Methods and systems for training a neural network include pre-training a bi-linear, tensor-based network, separately pre-training an auto-encoder, and training the bi-linear, tensor-based network and auto-encoder jointly. Pre-training the bi-linear, tensor-based network includes calculating high-order interactions between an input and a transformation to determine a preliminary network output and minimizing a loss function to pre-train network parameters. Pre-training the auto-encoder includes calculating high-order interactions of a corrupted real network output, determining an auto-encoder output using high-order interactions of the corrupted real network output, and minimizing a loss function to pre-train auto-encoder parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of training a neural network, comprising: pre-training a bi-linear, tensor-based network by: calculating high-order interactions between an input and a transformation to determine a preliminary network output; and minimizing a loss function to pre-train network parameters; separately pre-training an auto-encoder by: calculating high-order interactions of a corrupted real network output; determining an auto-encoder output using high-order interactions of the corrupted real network output; and minimizing a loss function to pre-train auto-encoder parameters; and training the bi-linear, tensor based network and auto-encoder jointly. 2. The method of claim 1, wherein pre-training the bi-linear, tensor-based network further comprises: applying a nonlinear transformation to an input; calculating high-order interactions between the input and the transformed input to determine a representation vector; applying the non-linear transformation to the representation vector; and calculating high-order interactions between the representation vector and the transformed representation vector to determine a preliminary output. 3. The method of claim 1, further comprising perturbing a portion of training data to produce the corrupted real network output. 4. The method of claim 1, wherein minimizing the loss function comprises gradient-based optimization. 5. The method of claim 1, wherein determining the auto-encoder output comprises reconstructing true labels from the corrupted real network output. 6. A system for training a neural network, comprising: a pre-training module, comprising a processor, configured to separately pre-train a bi-linear, tensor-based network, and to pre-train an auto-encoder to reconstruct true labels from corrupted real network outputs; and a training module configured to jointly train the bi-linear, tensor-based network and the auto-encoder. 7. The system of claim 6, wherein the pre-training module is further configured to calculate high-order interactions between an input and a transformation to determine a preliminary network output, and to minimize a loss function to pre-train network parameters to pre-train the bi-linear, tensor-based network. 8. The system of claim 7, wherein the pre-training module is further configured to apply a nonlinear transformation to an input, to calculate high-order interactions between the input and the transformed input to determine a representation vector, to apply the non-linear transformation to the representation vector, and to calculate high-order interactions between the representation vector and the transformed representation vector to determine a preliminary output. 9. The system of claim 7, wherein the pre-training module is further configured to use gradient-based optimization to minimize the loss function. 10. The system of claim 6, wherein the pre-training module is further configured to calculate high-order interactions of a corrupted real network output, to determine an auto-encoder output using high-order interactions of the corrupted real network output, and to minimize a loss function to pre-train auto-encoder parameters. 11. The system of claim 6, wherein the pre-training module is further configured to perturb a portion of training data to produce the corrupted real network output.