AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces epistemic learning from credal supervision, a framework that uses convex sets of plausible labelling distributions as supervision and learns sets of predictive distributions, and shows how credal labels can be obtained through a simple relaxation of existing probabilistic labels, reducing commitmen...
Textual descriptions of reaction conditions are encoded by a fine-tuned language model trained jointly with Gaussian process surrogates, yielding task-adaptive representations within a multi-objective Bayesian optimisation loop.
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