Nvidia Corporation

Daguang Xu

Dong Yang

Xiaosong Wang

Ziyue Xu

Fausto Milletari

Wenqi Li

Holger Reinhard Roth

Lickkong Tam

Andrew Feng

Yan Cheng

Yingda Xia

Ling Zhang

Holger Roth

Nicola Christin Rieke

Wentao Zhu

Rong Ou

Ali Hatamizadeh

Liyue Shen

Yuhong Wen

Vishwesh Nath

Riddhish Bhalodia

Can Zhao

Maximilian Baust

Charles Jonathan Hancox

Kevin Frank Lu

Te-Chung Isaac Yang

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 generate one or more data items related to an object from a set of multiple data items related to the same object using a generative adversarial network. In at least one embodiment, data objects in a set are disentangled into common and unique components and encoded such that they may be used to train one or more neural networks to generate missing data from the data objects in the set, such as generating missing medical images from a set of related medical images.

Representational disentanglement for multi-modality image completion using neural networks

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

Apparatuses, systems, and techniques to generate labeled training data. In at least one embodiment, labeled training images are generated from medial images annotated with natural language text.

Extending supervision using machine learning

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

Apparatuses, systems, and techniques to perform federated training of neural networks while maintaining control over dissemination of local models of neural networks from which aspects of local training data might be extracted. In at least one embodiment, a neural network is trained on local training data and a local model is provided to be aggregated with other local models into a global model that is in turn used for further local model training, wherein a provided local model or training is adjusted to reduce an ability to extract aspects of local training data therefrom.

Neural network trained using federated learning with local training data preserved at local edge circuits

Apparatuses, systems, and techniques to collaboratively train one or more machine learning models. Parameter reviewers may be configured to compare sets of machine learning model parameter information in order to generate one or more machine learning models, such as neural networks.

Processor and system to train machine learning models based on comparing accuracy of model parameters

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

Potomac, MD, United States of America

Daguang Xu