Dynamics as Code: On Model Compression via Dynamic System
The paper proposes model compression by encoding high-dimensional neural network weights as indices of trajectories generated by dynamic systems. This method is distinct from traditional techniques like pruning or quantization, offering a principled way to represent parameters compactly. Theoretical proofs show that finite trajectories can effectively approximate the weight space, linking state resolution directly to decompression error and compression ratio.