July 28, 2019

3084 words 15 mins read

Paper Group ANR 380

What do we need to build explainable AI systems for the medical domain?. Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing. Deriving Probability Density Functions from Probabilistic Functional Programs. An Ontological Architecture for Orbital Debris Data. Audio Scene Classification with Deep Recurrent Neural Networks. Web Robot D …

What do we need to build explainable AI systems for the medical domain?


Title	What do we need to build explainable AI systems for the medical domain?
Authors	Andreas Holzinger, Chris Biemann, Constantinos S. Pattichis, Douglas B. Kell
Abstract	Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement learning methods have even exceeded human performance in visual tasks, particularly on playing games such as Atari, or mastering the game of Go. Even in the medical domain there are remarkable results. The central problem of such models is that they are regarded as black-box models and even if we understand the underlying mathematical principles, they lack an explicit declarative knowledge representation, hence have difficulty in generating the underlying explanatory structures. This calls for systems enabling to make decisions transparent, understandable and explainable. A huge motivation for our approach are rising legal and privacy aspects. The new European General Data Protection Regulation entering into force on May 25th 2018, will make black-box approaches difficult to use in business. This does not imply a ban on automatic learning approaches or an obligation to explain everything all the time, however, there must be a possibility to make the results re-traceable on demand. In this paper we outline some of our research topics in the context of the relatively new area of explainable-AI with a focus on the application in medicine, which is a very special domain. This is due to the fact that medical professionals are working mostly with distributed heterogeneous and complex sources of data. In this paper we concentrate on three sources: images, *omics data and text. We argue that research in explainable-AI would generally help to facilitate the implementation of AI/ML in the medical domain, and specifically help to facilitate transparency and trust.
Tasks	Autonomous Driving, Game of Go, Recommendation Systems, Speech Recognition
Published	2017-12-28
URL	http://arxiv.org/abs/1712.09923v1
PDF	http://arxiv.org/pdf/1712.09923v1.pdf
PWC	https://paperswithcode.com/paper/what-do-we-need-to-build-explainable-ai
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Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing


Title	Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
Authors	Wesley Tansey, Jesse Thomason, James G. Scott
Abstract	We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance. To address this problem, we present Maximum Variance Total Variation denoising (MVTV), an approach that is conceptually related both to CART and to the more recent CRISP algorithm, a state-of-the-art alternative method for interpretable nonlinear regression. MVTV divides the feature space into blocks of constant value and fits the value of all blocks jointly via a convex optimization routine. Our method is fully data-adaptive, in that it incorporates highly robust routines for tuning all hyperparameters automatically. We compare our approach against CART and CRISP via both a complexity-accuracy tradeoff metric and a human study, demonstrating that that MVTV is a more powerful and interpretable method.
Tasks	Denoising
Published	2017-08-06
URL	http://arxiv.org/abs/1708.01947v1
PDF	http://arxiv.org/pdf/1708.01947v1.pdf
PWC	https://paperswithcode.com/paper/interpretable-low-dimensional-regression-via
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Deriving Probability Density Functions from Probabilistic Functional Programs


Title	Deriving Probability Density Functions from Probabilistic Functional Programs
Authors	Sooraj Bhat, Johannes Borgström, Andrew D. Gordon, Claudio Russo
Abstract	The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.
Tasks
Published	2017-04-04
URL	http://arxiv.org/abs/1704.00917v2
PDF	http://arxiv.org/pdf/1704.00917v2.pdf
PWC	https://paperswithcode.com/paper/deriving-probability-density-functions-from
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An Ontological Architecture for Orbital Debris Data


Title	An Ontological Architecture for Orbital Debris Data
Authors	Robert J. Rovetto
Abstract	The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.
Tasks
Published	2017-04-01
URL	http://arxiv.org/abs/1704.01014v2
PDF	http://arxiv.org/pdf/1704.01014v2.pdf
PWC	https://paperswithcode.com/paper/an-ontological-architecture-for-orbital
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Audio Scene Classification with Deep Recurrent Neural Networks


Title	Audio Scene Classification with Deep Recurrent Neural Networks
Authors	Huy Phan, Philipp Koch, Fabrice Katzberg, Marco Maass, Radoslaw Mazur, Alfred Mertins
Abstract	We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. An audio scene is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRU-based recurrent neural network is trained for sequence-to-label classification. The global predicted label for the entire sequence is finally obtained via aggregation of subsequence classification outputs. We will show that our approach obtains an F1-score of 97.7% on the LITIS Rouen dataset, which is the largest dataset publicly available for the task. Compared to the best previously reported result on the dataset, our approach is able to reduce the relative classification error by 35.3%.
Tasks	Scene Classification
Published	2017-03-14
URL	http://arxiv.org/abs/1703.04770v2
PDF	http://arxiv.org/pdf/1703.04770v2.pdf
PWC	https://paperswithcode.com/paper/audio-scene-classification-with-deep
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Web Robot Detection in Academic Publishing


Title	Web Robot Detection in Academic Publishing
Authors	Athanasios Lagopoulos, Grigorios Tsoumakas, Georgios Papadopoulos
Abstract	Recent industry reports assure the rise of web robots which comprise more than half of the total web traffic. They not only threaten the security, privacy and efficiency of the web but they also distort analytics and metrics, doubting the veracity of the information being promoted. In the academic publishing domain, this can cause articles to be faulty presented as prominent and influential. In this paper, we present our approach on detecting web robots in academic publishing websites. We use different supervised learning algorithms with a variety of characteristics deriving from both the log files of the server and the content served by the website. Our approach relies on the assumption that human users will be interested in specific domains or articles, while web robots crawl a web library incoherently. We experiment with features adopted in previous studies with the addition of novel semantic characteristics which derive after performing a semantic analysis using the Latent Dirichlet Allocation (LDA) algorithm. Our real-world case study shows promising results, pinpointing the significance of semantic features in the web robot detection problem.
Tasks
Published	2017-11-14
URL	http://arxiv.org/abs/1711.05098v1
PDF	http://arxiv.org/pdf/1711.05098v1.pdf
PWC	https://paperswithcode.com/paper/web-robot-detection-in-academic-publishing
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Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend


Title	Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with Trend
Authors	N. Olspert, J. Pelt, M. J. Käpylä, J. Lehtinen
Abstract	Period estimation is one of the central topics in astronomical time series analysis, where data is often unevenly sampled. Especially challenging are studies of stellar magnetic cycles, as there the periods looked for are of the order of the same length than the datasets themselves. The datasets often contain trends, the origin of which is either a real long-term cycle or an instrumental effect, but these effects cannot be reliably separated, while they can lead to erroneous period determinations if not properly handled. In this study we aim at developing a method that can handle the trends properly, and by performing extensive set of testing, we show that this is the optimal procedure when contrasted with methods that do not include the trend directly to the model. The effect of the form of the noise (whether constant or heteroscedastic) on the results is also investigated. We introduce a Bayesian Generalised Lomb-Scargle Periodogram with Trend (BGLST), which is a probabilistic linear regression model using Gaussian priors for the coefficients and uniform prior for the frequency parameter. We show, using synthetic data, that when there is no prior information on whether and to what extent the true model of the data contains a linear trend, the introduced BGLST method is preferable to the methods which either detrend the data or leave the data untrended before fitting the periodic model. Whether to use noise with different than constant variance in the model depends on the density of the data sampling as well as on the true noise type of the process.
Tasks	Time Series, Time Series Analysis
Published	2017-12-21
URL	http://arxiv.org/abs/1712.08235v2
PDF	http://arxiv.org/pdf/1712.08235v2.pdf
PWC	https://paperswithcode.com/paper/estimating-activity-cycles-with-probabilistic
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A Neural Stochastic Volatility Model


Title	A Neural Stochastic Volatility Model
Authors	Rui Luo, Weinan Zhang, Xiaojun Xu, Jun Wang
Abstract	In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e.g., deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based model \emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol}, on average negative log-likelihood.
Tasks	Time Series, Time Series Analysis
Published	2017-11-30
URL	http://arxiv.org/abs/1712.00504v2
PDF	http://arxiv.org/pdf/1712.00504v2.pdf
PWC	https://paperswithcode.com/paper/a-neural-stochastic-volatility-model
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Look Who’s Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches


Title	Look Who’s Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches
Authors	Ben Curran, Kyle Higham, Elisenda Ortiz, Demival Vasques Filho
Abstract	Quantitative methods to measure the participation to parliamentary debate and discourse of elected Members of Parliament (MPs) and the parties they belong to are lacking. This is an exploratory study in which we propose the development of a new approach for a quantitative analysis of such participation. We utilize the New Zealand government’s digital Hansard database to construct a topic model of parliamentary speeches consisting of nearly 40 million words in the period 2003-2016. A Latent Dirichlet Allocation topic model is implemented in order to reveal the thematic structure of our set of documents. This generative statistical model enables the detection of major themes or topics that are publicly discussed in the New Zealand parliament, as well as permitting their classification by MP. Information on topic proportions is subsequently analyzed using a combination of statistical methods. We observe patterns arising from time-series analysis of topic frequencies which can be related to specific social, economic and legislative events. We then construct a bipartite network representation, linking MPs to topics, for each of four parliamentary terms in this time frame. We build projected networks (onto the set of nodes represented by MPs) and proceed to the study of the dynamical changes of their topology, including community structure. By performing this longitudinal network analysis, we can observe the evolution of the New Zealand parliamentary topic network and its main parties in the period studied.
Tasks	Time Series, Time Series Analysis
Published	2017-07-11
URL	http://arxiv.org/abs/1707.03095v3
PDF	http://arxiv.org/pdf/1707.03095v3.pdf
PWC	https://paperswithcode.com/paper/look-whos-talking-bipartite-networks-as
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Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach


Title	Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach
Authors	Zhenghao Chen, Jianlong Zhou, Xiuying Wang
Abstract	Time-Spatial data plays a crucial role for different fields such as traffic management. These data can be collected via devices such as surveillance sensors or tracking systems. However, how to efficiently an- alyze and visualize these data to capture essential embedded pattern information is becoming a big challenge today. Classic visualization ap- proaches focus on revealing 2D and 3D spatial information and modeling statistical test. Those methods would easily fail when data become mas- sive. Recent attempts concern on how to simply cluster data and perform prediction with time-oriented information. However, those approaches could still be further enhanced as they also have limitations for han- dling massive clusters and labels. In this paper, we propose a visualiza- tion methodology for mobility data using artificial neural net techniques. This method aggregates three main parts that are Back-end Data Model, Neural Net Algorithm including clustering method Self-Organizing Map (SOM) and prediction approach Recurrent Neural Net (RNN) for ex- tracting the features and lastly a solid front-end that displays the results to users with an interactive system. SOM is able to cluster the visiting patterns and detect the abnormal pattern. RNN can perform the predic- tion for time series analysis using its dynamic architecture. Furthermore, an interactive system will enable user to interpret the result with graph- ics, animation and 3D model for a close-loop feedback. This method can be particularly applied in two tasks that Commercial-based Promotion and abnormal traffic patterns detection.
Tasks	Time Series, Time Series Analysis
Published	2017-07-09
URL	http://arxiv.org/abs/1707.02554v1
PDF	http://arxiv.org/pdf/1707.02554v1.pdf
PWC	https://paperswithcode.com/paper/visual-analytics-of-movement-pattern-based-on
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Framework

Monte Carlo approximation certificates for k-means clustering


Title	Monte Carlo approximation certificates for k-means clustering
Authors	Dustin G. Mixon, Soledad Villar
Abstract	Efficient algorithms for $k$-means clustering frequently converge to suboptimal partitions, and given a partition, it is difficult to detect $k$-means optimality. In this paper, we develop an a posteriori certifier of approximate optimality for $k$-means clustering. The certifier is a sub-linear Monte Carlo algorithm based on Peng and Wei’s semidefinite relaxation of $k$-means. In particular, solving the relaxation for small random samples of the dataset produces a high-confidence lower bound on the $k$-means objective, and being sub-linear, our algorithm is faster than $k$-means++ when the number of data points is large. We illustrate the performance of our algorithm with both numerical experiments and a performance guarantee: If the data points are drawn independently from any mixture of two Gaussians over $\mathbb{R}^m$ with identity covariance, then with probability $1-O(1/m)$, our $\operatorname{poly}(m)$-time algorithm produces a 3-approximation certificate with 99% confidence.
Tasks
Published	2017-10-03
URL	http://arxiv.org/abs/1710.00956v1
PDF	http://arxiv.org/pdf/1710.00956v1.pdf
PWC	https://paperswithcode.com/paper/monte-carlo-approximation-certificates-for-k
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Improving Semantic Composition with Offset Inference


Title	Improving Semantic Composition with Offset Inference
Authors	Thomas Kober, Julie Weeds, Jeremy Reffin, David Weir
Abstract	Count-based distributional semantic models suffer from sparsity due to unobserved but plausible co-occurrences in any text collection. This problem is amplified for models like Anchored Packed Trees (APTs), that take the grammatical type of a co-occurrence into account. We therefore introduce a novel form of distributional inference that exploits the rich type structure in APTs and infers missing data by the same mechanism that is used for semantic composition.
Tasks	Semantic Composition
Published	2017-04-21
URL	http://arxiv.org/abs/1704.06692v1
PDF	http://arxiv.org/pdf/1704.06692v1.pdf
PWC	https://paperswithcode.com/paper/improving-semantic-composition-with-offset
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Framework

Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis


Title	Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis
Authors	B. M. Pavlyshenko
Abstract	In this paper we study different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.
Tasks	Bayesian Inference, Time Series, Time Series Analysis
Published	2017-02-26
URL	http://arxiv.org/abs/1703.01977v1
PDF	http://arxiv.org/pdf/1703.01977v1.pdf
PWC	https://paperswithcode.com/paper/linear-machine-learning-and-probabilistic
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Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing


Title	Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing
Authors	Shujian Yu, Zubin Abraham, Heng Wang, Mohak Shah, Yantao Wei, José C. Príncipe
Abstract	A fundamental issue for statistical classification models in a streaming environment is that the joint distribution between predictor and response variables changes over time (a phenomenon also known as concept drifts), such that their classification performance deteriorates dramatically. In this paper, we first present a hierarchical hypothesis testing (HHT) framework that can detect and also adapt to various concept drift types (e.g., recurrent or irregular, gradual or abrupt), even in the presence of imbalanced data labels. A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR), is implemented under the HHT framework thereafter. By substituting a widely-acknowledged retraining scheme with an adaptive training strategy, we further demonstrate that the concept drift adaptation capability of HLFR can be significantly boosted. The theoretical analysis on the Type-I and Type-II errors of HLFR is also performed. Experiments on both simulated and real-world datasets illustrate that our methods outperform state-of-the-art methods in terms of detection precision, detection delay as well as the adaptability across different concept drift types.
Tasks
Published	2017-07-25
URL	http://arxiv.org/abs/1707.07821v6
PDF	http://arxiv.org/pdf/1707.07821v6.pdf
PWC	https://paperswithcode.com/paper/concept-drift-detection-and-adaptation-with
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Advances in Variational Inference


Title	Advances in Variational Inference
Authors	Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, Stephan Mandt
Abstract	Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.
Tasks
Published	2017-11-15
URL	http://arxiv.org/abs/1711.05597v3
PDF	http://arxiv.org/pdf/1711.05597v3.pdf
PWC	https://paperswithcode.com/paper/advances-in-variational-inference
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