Search Results - Theory of computation → Unsupervised learning and clustering

Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining by Shen, Weijun, Li, Yanhui, Chen, Lin, Han, Yuanlei, Zhou, Yuming, Xu, Baowen

“…With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models…”
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Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System by Huang, Jun, Yang, Yang, Yu, Hang, Li, Jianguo, Zheng, Xiao

“… opportunities for further performance gain. As a fresh attempt, we propose in this paper a semi-supervised graph-based anomaly detection method, MSTGAD, which seamlessly integrates all available data modalities via attentive multi-modal learning…”
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Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders by Ludmir, Jason Zev, Rebello, Sophia, Ruiz, Jacob, Patel, Tirthak

“… to the difficulty of gradient calculation. The challenge is even greater for anomaly detection, where unsupervised learning methods are essential to ensure practical applicability…”
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AutoConf: Automated Configuration of Unsupervised Learning Systems Using Metamorphic Testing and Bayesian Optimization by Shar, Lwin Khin, Goknil, Arda, Husom, Erik Johannes, Sen, Sagar, Tun, Yan Naing, Kim, Kisub

“…Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets…”
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A Needle is an Outlier in a Haystack: Hunting Malicious PyPI Packages with Code Clustering by Liang, Wentao, Ling, Xiang, Wu, Jingzheng, Luo, Tianyue, Wu, Yanjun

“…As the most popular Python software repository, PyPI has become an indispensable part of the Python ecosystem. Regrettably, the open nature of PyPI exposes…”
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Quantum Spectral Clustering of Mixed Graphs by Volya, Daniel, Mishra, Prabhat

“… In a case study, we apply our proposed algorithm to preform unsupervised machine learning…”
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Local Higher-Order Graph Clustering by Yin, Hao, Benson, Austin R, Leskovec, Jure, Gleich, David F

“…Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph…”
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Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation by Cheng, Jinglei, Zhou, Ruilin, Gan, Yuhang, Qian, Chen, Liu, Junyu

“…We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the…”
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CND-IDS: Continual Novelty Detection for Intrusion Detection Systems by Fuhrman, Sean, Gungor, Onat, Rosing, Tajana

“…Intrusion detection systems (IDS) play a crucial role in IoT and network security by monitoring system data and alerting to suspicious activities. Machine learning (ML…”
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Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems by Liu, Jinyang, Jiang, Zhihan, Gu, Jiazhen, Huang, Junjie, Chen, Zhuangbin, Feng, Cong, Yang, Zengyin, Yang, Yongqiang, Lyu, Michael R.

“… Motivated by these findings, we formulate the identification of functional clusters as a clustering problem and propose…”
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Subresolution Assist Feature Insertion by Variational Adversarial Active Learning and Clustering with Data Point Retrieval by 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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Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space by Cheng, Lixue, Sun, Jiace, Miller, Thomas F

“…We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML…”
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UDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing by Husom, Erik Johannes, Tverdal, Simeon, Goknil, Arda, Sen, Sagar

“…Manufacturing has enabled the mechanized mass production of the same (or similar) products by replacing craftsmen with assembly lines of machines. The quality…”
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MixQ: Taming Dynamic Outliers in Mixed-Precision Quantization by Online Prediction by Chen, Yidong, Zhang, Chen, Dong, Rongchao, Zhang, Haoyuan, Zhang, Yonghua, Lu, Zhonghua, Zhai, Jidong

“…Mixed-precision quantization has shown to be a promising method for enhancing the efficiency of LLMs. This technique boosts computational efficiency by…”
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Change Detection for Constantly Maintaining Up-to-date Metaverse Maps by Matsubara, Tomoya, Sugimoto, Maki, Saito, Hideo

“…Metaverse has been attracting more and more attention because of its potential for various use cases. In metaverse applications, the seamless integration of…”
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TopSelect: A Topology-based Feature Selection Method for Industrial Machine Learning by Abukwaik, Hadil, Sula, Lefter, Rodriguez, Pablo

“…Building robust industrial machine learning (ML) models requires incorporating domain knowledge in feature selection…”
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Can a Phone Hear the Shape of a Room? by Shih, Oliver, Rowe, Anthony

“…Understanding the location of acoustically reflective surfaces in a room is a critical component in advanced sound processing. For example, intelligent…”
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An Iterative Distance-Based Model for Unsupervised Weighted Rank Aggregation by Akritidis, Leonidas, Fevgas, Athanasios, Bozanis, Panayiotis

“… In this article, we introduce an unsupervised algorithm for learning the weights of the voters for a specific topic or query…”
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Is Unsupervised Clustering Somehow Truer?: Is Unsupervised Clustering Somehow Truer? by Søgaard, Anders

“… Some philosophers have argued that the use of unsupervised clustering algorithms is more justified than the use of supervised classification, because supervised classification is more biased, and because (parametric…”
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A Parallel Network Community Detection Algorithm Based on Distance Dynamics by Wu, Bin, Zhang, Cuiyun, Guo, Qian

“…In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing…”
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