Open-source model hubs now host over two million public AI models, yet teams building customer-facing AI systems must still determine which model to fine-tune for production deployment—a decision that shapes the quality, latency, and cost experienced by hundreds of millions of users. At Amazon, we spent over years of i...
Chen Luo, Yu-Lin Liu, Yi Liu et al.· Proceedings of the 20th ACM...· 0 citations
Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit infilling tasks, which require generating a middle span conditioned on both the prefix and the suffix. However, infilling is sensitive to th...
Hao-Bo Xu, Si-Rui Chen, Yuanchen Bei et al.· 0 citations
This work proposes a principled VLM TTA method called \algname, and theoretically reveals that the InfoNCE loss can be neatly reformulated as a Wasserstein OT formulation, thereby unifying the objectives of the inference and adaptation of VLMs to achieve their mutual benefits.
Qi Yu, Zhichen Zeng, Katherine Tieu et al.· 0 citations
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