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    VISION-LANGUAGE MODEL WITH AN ENSEMBLE OF EXPERTS
    2.
    发明公开
    VISION-LANGUAGE MODEL WITH AN ENSEMBLE OF EXPERTS 审中-公开

    公开(公告)号：US20240265690A1

    公开(公告)日：2024-08-08

    申请号：US18544840

    申请日：2023-12-19

    Applicant: NVIDIA Corporation

    Inventor： Animashree Anandkumar ,  Linxi Fan ,  Zhiding Yu ,  Chaowei Xiao ,  Shikun Liu

    IPC: G06V10/82 ,  G06V10/80

    CPC classification number: G06V10/82 ,  G06V10/811

    Abstract: A vision-language model learns skills and domain knowledge via distinct and separate task-specific neural networks, referred to as experts. Each expert is independently optimized for a specific task, facilitating the use of domain-specific data and architectures that are not feasible with a single large neural network trained for multiple tasks. The vision-language model implemented as an ensemble of pre-trained experts and is more efficiently trained compared with the single large neural network. During training, the vision-language model integrates specialized skills and domain knowledge, rather than trying to simultaneously learn multiple tasks, resulting in effective multi-modal learning.

    SYSTEM AND METHOD FOR RETRIEVAL-BASED CONTROLLABLE MOLECULE GENERATION
    7.
    发明公开
    SYSTEM AND METHOD FOR RETRIEVAL-BASED CONTROLLABLE MOLECULE GENERATION 审中-公开

    公开(公告)号：US20240029836A1

    公开(公告)日：2024-01-25

    申请号：US18353773

    申请日：2023-07-17

    Applicant: NVIDIA Corporation

    Inventor： Weili Nie ,  Zichao Wang ,  Chaowei Xiao ,  Animashree Anandkumar

    IPC: G16C20/90 ,  G16C20/70

    CPC classification number: G16C20/90 ,  G16C20/70

    Abstract: A machine learning framework is described for performing generation of candidate molecules for, e.g., drug discovery or other applications. The framework utilizes a pre-trained encoder-decoder model to interface between representations of molecules and embeddings for those molecules in a latent space. A fusion module is located between the encoder and decoder and is used to fuse an embedding for an input molecule with embeddings for one or more exemplary molecules selected from a database that is constructed according to a design criteria. The fused embedding is decoded using the decoder to generate a candidate molecule. The fusion module is trained to reconstruct a nearest neighbor to the input molecule from the database based on the sample of exemplary molecules. An iterative approach may be used during inference to dynamically update the database to include newly generated candidate molecules.

    TRAJECTORY STITCHING FOR ACCELERATING DIFFUSION MODELS
    8.
    发明申请
    TRAJECTORY STITCHING FOR ACCELERATING DIFFUSION MODELS 有权

    公开(公告)号：US20250103968A1

    公开(公告)日：2025-03-27

    申请号：US18821611

    申请日：2024-08-30

    Applicant: NVIDIA Corporation

    Inventor： Zizheng Pan ,  De-An Huang ,  Weili Nie ,  Zhiding Yu ,  Chaowei Xiao ,  Anima Anandkumar

    IPC: G06N20/20

    Abstract: Diffusion models are machine learning algorithms that are uniquely trained to generate high-quality data from an input lower-quality data. Diffusion probabilistic models use discrete-time random processes or continuous-time stochastic differential equations (SDEs) that learn to gradually remove the noise added to the data points. With diffusion probabilistic models, high quality output currently requires sampling from a large diffusion probabilistic model which corners at a high computational cost. The present disclosure stitches together the trajectory of two or more inferior diffusion probabilistic models during a denoising process, which can in turn accelerate the denoising process by avoiding use of only a single large diffusion probabilistic model.

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