Oscillator associative memories for high-capacity, compositional inference

A continuous-time oscillator network implements high-capacity associative memory for factorizing compositional representations. This talk studies applications to perceptual inference and combinatorial problems such as subset sum, compares factorization performance with existing approaches, and analyses convergence to explain the network's capacity advantages and efficient implementation.