modular framework
In AI, a modular framework refers to the design of a system that is composed of interchangeable modules or components, enabling flexibility and ease of updates or modifications to specific parts without overhauling the entire system. This is particularly useful in building complex AI systems where different functionalities can be developed and tested independently.
- AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
- Covariate-moderated Empirical Bayes Matrix Factorization
- Differentiation Through Black-Box Quadratic Programming Solvers
- Efficient semantic uncertainty quantification in language models via diversity-steered sampling
- Infrequent Exploration in Linear Bandits
- Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs