Deep learning performance distributions reveal tail risk for Trustworthy AI

A new study published on arXiv explores the non-deterministic nature of deep learning algorithms, revealing that model performance is best understood as a distribution rather than a single value. The research conducted 18,600 runs across various architectures and tasks, finding that performance distributions are often non-Gaussian, particularly in time series forecasting. The study concludes that mean performance alone is insufficient for model selection, emphasizing the need to assess spread, symmetry, and tail risk for Trustworthy AI applications. AI

IMPACT Highlights the need for distributional analysis beyond mean performance for reliable model selection in critical AI applications.

RANK_REASON Research paper analyzing deep learning performance distributions. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• deep learning
• Hugging Face
• image classification
• Time Series Forecasting
• Trustworthy AI

• paper
• safety

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