公开(公告)号：US20240007403A1

公开(公告)日：2024-01-04

申请号：US18298949

申请日：2023-04-11

Applicant: NVIDIA CORPORATION

Inventor： Gal CHECHIK ,  Gal DALAL ,  Benjamin FUHRER ,  Doron HARITAN KAZAKOV ,  Shie MANNOR ,  Yuval SHPIGELMAN ,  Chen TESSLER

IPC: H04L47/127 ,  H04L41/16

CPC classification number: H04L47/127 ,  H04L41/16

Abstract: In various embodiments, a congestion control modelling application automatically controls congestion in data transmission networks. The congestion control modelling application executes a trained neural network in conjunction with a simulated data transmission network to generate a training dataset. The trained neural network has been trained to control congestion in the simulated data transmission network. The congestion control modelling application generates a first trained decision tree model based on an initial loss for an initial model relative to the training dataset. The congestion control modelling application generates a final tree-based model based on the first trained decision tree model and at least a second trained decision tree model. The congestion control modelling application executes the final tree-based model in conjunction with a data transmission network to control congestion within the data transmission network.

公开(公告)号：US20250157115A1

公开(公告)日：2025-05-15

申请号：US18830218

申请日：2024-09-10

Applicant: NVIDIA CORPORATION

Inventor： Chen TESSLER ,  Gal CHECHIK ,  Jason PENG

IPC: G06T13/40

Abstract: One embodiment of a method for animating characters includes receiving a first state of a character and one or more constraints on one or more motions associated with a subset of joints belonging to the character, generating, via a trained machine learning model and based on the first state and the one or more constraints, a first action for the character to perform, and causing the character to perform the first action within a computer-based or physical environment.