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AssigneeF-Enterprises GmbH
InventorsEduard Weinwurm, Jürgen Firsching
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Method and apparatus for training an artificial neural network circuit. In some embodiments, a set of possible inputs to the artificial neural network is identified. A first similarity measure between each of the possible inputs and a known output relevant to a task for which the artificial neural network is to be trained is generated. A second similarity measure is subsequently generated based on the first similarity measure. A set of relevant inputs from the set of possible inputs is selected based on the second similarity measure, and the set of relevant inputs is used to train the artificial neural network. The first and second similarity measures may be generated using a cosine similarity function based on individual inputs from the set of possible inputs. A sorting function can be used based on magnitude of a combined similarity function to select those relevant inputs above a selected threshold.
A method for training an artificial neural network, comprising:
An apparatus, comprising:
A front end logic circuit configured to train an artificial neural network, the front end logic circuit comprising at least one processing circuit configured to use a cosine similarity function to generate, for each of a plurality of possible inputs, a first similarity measure between the possible input and a known output relevant to a task for which the artificial neural network logic circuit is to be trained, to derive a second similarity measure based on the first similarity measure, to select a set of relevant inputs from the set of possible inputs based on the second similarity measure;
Dependencies are inferred from the claim text.