Hanno Becker

Aug 12, 2024 | amazon.science | Yixin Chen |Shuai Zhang |Boran Han |Hanno Becker

In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs). CaMML, a lightweight module, is crafted to seamlessly integrate multimodal contextual samples into large models, thereby empowering the model to derive knowledge from analogous, domain-specific, up-to-date information and make grounded inferences. Importantly, CaMML is highly scalable and can efficiently handle lengthy multimodal context examples owing to its hierarchical design.

Aug 8, 2024 | amazon.science | June Lee |Hanno Becker |John Harrison |Juneyoung Lee

Most secure transactions online are protected by public-key encryption schemes like RSA, whose security depends on the difficulty of factoring large numbers. Public-key encryption improves security because it enables the encrypted exchange of private keys. But because it depends on operations like modular exponentiation of large integers, it introduces significant computational overhead.