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arXiv 2024年

作者： Legin, Ronan Isi, Maximiliano Wong, Kaze W.K. Hezaveh, Yashar Perreault-Levasseur, Laurence Ciela - Montreal Institute for Astrophysical Data Analysis and Machine Learning MontréalQC Canada Department of Physics Université de Montréal MontréalQC Canada Mila - Quebec Artificial Intelligence Institute MontréalQC Canada Center for Computational Astrophysics Flatiron Institute New YorkNY United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Trottier Space Institute MontréalQC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada

Gravitational-wave (GW) parameter estimation typically assumes that instrumental noise is Gaussian and stationary. Obvious departures from this idealization are typically handled on a case-by-case basis, e.g., through bespoke procedures to "clean" non-Gaussian noise transients (glitches), as was famously the case for the GW170817 neutron-star binary. Although effective, manipulating the data in this way can introduce biases in the inference of key astrophysical properties, like binary precession, and compound in unpredictable ways when combining multiple observations;alternative procedures free of the same biases, like joint inference of noise and signal properties, have so far proved too computationally expensive to execute at scale. Here we take a different approach: rather than explicitly modeling individual non-Gaussianities to then apply the traditional GW likelihood, we seek to learn the true distribution of instrumental noise without presuming Gaussianity and stationarity in the first place. Assuming only noise additivity, we employ score-based diffusion models to learn an empirical noise distribution directly from detector data and then combine it with a deterministic waveform model to provide an unbiased estimate of the likelihood function. We validate the method by performing inference on a subset of GW parameters from 400 mock observations, containing real LIGO noise from either the Livingston or Hanford detectors. We show that the proposed method can recover the true parameters even in the presence of loud glitches, and that the inference is unbiased over a population of signals without applying any cleaning to the data. This work provides a promising avenue for extracting unbiased source properties in future GW observations over the coming decade. © 2024, CC BY.

关键词： Background noise

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mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R

arXiv

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arXiv 2021年

作者： Schratz, Patrick Becker, Marc Lang, Michel Brenning, Alexander Friedrich Schiller University Jena Department of Geography Geographic Information Science Group Germany Ludwig-Maximilians-Universität München Department of Statistics Statistical Learning and Data Science Group Germany TU Dortmund University Faculty of Statistics Germany

Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution reviews the state-of-the-art in spatial and spatiotemporal cross-validation, and introduces the R package mlr3spatiotempcv as an extension package of the machine-learning framework mlr3. Currently various R packages implementing different spatiotemporal partitioning strategies exist: blockCV, CAST, skmeans and sperrorest. The goal of mlr3spatiotempcv is to gather the available spatiotemporal resampling methods in R and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the mlr3 machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied. © 2021, CC BY.

关键词： Autocorrelation

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ASSESSING REUSABILITY OF DEEP learning-BASED MONOTHERAPY DRUG RESPONSE PREDICTION MODELS TRAINED WITH OMICS data

arXiv

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arXiv 2024年

作者： Overbeek, Jamie C. Partin, Alexander Brettin, Thomas S. Chia, Nicholas Narykov, Oleksandr Vasanthakumari, Priyanka Wilke, Andreas Zhu, Yitan Clyde, Austin Jones, Sara Gnanaolivu, Rohan Liu, Yuanhang Jiang, Jun Wang, Chen Knutson, Carter McNaughton, Andrew Kumar, Neeraj Fernando, Gayara Demini Ghosh, Souparno Sanchez-Villalobos, Cesar Zhang, Ruibo Pal, Ranadip Ryan Weil, M. Stevens, Rick L. Data Science and Learning Division Argonne National Laboratory LemontIL United States Computing Environment and Life Sciences Argonne National Laboratory LemontIL United States Department of Computer Science The University of Chicago ChicagoIL United States Frederick National Laboratory for Cancer Research FrederickMD United States Department of Quantitative Health Sciences Mayo Clinic RochesterMN United States Pacific Northwest National Laboratory RichlandWA United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. Issues like underspecified software environments, lack of workflow documentation, and omission of data preprocessing scripts create challenges for users attempting to reproduce or build upon these models. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP a

关键词： Prediction models

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Generative locally linear embedding

arXiv

引用

arXiv 2021年

作者： Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark The Machine Learning Laboratory Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada The Department of Statistics and Actuarial Science University of Waterloo WaterlooON Canada The Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input space and embedding space, respectively. In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic. GLLE assumes that every data point is caused by its linear reconstruction weights as latent factors. The proposed GLLE algorithms can generate various LLE embeddings stochastically while all the generated embeddings relate to the original LLE embedding. We propose two versions for stochastic linear reconstruction, one using expectation maximization and another with direct sampling from a derived distribution by optimization. The proposed GLLE methods are closely related to and inspired by variational inference, factor analysis, and probabilistic principal component analysis. Our simulations show that the proposed GLLE methods work effectively in unfolding and generating submanifolds of data. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Heat flux for semi-local machine-learning potentials

arXiv

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arXiv 2023年

作者： Langer, Marcel F. Knoop, Florian Carbogno, Christian Scheffler, Matthias Rupp, Matthias Machine Learning Group Technische Universität Berlin Berlin10587 Germany Bifold - Berlin Institute for the Foundations of Learning and Data Berlin Germany The Nomad Laboratory The Fhi of the Max-Planck-Gesellschaft Iris Adlershof The Humboldt Universität zu Berlin Germany Linköping University LinköpingSE-581 83 Sweden Department of Computer and Information Science University of Konstanz Konstanz78464 Germany Belvaux Luxembourg

The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this paper, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semi-local interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

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XAI for Transformers: Better Explanations through Conservative Propagation

arXiv

引用

arXiv 2022年

作者： Ali, Ameen Ali Schnake, Thomas Eberle, Oliver Montavon, Grégoire Müller, Klaus-Robert Wolf, Lior The School of Computer Science Tel-Aviv University Israel Machine Learning Group Technische Universität Berlin Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transparency. Multiple interpretability methods, often based on gradient information, have been proposed. We show that the gradient in a Transformer reflects the function only locally, and thus fails to reliably identify the contribution of input features to the prediction. We identify Attention Heads and LayerNorm as the main reasons for such unreliable explanations and propose a more stable way for propagation through these layers. Our proposal, which can be seen as a proper extension of the well-established LRP method to Transformers, is shown both theoretically and empirically to overcome the deficiency of a simple gradient-based approach, and achieves state-of-the-art explanation performance on a broad range of Transformer models and datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： machine learning

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Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations

arXiv

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arXiv 2023年

作者： Khan, Danish Heinen, Stefan von Lilienfeld, O. Anatole Department of Chemistry University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

The feature vector mapping used to represent chemical systems is a key factor governing the superior data-efficiency of kernel based quantum machine learning (QML) models applicable throughout chemical compound space. Unfortunately, the most accurate representations require a high dimensional feature mapping, thereby imposing a considerable computational burden on model training and use. We introduce compact yet accurate, linear scaling QML representations based on atomic Gaussian many-body distribution functionals (MBDF), and their derivatives. Weighted density functions (DF) of MBDF values are used as global representations which are constant in size, i.e. invariant with respect to the number of atoms. We report predictive performance and training data efficiency that is competitive with state of the art for two diverse datasets of organic molecules, QM9 and QMugs. Generalization capability has been investigated for atomization energies, HOMO-LUMO eigenvalues and gap, internal energies at 0 K, zero point vibrational energies, dipole moment norm, static isotropic polarizability, and heat capacity as encoded in QM9. MBDF based QM9 performance lowers the optimal Pareto front spanned between sampling and training cost to compute node minutes, effectively sampling chemical compound space with chemical accuracy at a sampling rate of ∼ 48 molecules per core second. © 2023, CC0.

关键词： machine learning

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Predicting Response to Patients with Gastric Cancer Via a Dynamic-Aware Model with Longitudinal Liquid Biopsy data

SSRN

引用

SSRN 2024年

作者： Chen, Zifan Zhao, Jie Li, Yanyan Li, Yilin Liu, Huimin Feng, Xujiao Nan, Xinyu Dong, Bin Shen, Lin Chen, Yang Zhang, Li Center for Data Science Peking University Beijing China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Guangzhou Medical University Guangzhou China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Changsha China

Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have become recognized as a valuable data modality, offering essential cellular and molecular insights and enabling the capture of time-sensitive information. This study aimed to harness artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022, which included 1,895 tumor-related cellular images and 1,698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict GC treatment responses. DAM incorporates the dynamic data through AI-engineered components, enabling an in-depth longitudinal analysis. Utilizing three-fold cross-validation, DAM exhibited superior performance over traditional cell-counting-based methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses from early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic focus-area-related visual features and their strong association with treatment response. These findings represent a pioneering effort in applying AI technology for interpreting longitudinal liquid biopsy data and employ visual analytics in GC, offering a promising avenue toward precise response prediction and tailored treatment strategies for patients with ***: This work was supported by the National Natural science Foundation of China (81801778 to Li Zhang., 82203881 to Yang Chen, U22A20327 to Lin Shen, 12090022 to Bin Dong), Beijing Natural science Foundation (7222021 to Yang Chen), Beijing Hospitals Authority Youth Programme (QML20231115 to Yang Chen), Clinical Medicine Plus X-Young Scholars Project of P

关键词： Diseases

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Cross-Store Next-Basket Recommendation

Cross-Store Next-Basket Recommendation

引用

IEEE International Conference on data Mining (ICDM)

作者： Liang-Chen Ma Ya Li Zi-Feng Mai Fei-Yao Liang Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

关键词： Neural networks Collaboration data models Bipartite graph data mining

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Personalised Speech-Based PTSD Prediction Using Weighted-Instance learning

Personalised Speech-Based PTSD Prediction Using Weighted-Ins...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alexander Kathan Shahin Amiriparian Andreas Triantafyllopoulos Alexander Gebhard Sabrina Milkus Jonas Hohmann Pauline Muderlak Jürgen Schottdorf Richard Musil Björn W. Schuller EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing University of Augsburg Germany CHI – Chair of Health Informatics MRI Technical Univsersity of Munich Germany MCML – Munich Center for Machine Learning Germany Department of Psychiatry and Psychotherapy University Hospital LMU Munich Germany Zentrumspraxis Friedberg Germany GLAM – Group on Language Audio & Music Imperial College London UK MDSI – Munich Data Science Institute Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples’ daily life and can affect their mental, physical, or social wellbeing, which is why a timely and professional treatment is required. In this paper, we propose a personalised speech-based PTSD prediction approach using a newly collected dataset which consists of 15 participants, including speech recordings from people with PTSD and healthy controls. In addition, the dataset includes data before and after a clinical intervention so that the prediction can be analysed at different points in time. In our experiments, we demonstrate the superiority of the personalised approach, achieving a best area under the ROC curve (AUC) of 82 % and a best relative improvement of 7 % points compared to the non-personalised model.

关键词： Biological system modeling Human factors Recording Engineering in medicine and biology

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