Nvidia Corporation

Dong Yang

Daguang Xu

Ziyue Xu

Xiaosong Wang

Holger Reinhard Roth

Yingda Xia

Fausto Milletari

Ling Zhang

Lickkong Tam

Te-Chung Isaac Yang

Vishwesh Nath

Apparatuses, systems, and techniques to dynamically identify a neural network architecture for a data point. In at least one embodiment, a data point is fed to a plurality of neural network architectures, in which an optimal neural network architecture is determined based at least in part on information inferenced using the plurality of neural network architectures.

Technique to perform neural network architecture search

Apparatuses, systems, and techniques to identify objects within an image. In at least one embodiment, objects are identified in an image using one or more neural networks based, at least in part, on neural network outputs ranked according to uncertainty values.

Neural network training technique

Apparatuses, systems, and techniques to detect object in images including digital representations of those objects. In at least one embodiment, one or more objects are detected in an image based, at least in part, on points corresponding to a surface of one or more objects.

Object detection using one or more neural networks

Apparatuses, systems, and techniques to indicate an extent, to which text corresponds to one or more images. In at least one embodiment, an extent to which text corresponds to one or more images is indicated using one or more neural networks and used to train the one or more neural networks.

Pretraining framework for neural networks

Apparatuses, systems, and techniques to select labels of training images to train a network. In at least one embodiment, one or more labels of training images are selected to train a network.

Selecting annotations for training images using a neural network

Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

Multi-view image analysis using neural networks

In at least one embodiment, a reinforcement-learning-based searching approach is used to produce a training configuration for a machine-learning model. In at least one embodiment, 3D medical image segmentation is performed using learned image preprocessing parameters.

Growing community of inventors

North Bethesda, MD, United States of America

Dong Yang