公开(公告)号：US20250117633A1

公开(公告)日：2025-04-10

申请号：US18987302

申请日：2024-12-19

Applicant: Intel Corporation

Inventor： Anthony Daniel Rhodes ,  Ramesh Radhakrishna Manuvinakurike ,  Sovan Biswas ,  Giuseppe Raffa ,  Lama Nachman

IPC: G06N3/0475

Abstract: Predictive uncertainty of a generative machine learning model may be estimated. The generative machine learning model may be a large language model or large multi-modal model. A datum may be input into the generative machine learning model. The generative machine learning model may generate outputs from the datum. Latent embeddings for the outputs may be extracted from the generative machine learning model. A covariance matrix with respect to the latent embeddings may be computed. The covariance matrix may be a two-dimensional matrix, such as a square matrix. The predictive uncertainty of the generative machine learning model may be estimated using the covariance matrix. For instance, the matrix entropy of the covariance matrix may be determined. The matrix entropy may be an approximated dimension of a latent semantic manifold spanned by the outputs of the generative machine learning model and may indicate the predictive uncertainty of the generative machine learning model.

公开(公告)号：US20230124495A1

公开(公告)日：2023-04-20

申请号：US18050757

申请日：2022-10-28

Applicant: Intel Corporation

Inventor： Sovan Biswas ,  Anthony Daniel Rhodes ,  Ramesh Radhakrishna Manuvinakurike ,  Giuseppe Raffa ,  Richard Beckwith

IPC: G06V20/40 ,  G06V10/764 ,  G06V10/82

Abstract: Disclosed is a technical solution to process a video that captures actions to be performed for completing a task based on a chronological sequence of stages within the task. An example system may identify an action sequence from an instruction for the task. The system inputs the action sequence into a trained model (e.g., a recurrent neural network), which outputs the chronological sequence of stages. The RNN may be trained through self-supervised learning. The system may input the video and the chronological sequence of stages into another trained model, e.g., a temporal convolutional network. The other trained model may include hidden layers arranged before an attention layer. The hidden layers may extract features from the video and feed the features into the attention layer. The attention layer may determine attention weights of the features based on the chronological sequence of stages.