Deep neural networks have shown incredible performance for inference tasks in a variety of domains, but require significant storage space, which limits scaling and use on-device intelligence. This paper is concerned with finding universal lossless compressed representations deep feedforward synaptic weights drawn from discrete sets, directly performing without full decompression. The basic insi...