A large body of research since the 1980s empirically observed that large neural networks can be compressed orsparsified toasmallfraction oftheiroriginal sizewhilemaintaining theirpredictive accuracy[14-16,20,23,29,45].

However,existingmodelsand benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping byalleviating task-specific engineering effortsandby providing users with interactiveconversations.

A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling.

More specifically,inour main result (Theorem 3.2) we identify a set of sufficient conditions on the initialization and the network topology under which theglobal convergence ofgradient descent isobtained.