公开(公告)号：US20210073612A1

公开(公告)日：2021-03-11

申请号：US16566797

申请日：2019-09-10

Applicant: NVIDIA Corporation

Inventor： Arash Vahdat ,  Arun Mohanray Mallya ,  Ming-Yu Liu ,  Jan Kautz

IPC: G06N3/04 ,  G06F7/57 ,  G06N3/063 ,  G06N3/08

Abstract: In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.

公开(公告)号：US12249048B2

公开(公告)日：2025-03-11

申请号：US17681625

申请日：2022-02-25

Applicant: NVIDIA CORPORATION

Inventor： Arash Vahdat ,  Karsten Kreis ,  Jan Kautz

IPC: G06T5/70 ,  G06N3/08 ,  G06V10/82

Abstract: One embodiment of the present invention sets forth a technique for generating data. The technique includes sampling from a first distribution associated with the score-based generative model to generate a first set of values. The technique also includes performing one or more denoising operations via the score-based generative model to convert the first set of values into a first set of latent variable values associated with a latent space. The technique further includes converting the first set of latent variable values into a generative output.

公开(公告)号：US12175350B2

公开(公告)日：2024-12-24

申请号：US16566797

申请日：2019-09-10

Applicant: NVIDIA Corporation

Inventor： Arash Vahdat ,  Arun Mohanray Mallya ,  Ming-Yu Liu ,  Jan Kautz

IPC: G06N20/00 ,  G06F7/57 ,  G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G05B13/02

Abstract: In at least one embodiment, differentiable neural architecture search and reinforcement learning are combined under one framework to discover network architectures with desired properties such as high accuracy, low latency, or both. In at least one embodiment, an objective function for search based on generalization error prevents the selection of architectures prone to overfitting.

公开(公告)号：US20240273682A1

公开(公告)日：2024-08-15

申请号：US18431527

申请日：2024-02-02

Applicant: NVIDIA Corporation

Inventor： Weili Nie ,  Guan-Horng Liu ,  Arash Vahdat ,  De-An Huang ,  Anima Anandkumar

IPC: G06T5/60 ,  G06T5/50

CPC classification number: G06T5/60 ,  G06T5/50

Abstract: Image restoration generally involves recovering a target clean image from a given image having noise, blurring, or other degraded features. Current image restoration solutions typically include a diffusion model that is trained for image restoration by a forward process that progressively diffuses data to noise, and then by learning in a reverse process to generate the data from the noise. However, the forward process relies on Gaussian noise to diffuse the original data, which has little or no structural information corresponding to the original data versus learning from the degraded image itself which is much more structurally informative compared to the random Gaussian noise. Similar problems also exist for other data-to-data translation tasks. The present disclosure trains a data translation conditional diffusion model from diffusion bridge(s) computed between a first version of the data and a second version of the data, which can yield a model that can provide interpretable generation, sampling efficiency, and reduced processing time.

公开(公告)号：US20240185396A1

公开(公告)日：2024-06-06

申请号：US18222725

申请日：2023-07-17

Applicant: NVIDIA CORPORATION

Inventor： Ali Hatamizadeh ,  Jiaming Song ,  Jan Kautz ,  Arash Vahdat

IPC: G06T5/00 ,  G06T1/20 ,  G06T7/00

CPC classification number: G06T5/002 ,  G06T1/20 ,  G06T7/0002 ,  G06T2207/20081 ,  G06T2207/20182

Abstract: Apparatuses, systems, and techniques to generate images. In at least one embodiment, one or more machine learning models generate an output image based, at least in part, on calculating attention scores using time embeddings.

公开(公告)号：US11978258B2

公开(公告)日：2024-05-07

申请号：US17224041

申请日：2021-04-06

Applicant: NVIDIA Corporation

Inventor： Sina Mohseni ,  Arash Vahdat ,  Jay Yadawa

IPC: G06V20/56 ,  G06F18/211 ,  G06F18/2415 ,  G06F18/2431 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06V20/56 ,  G06F18/211 ,  G06F18/2415 ,  G06F18/2431 ,  G06N3/045 ,  G06N3/08

Abstract: Apparatuses, systems, and techniques to identify out-of-distribution input data in one or more neural networks. In at least one embodiment, a technique includes training one or more neural networks to infer a plurality of characteristics about input information based, at least in part, on the one or more neural networks being independently trained to infer each of the plurality of characteristics about the input information.

公开(公告)号：US20240104698A1

公开(公告)日：2024-03-28

申请号：US17719091

申请日：2022-04-12

Applicant: Nvidia Corporation

Inventor： Weili Nie ,  Yujia Huang ,  Chaowei Xiao ,  Arash Vahdat ,  Anima Anandkumar

IPC: G06T5/00 ,  G06N3/04 ,  G06T5/50

CPC classification number: G06T5/002 ,  G06N3/0445 ,  G06N3/0472 ,  G06T5/50 ,  G06T2207/20084

Abstract: Apparatuses, systems, and techniques are presented to remove unintended variations introduced into data. In at least one embodiment, a first image of an object can be generated based, at least in part, upon adding noise to, and removing the noise from, a second image of the object.

公开(公告)号：US11847538B2

公开(公告)日：2023-12-19

申请号：US17317698

申请日：2021-05-11

Applicant: NVIDIA Corporation

Inventor： Tianshi Cao ,  Alex Bie ,  Karsten Julian Kreis ,  Sanja Fidler ,  Arash Vahdat

IPC: G06N20/00 ,  G06F18/214 ,  G06F21/62 ,  G06N3/08 ,  G06V20/56

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F21/6218 ,  G06N3/08 ,  G06V20/56

Abstract: Apparatuses, systems, and techniques to train a generative model based at least in part on a private dataset. In at least one embodiment, the generative model is trained based at least in part on a differentially private Sinkhorn algorithm, for example, using backpropagation with gradient descent to determine a gradient of a set of parameters of the generative models and modifying the set of parameters based at least in part on the gradient.