Knowledge-Guided ML

ML: Machine Learning

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8 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Minimum-Sufficient Knowledge and Computation for Wind Energy Predictive Analysis
Nov 5, 2026Purbak Sengupta, Aparna S. varde, Emil Jørgensen NjorKnowledge-Guided ML Cost-Aware Inference
SDU Center for Software Technology University of Southern Denmark (SDU) Vejle, Denmark
PhysioAI: Clinical Knowledge-Guided Semantic Supervision for Skeleton-Based Physiotherapy Action Recognition
Sep 11, 2026Jie Cao, Euijoon Ahn, Anwar Hassan +1Skeleton-Based Action Recognition Human Activity Recognition
The University of Sydney, Australia · James Cook University, Australia · Nepean Hospital, Australia
Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition
Jul 1, 2026Wenting Ma, Zhipeng Zhang, Xiaohang Yuan +6Electrocardiography Knowledge-Guided ML
China Mobile Research Institute, Beijing, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou, China · Beijing Jiaotong University, Beijing, China +2
Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
Jun 5, 2026Ravisha Rupasinghe, Rajith Vidanaarachchi, Asela Hevapathige +3Knowledge-Guided ML Physics-Informed ML
AI, Optimisation and Pattern Recognition Lab, Department of Mechanical Engineering, University of Melbourne, Melbourne, Victoria, Australia · Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
Auditing and Fixing Economic Validity in Tabular Foundation Models for Discrete Choice
May 26, 2026Yingshuo Wang, Xian Sun, Yanhang Li +2Tabular Foundation Models Monotonicity Constraints
University of California, Berkeley, CA, USA · Duke University, Durham, NC, USA · Northeastern University, Boston, MA, USA +2
Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
Apr 28, 2026Yuting Yang, Gang Mei, Feng Chen +2Knowledge-Guided ML Landslide Susceptibility Mapping
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China · School of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710064, China
From Specification to Architecture: A Theory Compiler for Knowledge-Guided Machine Learning
Mar 15, 2026Asela Hevapathige, Yu Xia, Sachith Seneviratne +1Knowledge-Guided ML Scientific ML
AI, Optimization and Pattern Recognition Research Group, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Australia
Knowledge-informed Bidding with Dual-process Control for Online Advertising
Mar 5, 2026Huixiang Luo, Longyu Gao, Yaqi Liu +3Knowledge-Guided ML Automated Bidding
Alibaba Health, Hangzhou, China · Alibaba Health, Beijing, China

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Minimum-Sufficient Knowledge and Computation for Wind Energy Predictive Analysis

Purbak Sengupta, Aparna S. varde, Emil Jørgensen Njor

SDU Center for Software Technology University of Southern Denmark (SDU) Vejle, Denmark

Knowledge-Guided ML Cost-Aware Inference

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Resource-constrained cyber-physical decision systems often apply fixed sensing, knowledge, and computational pipelines despite state-dependent information needs. We introduce Minimum-Sufficient Knowledge and Computation (MSKC), a resource-aware computing principle: select the least-costly route meeting decision-risk and deployment constraints, escalating only when cheaper routes are insufficient. As proof of concept, we isolate adaptive knowledge routing, the decision underlying Knowledge-Guided Machine Learning (KGML), which conventionally injects external knowledge unconditionally rather than only where needed. We instantiate this for wind ramp-event predictive analysis, an edge-oriented decision-support task, evaluating local, relational, and physics-informed routes with a state-aware gate on publicly available real data from two wind farms. We establish conditions for feasibility preservation, cost dominance, and strict expected savings over always-on routing. At a 35.9% relational-knowledge injection rate, adaptive routing is statistically indistinguishable from always-on injection on the reported metrics while reducing knowledge calls by 64%, supporting sustainable, Green-IT-aligned deployment. These results support MSKC's premise that uniformly maximal intelligence is unnecessary. This offers intelligent data analysis for big data streams: the analysis itself decides, per state, how much knowledge and computation the data warrant.