Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive applications. In these settings, it is often desirable to quantify the uncertainty associated with the prediction (instead of just determining the most likely sequence, as in language modeling). In this paper, we propose a Monte Carlo framework to estimate probabilities and confidence intervals associated with the distribution of a discrete sequence. Our framework uses a Monte Carlo simulator, implemented as an autoregressively trained neural network, to sample sequences conditioned on an image input. We then use these samples to estimate the probabilities and confidence intervals. Experiments on synthetic and real data show that the framework produces accurate discriminative predictions, but can suffer from miscalibration. In order to address this shortcoming, we propose a time-dependent regularization method, which is shown to produce calibrated predictions.

翻译：从图像及其他高维数据中进行序列的概率预测是一项关键挑战，在风险敏感型应用中尤为如此。在此类场景中，通常需要量化与预测相关的不确定性（而非如语言建模中仅确定最可能的序列）。本文提出一种蒙特卡洛框架，用于估计与离散序列分布相关的概率及置信区间。该框架采用以自回归方式训练的神经网络实现的蒙特卡洛模拟器，在给定图像输入条件下对序列进行采样。随后利用这些样本估计概率及置信区间。在合成数据与真实数据上的实验表明，该框架能产生准确的判别式预测，但可能存在校准失准问题。为克服这一缺陷，我们提出一种时间依赖的正则化方法，实验证明该方法能够生成经过校准的预测。