Resumen de: 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.
Resumen de: 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.
Nº publicación: US2025390717A1 25/12/2025
Solicitante:
NIPPON TELEGRAPH AND TELEPHONE CORP [JP]
NIPPON TELEGRAPH AND TELEPHONE CORPORATION
Resumen de: US2025390717A1
An inference processing device includes: a division unit that divides a layer of a convolutional neural network into a plurality of sublayers in a channel direction; a convolution unit that executes convolution processing for each of the sublayers to output a convolution result; an addition unit that adds an intermediate value obtained by cumulatively adding convolution results up to a previous sublayer to the convolution result with an adder for adding a bias to the convolution result every time the convolution processing is executed, and outputs an addition result; and an activation unit that inputs, to an activation function, the addition result obtained by adding the convolution result of a last sublayer on which the convolution processing has been executed last.