SMA uses a submodular mutual information objective on data sets to deliver competitive zero-shot classification and retrieval performance on CLIP benchmarks with only tens of thousands of samples, orders of magnitude fewer than standard approaches.
Submodularity in machine learning and artificial intelligence
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An accelerated relax-and-round algorithm for concave coverage problems achieves Õ(mn ε^{-1}) runtime and a 0.827-approximation ratio for the logarithmic reward function.
Bootstrapping math questions via rewriting creates MetaMathQA; fine-tuning LLaMA-2 on it yields 66.4% on GSM8K for 7B and 82.3% for 70B, beating prior same-size models by large margins.
citing papers explorer
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SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning
SMA uses a submodular mutual information objective on data sets to deliver competitive zero-shot classification and retrieval performance on CLIP benchmarks with only tens of thousands of samples, orders of magnitude fewer than standard approaches.
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Accelerated Relax-and-Round for Concave Coverage Problems
An accelerated relax-and-round algorithm for concave coverage problems achieves Õ(mn ε^{-1}) runtime and a 0.827-approximation ratio for the logarithmic reward function.
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MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Bootstrapping math questions via rewriting creates MetaMathQA; fine-tuning LLaMA-2 on it yields 66.4% on GSM8K for 7B and 82.3% for 70B, beating prior same-size models by large margins.