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Found 5 papers, 1 papers with code
Distilling Mathematical Reasoning Capabilities into Small Language Models
This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small Language Models (SLMs) without compromising performance.
A Survey on Model Compression for Large Language Models
As these challenges become increasingly pertinent, the field of model compression has emerged as a pivotal research area to alleviate these limitations.
Robust Neural Architecture Search
Neural Architectures Search (NAS) becomes more and more popular over these years.
Operation-level Progressive Differentiable Architecture Search
It can effectively alleviate the unfair competition between operations during the search phase of DARTS by offsetting the inherent unfair advantage of the skip connection over other operations.
Improving Differentiable Architecture Search via Self-Distillation
Differentiable Architecture Search (DARTS) is a simple yet efficient Neural Architecture Search (NAS) method.
