Results 91 to 100 of about 339,627 (329)

Wide Neural Networks Forget Less Catastrophically

, 2021
ICML ...
Mirzadeh, Seyed Iman, Chaudhry, Arslan, Yin, Dong, Hu, Huiyi, Pascanu, Razvan, Gorur, Dilan, Farajtabar, Mehrdad   +6 more
openaire   +2 more sources

Deep Reinforcement Learning‐Based Control for Real‐Time Hybrid Simulation of Civil Structures

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT Real‐time Hybrid Simulation (RTHS) is a cyber‐physical technique that studies the dynamic behavior of a system by combining physical and numerical components that are coupled through a boundary condition enforcer. In structural engineering, the numerical components are subjected to environmental loads that become dynamic displacements of the ...
Andrés Felipe Niño, Alejandro Palacio‐Betancur, Piedad Miranda‐Chiquito, Juan David Amaya, Christian E. Silva, Mariantonieta Gutierrez Soto, Luis Felipe Giraldo   +6 more
wiley   +1 more source

A multifidelity approach to continual learning for physical systems

Machine Learning: Science and Technology
We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting ...
Amanda Howard, Yucheng Fu, Panos Stinis
doaj   +1 more source

Remembering for the right reasons: Explanations reduce catastrophic forgetting

Applied AI Letters, 2021
The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on ...
Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, Trevor Darrell   +6 more
doaj   +1 more source

Handling Catastrophic Forgetting: Online Continual Learning for Next Activity Prediction

Predictive business process monitoring focuses on predicting future process trajectories, including next-activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts.
Tamara Verbeek, Marwan Hassani
openaire   +2 more sources

Overcoming Catastrophic Forgetting by Generative Regularization

, 2019
In this paper, we propose a new method to overcome catastrophic forgetting by adding generative regularization to Bayesian inference framework. Bayesian method provides a general framework for continual learning. We could further construct a generative regularization term for all given classification models by leveraging energy-based models and ...
Chen, Patrick H., Wei, Wei, Hsieh, Cho-jui, Dai, Bo   +3 more
openaire   +2 more sources

Sliding Doors: Frame Uptake and Rejection by Learners in a Museum‐Based Climate Learning Experience

Science Education, EarlyView.
ABSTRACT Science education efforts that support public understanding of modern climate change are critically needed. However, implementing climate‐related learning experiences can be challenging, as public audiences tend to experience a wide range of understandings of and emotions around the issue. In light of these challenges, many scholars have posed
Lynne Zummo, Benjamin Janney, Kaitlyn Kinshella, Jordan Giron, Marc Whiting, Ajla Auker, Hailey Sherman, Monika Lohani   +7 more
wiley   +1 more source

EPicker is an exemplar-based continual learning approach for knowledge accumulation in cryoEM particle picking

Nature Communications, 2022
Many existing deep learning algorithms for particle picking are not predictable on unseen datasets. Here the authors report an exemplar-based continual learning approach, EPicker, enabling accumulation of new knowledge of cryoEM particle picking without ...
Xinyu Zhang, Tianfang Zhao, Jiansheng Chen, Yuan Shen, Xueming Li   +4 more
doaj   +1 more source

Overcoming Catastrophic Forgetting by Neuron-Level Plasticity Control

Proceedings of the AAAI Conference on Artificial Intelligence, 2020
To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning a new task, the proposed method preserves the existing knowledge from the previous tasks by controlling the plasticity of the network at the neuron level.
Paik, Inyoung, Oh, Sangjun, Kwak, Tae-Yeong, Kim, Injung   +3 more
openaire   +3 more sources

“Are We Watching the Same Video?”: On the Definition of the Situation and Audience Sense‐Making on Social Media about the Sexual Abuse Allegations Against Marilyn Manson

Symbolic Interaction, EarlyView.
How situations are defined is a social process. This paper examines how users on YouTube make sense of the alleged sexual assault perpetrated by shock rocker Marilyn Manson in the 2007 “Heart Shaped‐Glasses (When the Heart Guides the Hand)” music video.
Stacey Hannem, Christopher J. Schneider
wiley   +1 more source