—The deep-learning scaling that drove the past decade of AI is colliding with hard limits in energy, data, robustness, and explainability, just as emerging applications demand the capabilities neural networks lack: reasoning, abstraction, and collaboration. The CoCoSys JUMP 2.0 center confronts this by co-designing cog...
Zi-Shen Wan, Yu Cao, S. K. Gupta et al.· IEEE Micro· 0 citations
Hyperdimensional computing (HDC) is attractive for efficient and robust learning, but conventional inference still encodes every query independently, repeatedly paying the cost of high-dimensional projection. We introduce SupHDC, a new inference paradigm that processes multiple queries through a shared encoding computa...
Quan-Ling Zhao, N. Pandey, Ye Tian et al.· 0 citations
The Superposed Latent Autoencoder (SLAE) is introduced, which preserves high-capacity latent representations while sharing storage through learned superposition, and suggests a new principle for representation compression: instead of making every latent smaller, keep representations wide and let them share memory.
Quanling Zhao, Jia-Ying Yang, Tianqi Zhang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
