👉 Fluid is a neural network architecture developed by Liquid AI that employs a unique composition of computational units, including convolutions, multi-layer perceptrons, and recurrent layers, all interconnected in a flexible manner. During training, the network learns to dynamically adjust the connections between these units based on the input data, allowing it to adapt its internal structure to capture complex patterns and relationships. This fluidity in architecture enables Liquid Neural Networks to efficiently model sequential data, such as time series or signals, by leveraging a custom design space that includes both continuous-time dynamics and discrete-time operations. The calculations in Fluid are performed using a combination of these elements, allowing for effective and efficient processing of various types of data, making it particularly suitable for applications requiring real-time or low-latency responses.
