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28
results.
Updated on
21/01/2026 [07:35:00]
Results 25 to 28 of 28
Absstract of: US2025390106A1
An embodiment relates to a robot executing a social-friendly navigation algorithm. The robot may include a communication unit, an input unit, a driving unit configured to move the robot, a memory, and at least one processor connected to the memory and configured to execute computer-readable instructions stored in the memory. By performing neural network computation using a separate processor and utilizing multiple processors in parallel, device efficiency may be improved.
Absstract of: US2025390532A1
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for receiving a query relating to a data item that includes multiple data item samples and processing the query and the data item to generate a response to the query. In particular, the described techniques include adaptively selecting a subset of the data item samples using a selection neural network conditioned on features of the data item samples and the query. Then processing the subset and query using a downstream task neural network to generate a response to the query. By adaptively selecting the subset of data item samples according to the query, the described techniques generate responses to queries that are more accurate and require less computation resources than would be the case using other techniques.
Nº publicación: US2025390745A1 25/12/2025
Applicant:
MICROSOFT TECH LICENSING LLC [US]
Microsoft Technology Licensing, LLC
Absstract of: US2025390745A1
Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.
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