constrained statistical problems remains challenging and guarantees scarce, leaving them to be tackled using regularized formulations. Though practical and effective, selecting regularization parameters so as to satisfy requirements is challenging, if at all possible, due to the lack of a straightforward relation between parameters and constraints. In this work, we propose to directly tackle the constrained statistical problem overcoming its infinite dimensionality, unknown distributions, and constraints by leveraging finite dimensional parameterizations, sample averages, and duality theory. Aside from making the problem tractable, these tools allow us to bound the empirical duality gap, i.e., the difference between our approximate tractable solutions and the actual solutions of the original statistical problem. We demonstrate the effectiveness and usefulness of this constrained formulation in a fair learning application. |
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| Published | 2020-02-12 |
| URL | https://arxiv.org/abs/2002.05183v1 |
| https://arxiv.org/pdf/2002.05183v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-empirical-duality-gap-of-constrained |
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Quality Control of Neuron Reconstruction Based on Deep Learning
| Title | Quality Control of Neuron Reconstruction Based on Deep Learning |
| Authors | Donghuan Lu, Sujun Zhao, Peng Xie, Kai Ma, Lijuan Liu, Yefeng Zheng |
| Abstract | Neuron reconstruction is essential to generate exquisite neuron connectivity map for understanding brain function. Despite the significant amount of effect that has been made on automatic reconstruction methods, manual tracing by well-trained human annotators is still necessary. To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper. By formulating the quality control problem into a binary classification task regarding each single point, the proposed approach overcomes the technical difficulties resulting from the large image size and complex neuron morphology. Not only it provides the evaluation of reconstruction quality, but also can locate exactly where the wrong tracing begins. This work presents one of the first comprehensive studies for whole-brain scale quality control of neuron reconstructions. Experiments on five-fold cross validation with a large dataset demonstrate that the proposed approach can detect 74.7% errors with only 1.4% false alerts. |
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| Published | 2020-03-19 |
| URL | https://arxiv.org/abs/2003.08556v1 |
| https://arxiv.org/pdf/2003.08556v1.pdf | |
| PWC | https://paperswithcode.com/paper/quality-control-of-neuron-reconstruction |
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Multi-modal Self-Supervision from Generalized Data Transformations
| Title | Multi-modal Self-Supervision from Generalized Data Transformations |
| Authors | Mandela Patrick, Yuki M. Asano, Ruth Fong, João F. Henriques, Geoffrey Zweig, Andrea Vedaldi |
| Abstract | Self-supervised learning has advanced rapidly, with several results beating supervised models for pre-training feature representations. While the focus of most of these works has been new loss functions or tasks, little attention has been given to the data transformations that build the foundation of learning representations with desirable invariances. In this work, we introduce a framework for multi-modal data transformations that preserve semantics and induce the learning of high-level representations across modalities. We do this by combining two steps: inter-modality slicing, and intra-modality augmentation. Using a contrastive loss as the training task, we show that choosing the right transformations is key and that our method yields state-of-the-art results on downstream video and audio classification tasks such as HMDB51, UCF101 and DCASE2014 with Kinetics-400 pretraining. |
| Tasks | Audio Classification |
| Published | 2020-03-09 |
| URL | https://arxiv.org/abs/2003.04298v1 |
| https://arxiv.org/pdf/2003.04298v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-modal-self-supervision-from-generalized |
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Unsupervised Separation of Native and Loanwords for Malayalam and Telugu
| Title | Unsupervised Separation of Native and Loanwords for Malayalam and Telugu |
| Authors | Sridhama Prakhya, Deepak P |
| Abstract | Quite often, words from one language are adopted within a different language without translation; these words appear in transliterated form in text written in the latter language. This phenomenon is particularly widespread within Indian languages where many words are loaned from English. In this paper, we address the task of identifying loanwords automatically and in an unsupervised manner, from large datasets of words from agglutinative Dravidian languages. We target two specific languages from the Dravidian family, viz., Malayalam and Telugu. Based on familiarity with the languages, we outline an observation that native words in both these languages tend to be characterized by a much more versatile stem - stem being a shorthand to denote the subword sequence formed by the first few characters of the word - than words that are loaned from other languages. We harness this observation to build an objective function and an iterative optimization formulation to optimize for it, yielding a scoring of each word’s nativeness in the process. Through an extensive empirical analysis over real-world datasets from both Malayalam and Telugu, we illustrate the effectiveness of our method in quantifying nativeness effectively over available baselines for the task. |
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| Published | 2020-02-12 |
| URL | https://arxiv.org/abs/2002.05527v1 |
| https://arxiv.org/pdf/2002.05527v1.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-separation-of-native-and |
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Scale-Invariant Multi-Oriented Text Detection in Wild Scene Images
| Title | Scale-Invariant Multi-Oriented Text Detection in Wild Scene Images |
| Authors | Kinjal Dasgupta, Sudip Das, Ujjwal Bhattacharya |
| Abstract | Automatic detection of scene texts in the wild is a challenging problem, particularly due to the difficulties in handling (i) occlusions of varying percentages, (ii) widely different scales and orientations, (iii) severe degradations in the image quality etc. In this article, we propose a fully convolutional neural network architecture consisting of a novel Feature Representation Block (FRB) capable of efficient abstraction of information. The proposed network has been trained using curriculum learning with respect to difficulties in image samples and gradual pixel-wise blurring. It is capable of detecting texts of different scales and orientations suffered by blurring from multiple possible sources, non-uniform illumination as well as partial occlusions of varying percentages. Text detection performance of the proposed framework on various benchmark sample databases including ICDAR 2015, ICDAR 2017 MLT, COCO-Text and MSRA-TD500 improves respective state-of-the-art results significantly. Source code of the proposed architecture will be made available at github. |
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| Published | 2020-02-15 |
| URL | https://arxiv.org/abs/2002.06423v1 |
| https://arxiv.org/pdf/2002.06423v1.pdf | |
| PWC | https://paperswithcode.com/paper/scale-invariant-multi-oriented-text-detection |
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Explainable and Scalable Machine-Learning Algorithms for Detection of Autism Spectrum Disorder using fMRI Data
| Title | Explainable and Scalable Machine-Learning Algorithms for Detection of Autism Spectrum Disorder using fMRI Data |
| Authors | Taban Eslami, Joseph S. Raiker, Fahad Saeed |
| Abstract | Diagnosing Autism Spectrum Disorder (ASD) is a challenging problem, and is based purely on behavioral descriptions of symptomology (DSM-5/ICD-10), and requires informants to observe children with disorder across different settings (e.g. home, school). Numerous limitations (e.g., informant discrepancies, lack of adherence to assessment guidelines, informant biases) to current diagnostic practices have the potential to result in over-, under-, or misdiagnosis of the disorder. Advances in neuroimaging technologies are providing a critical step towards a more objective assessment of the disorder. Prior research provides strong evidence that structural and functional magnetic resonance imaging (MRI) data collected from individuals with ASD exhibit distinguishing characteristics that differ in local and global spatial, and temporal neural-patterns of the brain. Our proposed deep-learning model ASD-DiagNet exhibits consistently high accuracy for classification of ASD brain scans from neurotypical scans. We have for the first time integrated traditional machine-learning and deep-learning techniques that allows us to isolate ASD biomarkers from MRI data sets. Our method, called Auto-ASD-Network, uses a combination of deep-learning and Support Vector Machines (SVM) to classify ASD scans from neurotypical scans. Such interpretable models would help explain the decisions made by deep-learning techniques leading to knowledge discovery for neuroscientists, and transparent analysis for clinicians. |
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| Published | 2020-03-02 |
| URL | https://arxiv.org/abs/2003.01541v1 |
| https://arxiv.org/pdf/2003.01541v1.pdf | |
| PWC | https://paperswithcode.com/paper/explainable-and-scalable-machine-learning |
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Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization
| Title | Columnwise Element Selection for Computationally Efficient Nonnegative Coupled Matrix Tensor Factorization |
| Authors | Thirunavukarasu Balasubramaniam, Richi Nayak, Chau Yuen |
| Abstract | Coupled Matrix Tensor Factorization (CMTF) facilitates the integration and analysis of multiple data sources and helps discover meaningful information. Nonnegative CMTF (N-CMTF) has been employed in many applications for identifying latent patterns, prediction, and recommendation. However, due to the added complexity with coupling between tensor and matrix data, existing N-CMTF algorithms exhibit poor computation efficiency. In this paper, a computationally efficient N-CMTF factorization algorithm is presented based on the column-wise element selection, preventing frequent gradient updates. Theoretical and empirical analyses show that the proposed N-CMTF factorization algorithm is not only more accurate but also more computationally efficient than existing algorithms in approximating the tensor as well as in identifying the underlying nature of factors. |
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| Published | 2020-03-07 |
| URL | https://arxiv.org/abs/2003.03506v1 |
| https://arxiv.org/pdf/2003.03506v1.pdf | |
| PWC | https://paperswithcode.com/paper/columnwise-element-selection-for |
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