AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces a sampling-free, perturbation-based training framework based on continuous and differentiable masking that achieves competitive or superior attribution faithfulness compared to strong sampling-based baselines, while dramatically reducing computational cost and enabling substantially improved scalab...
These findings provide practical guidance for integrating open-source protein structure prediction models into AI-driven nanobody discovery pipelines while highlighting the need for improved generalization across antigens.
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