We demonstrate the effectiveness of the categorical distribution as a neural network output for next event prediction. This is done for both discrete-time and continuous-time event sequences. To model continuous-time processes, the categorical distribution is interpreted as a piecewise-constant density function and is shown to be competitive across a range of datasets. We then argue for the importance of studying discrete-time processes by introducing a neuronal spike prediction task motivated by retinal prosthetics, where discretization of event times is consequent on the task description. Separately, we show evidence that commonly used datasets favour smaller models. Finally, we introduce new synthetic datasets for testing larger models, as well as synthetic datasets with discrete event times.

翻译：我们证明了分类分布作为神经网络输出在下一事件预测中的有效性。该方法适用于离散时间和连续时间事件序列。为建模连续时间过程，分类分布被解释为分段常数密度函数，并在多个数据集上展现出竞争力。随后，我们通过引入受视网膜假体启发的神经元尖峰预测任务，论证了研究离散时间过程的重要性——该任务描述天然要求事件时间的离散化。此外，我们提供了证据表明常用数据集倾向于支持较小模型。最后，我们引入了用于测试较大模型的新合成数据集，以及包含离散事件时间的合成数据集。