Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation.

We show that diffusion models actually are capable of generating high quality samples, sometimes better than the published results on other types of generative models (Section 4). In addition, we show that a certain parameterization of diffusion models reveals an equivalence with denoising score matching over multiple noise levels during training and with annealed Langevin dynamics during sampling (Section 3.2) [55, 61].

We carefully design the potential functions to enable relational reasoning among different objects according to knowledge graph embedding methods.

While this has been demonstrated empirically overtimeinavarietyoftasks,theoretical resultsquantifying thebenefits of such methods are still few and far between. In this paper, we discuss the kind of structure in a Markov decision process which gives rise to efficient HRLmethods.