The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels.

Numenta has achieved greater than 100x performance improvements on inference tasks in deep learning networks without loss of accuracy.

Recently, OpenAI has improved the development method of sparse autoencoders and succeeded in creating a sparse autoencoder that can support GPT-4 and GPT-2 small.

REDWOOD CITY, Calif., May 21, 2021 — Numenta, Inc. announced it has achieved greater than 100x performance improvements on inference tasks in deep learning networks without any loss in accuracy. In a ...

Numenta announced a technology demonstration showing their brain-derived sparse networks perform inference tasks 50x faster than dense networks.

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...