Abstract: An agnostic combinatorial knowledge distillation (CKD) method for transferring trained knowledge of neural model from a complex model (teacher) to a less complex model (student) is described. In addition to training the student to generate a final output that approximates both the teacher's final output and a ground truth of a training input, the method further maximizes knowledge transfer by training hidden layers of the student to generate outputs that approximate a representation of a subset of teacher hidden layers are mapped to each of the student hidden layers for a given training input.