Wemodel theeventofinterest asaspecial terminal state, andwe seek to estimate the survival distribution (i.e., the distribution of the hitting time for that terminal state) from anyother state.

Graph neural networks (GNNs) have shown the power in graph representation learningfornumeroustasks.

The early theory of actor-critic methods considered convergence using linear function approximators for the policy and value functions. Recent work has established convergence using neural network approximators with a single hidden layer. In this work we are taking the natural next step and establish convergence using deep neural networks with an arbitrary number of hidden layers, thus closing a gap between theory and practice. We show that actor-critic updates projected on a ball around the initial condition will converge to a neighborhood where the average of the squared gradients is O (1 / m) + O (ϵ), with m being the width of the neural network and ϵ the approximation quality of the best critic neural network over the projected set.

Thisinteresting finding notonlyhighlights the potential redundancy of information added during fine-tuning, but also has significant implications for the multi-tenant serving and multi-tenant storage of fine-tuned models.