Results 81 to 90 of about 337,942 (263)

PS-SNN: pattern separation learning for expandable spiking neural networks in class-incremental learning

Scientific Reports
Biological brains mitigate interference by orthogonalizing neural representations of similar memories, thereby preserving stability across tasks in continual learning. However, most existing continual learning approaches for spiking neural networks (SNNs)
Ke Hu, Liangsheng Wen, Tingting Zhang, Hao Zhang   +3 more
doaj   +1 more source

Feature-Generation-Replay Continual Learning Combined with Mixture-of-Experts for Data-Driven Autonomous Guidance

Drones
Continual learning (CL) is a key technology for enabling data-driven autonomous guidance systems to operate stably and persistently in complex and dynamic environments.
Bowen Li, Junxiang Li, Hongji Cheng, Tao Wu, Binhan Du   +4 more
doaj   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Bias Calibration for Semi-Supervised Continual Learning

Sensors
In sensor-centric fields like healthcare, environmental monitoring, and industry, image classification is key to turning visual sensor data into actionable insights.
Zhong Ji, Zhanyu Jiao, Deyu Miao, Chen Tang   +3 more
doaj   +1 more source

Bilevel Continual Learning

, 2020
Continual learning aims to learn continuously from a stream of tasks and data in an online-learning fashion, being capable of exploiting what was learned previously to improve current and future tasks while still being able to perform well on the previous tasks.
Pham, Quang, Sahoo, Doyen, Liu, Chenghao, Hoi, Steven C. H   +3 more
openaire   +2 more sources

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley   +1 more source

Opportunistic Dynamic Architecture for Class-Incremental Learning

IEEE Access
Continual learning has attracted increasing attention over the last few years, as it enables to continually learn new tasks over time, which has significant implication to many real-world applications.
Fahrurrozi Rahman, Andrea Rosales Sanabria, Juan Ye   +2 more
doaj   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Hydration Behavior of Tricalcium Silicate in Seawater Relevant Salt Systems: A Hybrid Study with the Aid of Machine Learning

Advanced Intelligent Discovery, EarlyView.
The hydration behavior of C3S in seawater‐relevant solutions is studied based on experiments, boundary nucleation and growth (BNG) modeling, and machine learning. The main ions included in seawater modify hydration mechanisms, with MgCl2 showing the strongest acceleration effect at the same concentration.
Yanjie Sun, Chen Zhang, Ting Zhang, Mengze Hong, Peiliang Shen, Chi Sun Poon, Gang Li   +6 more
wiley   +1 more source