The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end of 2020, that put the work on other researchers’ radar. In the intervening time, the paper’s authors have presented the work to a wider audience through a series of lectures.
Ramin Hasani’s TEDx talk at MIT is one of the best examples. Hasani is the Principal AI and Machine Learning Scientist at the Vanguard Group and a Research Affiliate at CSAIL MIT, and served as the paper’s lead author.
“These are neural networks that can stay adaptable, even after training,” Hasani says in the video, which appeared online in January. When you train these neural networks, they can still adapt themselves based on the incoming inputs that they receive.”
The “liquid” bit is a reference to the flexibility/ad...
