SOURCE-LINKED INTELLIGENCE

Beyond Maximum Entropy: A new paradigm for Modeling, Inference, and Learning Efficiency

CORDIS · observation · Publication date unknown

Beyond Maximum Entropy: A new paradigm for Modeling, Inference, and Learning Efficiency Machine learning is revolutionizing science and society, enabling transformative breakthroughs such as predicting protein structures, simulating many body quantum systems, and redefining entire fields like healthcare, finance, or climate modeling. Yet, this rapid progress comes at a cost: the unprecedented scale and complexity of state-of-the-art models make them resource-intensive, inaccessible, and often opaque. These challenges limit their potential, hindering our ability to understand, optimize, and deploy them responsibly. To unlock the full power of machine learning, we must reimagine its foundations. This project challenges the 'bigger is better' paradigm in machine learning by proposing an alternative: the development of simpler, more efficient generative models that can handle the complexity of the real world while remaining analytically interpretable. Using techniques from stat

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recordType

award

status

SIGNED

region

EU

value

1999741

unit

EUR