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2019 Conference on "learning, Knowledge, Data, Analytics", LWDA 2019

作者： Pick, Annika Horváth, Tamás Wrobel, Stefan Fraunhofer IAIS Sankt Augustin Germany Dep. of Computer Science University of Bonn Germany Fraunhofer Center for Machine Learning Sankt Augustin Germany

The main computational effort in generating all frequent itemsets in a transactional database is in the step of deciding whether an itemset is frequent, or not. We present a method for estimating itemset supports with two-sided error. In a preprocessing step our algorithm first partitions the database into groups of similar transactions by using locality sensitive hashing and calculates a summary for each of these groups. The support of a query itemset is then estimated by means of these summaries. Our preliminary empirical results indicate that the proposed method results in a speed-up of up to a factor of 50 on large datasets. The F-measure of the output patterns varies between 0.83 and 0.99. Copyright © 2019 for this paper by its authors.

关键词： Large dataset

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Pruning neural network models for gene regulatory dynamics using data and domain knowledge 38

Pruning neural network models for gene regulatory dynamics u...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hossain, Intekhab Fischer, Jonas Burkholz, Rebekka Quackenbush, John Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA02115 United States Dep. for Computer Vision and Machine Learning Max Planck Institute for Informatics Saarbrücken Germany Helmholtz Center CISPA for Information Security Saarbrücken Germany

The practical utility of machine learning models in the sciences often hinges on their interpretability. It is common to assess a model's merit for scientific discovery, and thus novel insights, by how well it aligns with already available domain knowledge-a dimension that is currently largely disregarded in the comparison of neural network models. While pruning can simplify deep neural network architectures and excels in identifying sparse models, as we show in the context of gene regulatory network inference, state-of-the-art techniques struggle with biologically meaningful structure learning. To address this issue, we propose DASH‡, a generalizable framework that guides network pruning by using domain-specific structural information in model fitting and leads to sparser, better interpretable models that are more robust to noise. Using both synthetic data with ground truth information, as well as real-world gene expression data, we show that DASH, using knowledge about gene interaction partners within the putative regulatory network, outperforms general pruning methods by a large margin and yields deeper insights into the biological systems being studied. © 2024 Neural information processing systems foundation. All rights reserved.

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Latent Event-Predictive Encodings through Counterfactual Regularization 43

Latent Event-Predictive Encodings through Counterfactual Reg...

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43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Humaidan, Dania Otte, Sebastian Gumbsch, Christian Wu, Charley Butz, Martin V. Neuro-Cognitive Modeling Group Dep. of Psychology Dep. of Computer Science Sand 14 Tübingen72076 Germany Cluster of Excellence – Machine Learning for Science Maria-von-Linden-Str. 6 Tübingen72076 Germany Autonomous Learning Group MPI for Intelligent Systems Tübingen72076 Germany Human and Machine Cognition Lab Maria-von-Linden-Str. 6 Tübingen72076 Germany

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making. © Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Intelligent systems

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FRI - Feature relevance intervals for interpretable and interactive data exploration

arXiv

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arXiv 2019年

作者： Pfannschmidt, Lukas Gpfert, Christina Neumann, Ursula Heider, Dominik Hammer, Barbara DiDy & Machine learning group Bielefeld University Bielefeld Germany Machine learning group Bielefeld University Bielefeld Germany Dep. of Mathematics and Computer Science University of Marburg Marburg Germany

Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance interval method, FRI is able to provide the base for further general experimentation or in specific can facilitate the search for alternative biomarkers. It can be used in an interactive context, by providing model manipulation and visualization methods, or in a batch process as a filter method. Copyright © 2019, The Authors. All rights reserved.

关键词： Batch data processing

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Similarity-Based Clustering 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Michael Biehl Barbara Hammer Michel Verleysen Thomas Villmann

ISBN: (数字)9783642018053

ISBN: (纸本)9783642018046

Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox for mining, visualization,and inspection of largedata sets. They combine simple and human-understandable principles, such as distance-based classi?cation, prototypes, or Hebbian learning, with a large variety of di?erent, problem-adapted design choices, such as a data-optimum topology, similarity measure, or learning mode. In medicine, biology, and medical bioinformatics, more and more data arise from clinical measurements such as EEG or fMRI studies for monitoring brain activity, mass spectrometry data for the detection of proteins, peptides and composites, or microarray pro?les for the analysis of gene expressions. Typically, data are high-dimensional, noisy, and very hard to inspect using classic (e. g. , symbolic or linear) methods. At the same time, new technologies ranging from the possibility of a very high resolution of spectra to high-throughput screening for microarray data are rapidly developing and carry thepromiseofane?cient,cheap,andautomaticgatheringoftonsofhigh-quality data with large information potential. Thus, there is a need for appropriate - chine learning methods which help to automatically extract and interpret the relevant parts of this information and which, eventually, help to enable und- standingofbiologicalsystems,reliablediagnosisoffaults,andtherapyofdiseases such as cancer based on this information. Moreover, these application scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of the speci?cs of the data and learning tasks. Since these characteristics are particularly pronounced within the medical domain, but not limited to it and of principled interest, this research topic opens the way towardimportant new directions of algorithmic design and accompanying theory.

关键词： Life Sciences, general Computational Biology/Bioinformatics Biomedicine general Data Mining and Knowledge Discovery Information Storage and Retrieval computer Imaging, vision, Pattern Recognition and Graphics

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Pruning neural network models for gene regulatory dynamics using data and domain knowledge

arXiv

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arXiv 2024年

关键词： Neural network models

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Latent event-predictive encodings through counterfactual regularization

arXiv

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arXiv 2021年

A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making. Copyright © 2021, The Authors. All rights reserved.

关键词： Recurrent neural networks

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