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Resolving the Stability-Plasticity Dilemma in RL via Complementary Continual Critics Neural networks have shown remarkable success in supervised learning when trained on a single task Official Presentation Video of the CVPR 2023 paper "Achieving a Better Okay uh hi everyone i'm Shabbh uh I'll talk about our work reinitializing weights versus units for maintaining Guest speaker Burak Gurbuz talked about his recent work with Constantine Dovrolis that was presented in ICML 2022: “NISPA: ... Abstract: Any learning system worthy of the name must continue to learn indefinitely. Unfortunately, our most advanced ...
... to consider and then I will you know join them at some point so the so loss of Winter Conference on Applications of Computer Vision (WACV), 2026 More Than Memory Savings: Zeroth-Order Optimization ... Abstract: Neural networks present a highly expressive and adaptable model class. However, recent works have shown that this ... Paper: Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of In the neocortex, synaptic inhibition shapes all forms of spontaneous and sensory evoked activity. Importantly, inhibitory ... Here we introduce dynamic programming, which is a cornerstone of model-based reinforcement learning. We demonstrate ...
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In this AI Research Roundup episode, Alex discusses the paper: 'KL-Regularized Reinforcement Learning is Designed to Mode ... In this video, we continue our journey into dynamic programming in reinforcement learning with our first algorithm — Policy ...
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Mansi Maheshwari - Addressing the Plasticity Stability Dilemma in Reinforcement Learning
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Last Updated: May 22, 2026
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