The research investigates the performance of autoencoders in the compression of structured data. Autoencoders are a prominent model in many branches of machine learning and data compression. The researchers prove…
Continue readingThe article addresses the challenges in evaluating large language models, including data contamination, sensitivity to prompts, and the high cost of benchmark creation. The authors propose a new approach that…
Continue readingThe paper discusses the challenges in evaluating large language models, including data contamination, prompt sensitivity, and benchmark creation cost. To address these, the authors propose a lossless data compression-based evaluation…
Continue readingThe article presents MGARD, a software that provides MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. It is optimized for high-performance, error-controlled data compression and refactoring….
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