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Found 4 papers, 2 papers with code
A structure-aware framework for learning device placements on computation graphs
To bridge the gap between encoder-placer and grouper-placer techniques, we propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit using reinforcement learning.
Exploring Neuron Interactions and Emergence in LLMs: From the Multifractal Analysis Perspective
Prior studies on the emergence in large models have primarily focused on how the functional capabilities of large language models (LLMs) scale with model size.
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block.
Leveraging Reinforcement Learning and Large Language Models for Code Optimization
We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.
