These profiles are fed back to the LLM, which then revises the code to reduce overhead.

For example, recent video LDMs can generate more than 16 frames, but GPUs with 24 GB memory can only perform gradient-based decoder inversion for 4 frames.

Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters.

When training complex neural networks, memory usage can be an important bottleneck.

One such filter, the partitioned learned Bloom filter (PLBF), achieves excellent memory efficiency.

Neural Information Processing Systems http://nips.cc/

This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead.