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TOKEN SELECTION IN TRANSFORMER NEURAL NETWORKS FOR EFFICIENT INFERENCING

Resumen de: US2025322275A1

Certain aspects of the present disclosure provide techniques and apparatus for processing data using a transformer neural network. The method generally includes generating, via a first attention layer of a machine learning model, a first attention map based on an input into the machine learning model; identifying, using a token prediction model, a first subset of tokens in the first attention map more relevant to a second attention layer of the machine learning model and a second subset of tokens in the first attention map less relevant to the second attention layer of the machine learning model; generating, via the second attention layer of the machine learning model, a second attention map based on the first subset of tokens in the first attention map; and generating an inference based on the second attention map and the second subset of tokens in the first attention map.

GRAPH-BASED MODELING OF RELATIONAL AFFECT IN GROUP INTERACTIONS

Nº publicación: US2025322206A1 16/10/2025

Solicitante:

HONDA MOTOR CO LTD [JP]
Honda Motor Co., Ltd

Resumen de: US2025322206A1

According to one aspect, graph-based modeling of relational affect in group interactions may include generating a graph neural network (GNN) based on multi-modal behavioral data associated with interactions between two or more individuals for each of the two or more individuals and relational context information associated with the interaction or the two or more individuals, performing message passing between nodes of the GNN based on the relational context information, generating a representation read-out associated with the GNN or a subgraph of the GNN, and performing an action based on the representation read-out.