公开(公告)号：US20240062042A1

公开(公告)日：2024-02-22

申请号：US18451692

申请日：2023-08-17

Applicant: SRI International

Inventor： Aswin Nadamuni Raghavan ,  Saurabh Farkya ,  Jesse Albert Hostetler ,  Avraham Joshua Ziskind ,  Michael Piacentino ,  Ajay Divakaran ,  Zhengyu Chen

IPC: G06N3/045 ,  G06F21/56 ,  G06N3/098

CPC classification number: G06N3/045 ,  G06F21/566 ,  G06N3/098 ,  G06F2221/033

Abstract: In general, the disclosure describes techniques for implementing an MI-based attack detector. In an example, a method includes training a neural network using training data, applying stochastic quantization to one or more layers of the neural network, generating, using the trained neural network, an ensemble of neural networks having a plurality of quantized members, wherein at least one of weights or activations of each of the plurality of quantized members have different bit precision, and combining predictions of the plurality of quantized members of the ensemble to detect one or more adversarial attacks and/or determine performance of the ensemble of neural networks.

公开(公告)号：US20230394413A1

公开(公告)日：2023-12-07

申请号：US18330930

申请日：2023-06-07

Applicant: SRI International

Inventor： Subhodev Das ,  Aswin Nadamuni Raghavan ,  Avraham Joshua Ziskind ,  Timothy J. Meo ,  Bhoram Lee ,  Chih-hung Yeh ,  John Cadigan ,  Ali Chaudhry ,  Jonathan C. Balloch

IPC: G06Q10/0637

CPC classification number: G06Q10/06375

Abstract: In general, the disclosure describes techniques for Artificial Intelligence (AI) models that can automatically generate diverse, explainable, interpretable, reactive, and coordinated behaviors for a team. In an example, a method includes receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team including a plurality of agents; generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of problem solving in the simulator; outputting, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprises recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data.