NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks скачать в хорошем качестве

NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks

Guest speaker Burak Gurbuz talked about his recent work with Constantine Dovrolis that was presented in ICML 2022: “NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks.” He started the presentation with an overview of the biological aspects of continual learning, then introduced NISPA and shared experimental results. Paper: https://arxiv.org/abs/2206.09117 - - - - Numenta has developed breakthrough advances in AI technology that enable customers to achieve 10-100X improvement in performance across broad use cases, such as natural language processing and computer vision. Backed by two decades of neuroscience research, we developed a framework for intelligence called The Thousand Brains Theory. By leveraging these discoveries and applying them to AI systems, we’re able to deliver extreme performance improvements and unlock new capabilities. Subscribe to our News Digest for the latest news about neuroscience and artificial intelligence: https://numenta.com/news-digest/ Subscribe to our Newsletter for the latest Numenta updates: https://tinyurl.com/NumentaNewsletter Our Social Media:   / numenta     / officialnumenta     / numenta   Our Open Source Resources: https://github.com/numenta https://discourse.numenta.org/ Our Website: https://numenta.com/