There are still, however, some unsolved challenges for a successful deployment of RL such as efficient learning of constrained or safe Markov Decision Processes (MDPs) [4].

Wethen develop an information rectification module (IRM) and a distribution guided distillation (DGD) scheme for fully quantized vision transformers (Q-ViT) to effectively eliminate such distortion, leading to a fully quantized ViTs.

Variational autoencoders (VAE)areapowerful andwidely-used classofmodels to learn complex data distributions in an unsupervised fashion.

We determine the sample complexity of pure exploration bandit problems with multiple goodanswers.

Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction.

Based on our framework, we also present a hybrid of DRL and EMalgorithms thathasaguaranteed convergencerate.

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