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Validity-Based Sampling and Smoothing Methods for Multiple Reference Image Captioning
In image captioning, multiple captions are often provided as ground truths, since a valid caption is not always uniquely determined.
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives
Diffusion generative models have emerged as a new challenger to popular deep neural generative models such as GANs, but have the drawback that they often require a huge number of neural function evaluations (NFEs) during synthesis unless some sophisticated sampling strategies are employed.
Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings
This paper is the first attempt to learn the policy of an inquiry dialog system (IDS) by using deep reinforcement learning (DRL).
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