Suchergebnisse - Computing methodologies Machine learning Machine learning approaches Rule learning

FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization von Zhou, Yifei, Huang, Xuchu, Ni, Chenyu, Zhou, Min, Yan, Zheyu, Yin, Xunzhao, Zhuo, Cheng

“… Hyperdimensional Computing (HDC), a promising braininspired computational model, is integral to neuro-symbolic AI …”
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Automatic Extraction of Cause-Effect-Relations from Requirements Artifacts von Frattini, Julian, Junker, Maximilian, Unterkalmsteiner, Michael, Mendez, Daniel

“… Background: The detection and extraction of causality from natural language sentences have shown great potential in various fields of application. The field of …”
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Mining exception-handling rules as sequence association rules von Thummalapenta, Suresh, Xie, Tao

“… To address this issue, we develop a novel approach that mines exception-handling rules as sequence association rules of the form “(FC1c1…FCcn) ∧ FCa ⇒ (FCe1…FCem …”
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Modeling Adaptive Learning Agents for Domain Knowledge Transfer von Hoser, Moritz

“… This paper states the thesis that when learning agents are applied to work environments that require domain-specific experience, the agent benefits if it can be further adapted by a supervising domain expert …”
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Coordinate Systems: Level Ascending Ontological Options von Partridge, Chris, Mitchell, Andrew, Loneragan, Michael, Atkinson, Hayden, de Cesare, Sergio, Khan, Mesbah

“… We are involved in a proof of concept project that addresses this challenge through a foundational conceptual model using a constructional approach based upon the BORO Foundational Ontology …”
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Improving Validity of Cause of Death on Death Certificates von Hoffman, Ryan A, Venugopalan, Janani, Qu, Li, Wu, Hang, Wang, May D

“… such as Center for disease prevention and control (CDC). In this study, we utilize knowledge from publicly available expert-formulated rules for the cause of death …”
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BDefects4NN: A Backdoor Defect Database for Controlled Localization Studies in Neural Networks von Xiao, Yisong, Liu, Aishan, Zhang, Xinwei, Zhang, Tianyuan, Li, Tianlin, Liang, Siyuan, Liu, Xianglong, Liu, Yang, Tao, Dacheng

“… Pre-trained large deep learning models are now serving as the dominant component for downstream middleware users and have revolutionized the learning paradigm, replacing the traditional approach …”
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Subresolution Assist Feature Insertion by Variational Adversarial Active Learning and Clustering with Data Point Retrieval von Tseng, Sean Shang-En, Jiang, Iris Hui-Ru, Shiely, James P.

“… Thus, state-of-the-art works resort to machine learning to reduce runtime but require abundant training samples to generalize the trained models and achieve high performance …”
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EImprove - Optimizing Energy and Comfort in Buildings based on Formal Semantics and Reinforcement Learning von Verma, Sagar, Agrawal, Supriya, Venkatesh, R, Shrotri, Ulka, Nagarathinam, Srinarayana, Jayaprakash, Rajesh, Dutta, Aabriti

“… The control logic is usually specified as 'if-then-that-else' rules that capture the domain expertise of HVAC operators, but they often have conflicts that may lead to sub-optimal HVAC performance …”
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In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor von Shrestha, Amar, Fang, Haowen, Rider, Daniel Patrick, Mei, Zaidao, Qiu, Qinru

“… Although widely used in machine learning, backpropagation cannot directly be applied to SNN training and is not feasible on a neuromorphic processor that emulates biological neuron and synapses …”
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DiffPattern: Layout Pattern Generation via Discrete Diffusion von Wang, Zixiao, Shen, Yunheng, Zhao, Wenqian, Bai, Yang, Chen, Guojin, Farnia, Farzan, Yu, Bei

“… Then a white-box pattern assessment is utilized to generate legal patterns given desired design rules …”
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Search-Based LLMs for Code Optimization von Gao, Shuzheng, Gao, Cuiyun, Gu, Wenchao, Lyu, Michael R.

“… Early research in code optimization employs rule-based methods and focuses on specific inefficiency issues, which are labor-intensive and suffer from the low coverage issue …”
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ExplainExplore: Visual Exploration of Machine Learning Explanations von Collaris, Dennis, van Wijk, Jarke J.

“… Machine learning models often exhibit complex behavior that is difficult to understand …”
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NeurFill: Migrating Full-Chip CMP Simulators to Neural Networks for Model-Based Dummy Filling Synthesis von Cai, Junzhe, Yan, Changhao, Ma, Yuzhe, Yu, Bei, Zhou, Dian, Zeng, Xuan

“… The experimental results show that the proposed NeurFill outperforms existing rule- and model-based methods …”
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FedEDA: Federated Learning Framework for Privacy-Preserving Machine Learning in EDA von Kim, Joonseok, Kim, Donggyu, Park, Seonghyeon, Kang, Seokhyeong

“… ). As a key approach to overcome these challenges, Machine Learning (ML) techniques have been widely studied in the field of EDA …”
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Late Breaking Results From Hybrid Design Automation for Field-coupled Nanotechnologies von Hofmann, Simon, Walter, Marcel, Servadei, Lorenzo, Wille, Robert

“… In an attempt to balance scalability and quality, we created and evaluated a hybrid approach that combines the best of established design methods and deep reinforcement learning …”
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Recommending New Tags Using Domain-Ontologies von Baruzzo, Andrea, Dattolo, Antonina, Pudota, Nirmal, Tasso, Carlo

“… In this paper, we propose an automated approach for recommending new tags for Web resources by using domain ontologies and key-phrases …”
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Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification von D’Angelo, Gianni, Rampone, Salvatore, Palmieri, Francesco

“… Pervasive computing is one of the latest and more advanced paradigms currently available in the computers arena …”
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Multi-model Ontology-Based Hybrid Recommender System in E-learning Domain von Zhuhadar, Leyla, Nasraoui, Olfa, Wyatt, Robert, Romero, Elizabeth

“… This paper introduces a multi-model ontology-based framework for semantic search of educational content in E-learning repository of courses, lectures, multimedia resources, etc …”
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SSDL-ILT: Efficient ILT utilizing a self-supervised deep learning model von Xu, Rui, Yang, Junqi, Jiang, Haoxiang, Fang, Ming

“… However, application of ILT is hindered by time-intensive physical simulation. Herein, we propose an efficient ILT algorithm leveraging a deep learning model …”
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