Jian Huang
3 indexed papers
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The paper proposes RIEQE, a two-stage training framework that synergistically co-evolves implicit and explicit reasoning capabilities in Large Reasoning Models (LRMs) to significantly improve fine-grained translation quality estimation.
This paper proposes a semi-supervised framework, RepG, for conditional generative modeling using stochastic interpolation and low-dimensional latent representations.
The paper introduces Label-Augmented Conditional Diffusion (LACD), a method for learning complex conditional distributions using unlabeled data, and provides theoretical guarantees for its effectiveness.
Papers
Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations
This paper proposes a semi-supervised framework, RepG, for conditional generative modeling using stochastic interpolation and low-dimensional latent representations.