Results 81 to 90 of about 339,627 (329)
Catastrophic Importance of Catastrophic Forgetting
, 2018 This paper describes some of the possibilities of artificial neural networks that open up after solving the problem of catastrophic forgetting. A simple model and reinforcement learning applications of existing methods are also proposed.
openaire +2 more sources
The Non‐Professional Virtues of the Hospice Volunteer
Journal of Applied Philosophy, EarlyView.ABSTRACT Volunteers have long played a significant role in hospice care. Much of the care volunteers provide consists of weekly hour‐long in‐home visits. Home‐visiting hospice volunteers are not professionals, nor are they strangers or intimates. Hospice volunteers will not typically face moral dilemmas, nor be called upon to make dramatic decisions ...
Michael B. Gill
wiley +1 more source
Pseudorehearsal in value function approximation
, 2017 Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Q-learning with function approximation in a pole ...
A Robins +16 more
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Solutions to the Catastrophic Forgetting Problem [PDF]
, 2022 In this paper we review three kinds of proposed solutions to the catastrophic forgetting problem in neural networks. The solutions are based on reducing hidden unit overlap, rehearsal, and pseudorehearsal mechanisms. We compare the methods and identify some underlying similarities.
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Acting authentically: Using play to cultivate authentic interrelating in role performance
Journal of Organizational Behavior, EarlyView.Summary Research is increasingly demonstrating that authenticity and human connection are fundamental and interrelated human needs. However, organizational roles often constrain authenticity and connection in workplace interactions, especially roles that are highly scripted.
Lyndon E. Garrett
wiley +1 more source
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting [PDF]
, 2018 We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks. The method is grounded in a Bayesian online learning framework, where we recursively approximate the posterior after every task ...
Barber, David +2 more
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Strategic Change, EarlyView.ABSTRACT Digital platform (DP) enterprises have risen to the top of the global economy by inverting traditional business models. They earn money through matchmaking, transaction facilitation, and efficient orchestration of other stakeholders' resources.
Lukas R. G. Fitz, Jochen Scheeg
wiley +1 more source
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced\n Training for Neural Machine Translation [PDF]
, 2022 Chenze Shao, Yan Feng
openalex +1 more source
Benchmarking Large Language Models for Polymer Property Predictions
Macromolecular Rapid Communications, EarlyView.Large language models (LLMs) are fine‐tuned on polymer thermal property datasets to directly predict glass transition, melting, and decomposition temperatures from SMILES inputs. Compared to state‐of‐the‐art models such as Polymer Genome, polyGNN, and polyBERT, LLMs achieve competitive yet lower accuracy.
Sonakshi Gupta +3 more
wiley +1 more source
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
, 2021 In lifelong learning systems, especially those based on artificial neural networks, one of the biggest obstacles is the severe inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting.
Giles, C. Lee +3 more
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