Empirically, MiTSformer achieves consistent SOTAonfivemixedtime series analysis tasks, including classification, extrinsic regression,anomalydetection,imputation,andlong-termforecasting.

Multi-view multi-person 3D pose estimation aims to localize 3D skeleton joints for each person instance in a scene from multi-view camera inputs. It is a fundamental task that benefits many real-world applications (such assurveillance, sportscast, gaming and mixed reality) and ismainly tackled byreconstruction-based [6,14,4]andvolumetric [40]approaches inpreviousliterature, as showninFig.1(a)and(b).

Wepropose MCUNet, aframeworkthat jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference onmicrocontrollers.

We thank all the reviewers for their constructive comments. We will provide details in the final draft. MCUNet shows consistent improvement across different devices (F746, H743) and tasks (classification, detection). R1: Whether the overall network topology brings major improvement. R2: Why the auto-tuning in TVM fails to work on MCUs.

This is only for the ease of visualization. For linear MDP,In the generative model setting, Agarwal et al. [2020] shows model-based approach is still minimax optimal O((1 γ) 3SA/2)byusing as-absorbing MDP construction andthismodelbased technique is later reused for other more general settings (e.g. Itrequires high probability guarantee for learning optimal policyforany reward function, which is strictly stronger than the standard learning task that one only needs to learn to optimal policy for a fixed reward. B.2 GeneralabsorbingMDP The general absorbing MDP is defined as follows: for a fixed states and a sequence {ut}Ht=1, MDPMs,{ut}Ht=1 is identical toM for all states excepts, and state s is absorbing in the sense PMs,{ut}Ht=1(s|s,a) = 1 for all a, and the instantaneous reward at timet is rt(s,a) = ut for all a A. Also,weusetheshorthand notationVπ{s,ut} forVπs,Ms,{u We focus on the first claim. Later we shall remove the conditional onN (see SectionB.7). We use the singleton-absorbing MDPMs,{u?t}Ht=1 to handle the case (recallu?t

This dataset encompasses human preferences in text-to-video generation tasks along two primary dimensions: helpfulness and harmlessness.