检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Abdelmalak, Ibram Madhusudhanan, Kiran Choi, Jungmin Stubbemann, Maximilian Schmidt-Thieme, Lars Information Science and Machine Learning Lab VWFS Data Analytics Research Center University of Hildesheim Niedersachsen Hildesheim Germany

Time-series forecasting research has converged to a small set of datasets and a standardized collection of evaluation scenarios. Such a standardization is to a specific extent needed for comparable research. However, the underlying assumption is, that the considered setting is a representative for the problem as a whole. In this paper, we challenge this assumption and show that the current scenario gives a strongly biased perspective on the state of time-series forecasting research. To be more detailed, we show that the current evaluation scenario is heavily biased by the simplicity of the current datasets. We furthermore emphasize, that when the lookback-window is properly tuned, current models usually do not need any information flow across channels. However, when using more complex benchmark data, the situation changes: Here, modeling channel-interactions in a sophisticated manner indeed enhances performances. Furthermore, in this complex evaluation scenario, Crossformer, a method regularly neglected as an important baseline, is the SOTA method for time series forecasting. Based on this, we present the Fast Channel-dependent Transformer (FaCT), a simplified version of Crossformer which closes the runtime gap between Crossformer and TimeMixer, leading to an efficient model for complex forecasting datasets. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives

arXiv

引用

arXiv 2025年

作者： Haddadnia, Mohammad Grashoff, Leonie Strieth-Kalthoff, Felix Harvard University Department of Biological Chemistry & Molecular Pharmacology BostonMA United States Harvard University Dana-Farber Cancer Institute Department of Cancer Biology BostonMA United States University of Wuppertal School of Mathematics and Natural Sciences Wuppertal Germany University of Wuppertal Interdisciplinary Center for Machine Learning and Data Analytics Wuppertal Germany

Scientific optimization problems are usually concerned with balancing multiple competing objectives, which come as preferences over both the outcomes of an experiment (e.g. maximize the reaction yield) and the corresponding input parameters (e.g. minimize the use of an expensive reagent). Typically, practical and economic considerations define a hierarchy over these objectives, which must be reflected in algorithms for sample-efficient experiment planning. Herein, we introduce BoTier, a composite objective that can flexibly represent a hierarchy of preferences over both experiment outcomes and input parameters. We provide systematic benchmarks on synthetic and real-life surfaces, demonstrating the robust applicability of BoTier across a number of use cases. Importantly, BoTier is implemented in an auto-differentiable fashion, enabling seamless integration with the BoTorch library, thereby facilitating adoption by the scientific community. © 2025, CC BY.

关键词： Bot (Internet)

来源：评论

学校读者我要写书评

暂无评论

Generative models for the transfer of knowledge in seismic interpretation with deep learning

引用

Leading Edge 2021年第7期40卷 534-542页

作者： Durall, Ricard Tschannen, Valentin Ettrich, Norman Keuper, Janis Fraunhofer Institute for Industrial Mathematics Kaiserslautern Germany Interdisciplinary Center for Scientific Computing University of Heidelberg Heidelberg Germany Fraunhofer Research Center for Machine Learning Germany Institute for Machine Learning and Analytics Offenburg University Offenburg Germany

Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network. © 2021 Society of Exploration Geophysicists. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Massive data clustering by multi-scale psychological observations

引用

National Science Review 2022年第2期9卷 43-51页

作者： Shusen Yang Liwen Zhang Chen Xu Hanqiao Yu Jianqing Fan Zongben Xu National Engineering Laboratory of Big Data Analytics Xi'an Jiaotong University Industrial Artificial Intelligent Center Pazhou Laboratory Department of Mathematics and Statistics University of Ottawa Center for Statistics and Machine Learning Princeton University

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence,and a vital procedure in data-driven scientific research over all ***,existing methods have various limitations,especially we ak cognitive interpretability and poor computational scalability,when it comes to clustering massive datasets that are increasingly available in all ***,by simulating the multi-scale cognitive observation process of humans,we design a scalable algorithm to detect clusters hierarchically hidden in massive *** observation scale changes,following the Weber-Fechner law to capture the gradually emerging meaningful grouping *** validated our approach in real datasets with up to a billion records and 2000 dimensions,including taxi trajectories,single-cell gene expressions,face images,computer logs and *** approach outperformed popular methods in usability,efficiency,effectiveness and robustness across different domains.

关键词： massive data clustering psychological observation Weber–Fechner law cognitive interpretability computational scalability

来源：评论

学校读者我要写书评

暂无评论

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Madhusudhanan, Kiran Behrens, Gunnar Stubbemann, Maximilian Schmidt-Thieme, Lars University Of Hildesheim Information Systems and Machine Learning Lab Vwfs Data Analytics Research Center Germany Volkswagen Financial Services Ag Data Analytics & Ai Engineering United States

ISBN: (纸本)9798350362480

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer durations. Our experiments further indicate that ProbSAINT is especially accurate in instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial. © 2024 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

来源：评论

学校读者我要写书评

暂无评论

A Two-Stage Minimum Cost Multicut Approach to Self-supervised Multiple Person Tracking 15th

A Two-Stage Minimum Cost Multicut Approach to Self-supervise...

引用

15th Asian Conference on Computer Vision, ACCV 2020

作者： Ho, Kalun Kardoost, Amirhossein Pfreundt, Franz-Josef Keuper, Janis Keuper, Margret Fraunhofer Center Machine Learning Sankt Augustin Germany CC-HPC Fraunhofer ITWM Kaiserslautern Germany Data and Web Science Group University of Mannheim Mannheim Germany Institute for Machine Learning and Analytics Offenburg University Offenburg Germany

ISBN: (纸本)9783030695316

Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present a self-supervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without supervision. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an AutoEncoder to generate suitable latent representations. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features can be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking. © 2021, Springer Nature Switzerland AG.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks 38

The Map Equation Goes Neural: Mapping Network Flows with Gra...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Blöcker, Christopher Tan, Chester Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised with custom-tailored search algorithms, but often without leveraging recent advances in deep learning. Recently, first works have started incorporating such objectives into loss functions for deep graph clustering and pooling. We consider the map equation, a popular information-theoretic objective function for unsupervised community detection, and express it in differentiable tensor form for optimisation through gradient descent. Our formulation turns the map equation compatible with any neural network architecture, enables end-to-end learning, incorporates node features, and chooses the optimal number of clusters automatically, all without requiring explicit regularisation. Applied to unsupervised graph clustering tasks, we achieve competitive performance against state-of-the-art deep graph clustering baselines in synthetic and real-world datasets. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

System Architecture for Reading and Interpreting Physical Printouts of Medical Forms 22

System Architecture for Reading and Interpreting Physical Pr...

引用

22nd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2021

作者： Snegireva, Ekaterina Khazankin, Grigory R. Mikheenko, Igor Stream Data Analytics and Machine Learning Laboratory Novosibirsk State University Novosibirsk Russia Novosibirsk State University Novosibirsk Russia Meshalkin National Medical Research Center Novosibirsk Russia

ISBN: (纸本)9781665414982

This article describes the developed architecture of the system module for processing and interpreting analog medical data. Patients often undergo examinations in various medical institutions, and since their results are often handed out to the patient in printed form, the receiving institution transfers them to its database manually. There is also a tendency to completely refuse analog media and use only digital ones. But in this case, another problem appears-either loss or conversion of the accumulated analog base into digital format. These days, automatic document management systems for medical institutions-Health information systems (HIS)-are actively developing. The software module developed in accordance with the architecture described in the article can be used by developers of various HIS to automate the work with analog data. If it is necessary, it can also be freely expanded by adding new modules for working with various analog data. In this article, we take ECG scans and medical test results as examples of such data. As a result of the work undertaken the prototype of the designed system was developed and tested. © 2021 IEEE.

关键词： Medical information systems

来源：评论

学校读者我要写书评

暂无评论

Perception datasets for Anomaly Detection in Autonomous Driving: A Survey

arXiv

引用

arXiv 2023年

作者： Bogdoll, Daniel Uhlemeyer, Svenja Kowol, Kamil Zöllner, J. Marius FZI Research Center for Information Technology Germany Karlsruhe Institute of Technology Germany University of Wuppertal Germany Interdisciplinary Center for Machine Learning and Data Analytics Germany

Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance in all kinds of situations. However, these DNN are usually restricted to a closed set of semantic classes available in their training data, and are therefore unreliable when confronted with previously unseen instances. Thus, multiple perception datasets have been created for the evaluation of anomaly detection methods, which can be categorized into three groups: real anomalies in real-world, synthetic anomalies augmented into real-world and completely synthetic scenes. This survey provides a structured and, to the best of our knowledge, complete overview and comparison of perception datasets for anomaly detection in autonomous driving. Each chapter provides information about tasks and ground truth, context information, and licenses. Additionally, we discuss current weaknesses and gaps in existing datasets to underline the importance of developing further data. © 2023, CC BY.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：