However, the leap in performance often comes at the cost of large-scale labeled data.

If the relaxation is loose, however, then the resulting certificate can be too conservative to be practically useful. Recently, a less conservative robustness certificate was proposed, based on a semidefinite programming (SDP) relaxation of the ReLU activation function.

Privacy is a central challenge for systems that learn from sensitive data sets, especially when a system's outputs must be continuously updated to reflect changing

Data augmentation is essential to achieve state-of-the-art performance in many deeplearningapplications.

Atypical MLLM [7]consists of a pre-trained LLM, a pre-trained modality encoder, and a modality interface to connect them.

We consider an online prediction problem in the context of network caching.

In the first part of this paper, we establish a strong duality result for this minimization problem.

In the first part of this paper, we establish a strong duality result for this minimization problem.