System and method for hyper-parameter analysis for multi-layer computational structures
First Claim
1. A computer-implemented method for analyzing hyper-parameters of a multi-layer computational structure comprising:
- accessing, using at least one processor, input data for recognition, wherein the input data includes at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set;
processing the input data using one or more layers of the multi-layer computational structure;
performing matrix factorization of the one or more layers of the multi-layer computational structure;
analyzing one or more hyper-parameters of the one or more layers based upon, at least in part, the matrix factorization of the one or more layers;
training one or more filters of the one or more layers; and
converting one or more trained filters of the one or more layers to a plurality vectors.
2 Assignments
0 Petitions
Accused Products
Abstract
The present disclosure relates to a computer-implemented method for analyzing one or more hyper-parameters for a multi-layer computational structure. The method may include accessing, using at least one processor, input data for recognition. The input data may include at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set. The method may further include processing the input data using one or more layers of the multi-layer computational structure and performing matrix factorization of the one or more layers. The method may also include analyzing one or more hyper-parameters for the one or more layers based upon, at least in part, the matrix factorization of the one or more layers.
-
Citations
46 Claims
-
1. A computer-implemented method for analyzing hyper-parameters of a multi-layer computational structure comprising:
-
accessing, using at least one processor, input data for recognition, wherein the input data includes at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set; processing the input data using one or more layers of the multi-layer computational structure; performing matrix factorization of the one or more layers of the multi-layer computational structure; analyzing one or more hyper-parameters of the one or more layers based upon, at least in part, the matrix factorization of the one or more layers; training one or more filters of the one or more layers; and converting one or more trained filters of the one or more layers to a plurality vectors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
-
-
16. A system for analyzing the optimal number of feature maps for a multi-layer computational structure comprising:
a computing device having at least one processor configured to receive input data for recognition, wherein the input data includes at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set, the at least one processor further configured to process the input data using one or more layers of the multi-layer computational structure, the at least one processor further configured to perform matrix factorization of the one or more layers, and the at least one processor further configured to analyze one or more hyper-parameters for the one or more layers based upon, at least in part, the matrix factorization of the one or more layers, wherein the at least one processor is further configured to train one or more filters from the one or more layers and wherein the at least one processor is further configured to retrain the one or more filters of the one or more layers, based upon, at least in part, the analyzing of the one or more hyper-parameters of the one or more layers. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
-
31. A non-transitory computer-readable storage medium for analyzing hyper-parameters of a multi-layer computational structure, the computer-readable storage medium having stored thereon instructions that when executed by a machine result in the following operations:
-
accessing input data for recognition, wherein the input data includes at least one of an image, a pattern, a speech input, a natural language input, a video input, and a complex data set; processing the input data using one or more layers of the multi-layer computational structure; performing matrix factorization of the one or more layers; analyzing one or more hyper-parameters of the one or more layers based upon, at least in part, the matrix factorization of the one or more layers; and wherein the multi-layer computational structure includes a plurality of hybrid layers wherein the feature maps of each of the plurality of hybrid layers is associated with one or more different feature maps of one or more previous layers. - View Dependent Claims (32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46)
-
Specification