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Fence proposes using Small Language Models trained on high-quality synthetic data as specialized guardrails for LLM applications, demonstrating performance gains over prompt-based LLM guardrails.
This paper provides a comprehensive review of Neural Architecture Search (NAS) methods applied to Generative Adversarial Networks (GANs), categorizing approaches and highlighting benefits and limitations.
This paper proposes a hybrid WGAN-GA approach for refining generative graph topologies, using a genetic algorithm to correct residual structural deviations in GAN-based generated graphs, improving realism for synthetic graph synthesis and data augmentation.
This paper establishes mathematical equivalences between generative adversarial networks (GANs), inverse reinforcement learning (IRL), and energy-based models (EBMs), demonstrating that certain IRL methods are equivalent to GANs with evaluable generator density. The work bridges three research communities to enable knowledge transfer for developing more stable and scalable algorithms.