During the operation of a system including a deep neural network (DNN), new
input values that were not included in the training dataset are given to the
DNN. In such a case, the DNN may be incrementally trained with the new input
values; however, that training may reduce the accuracy of the DNN in regard to
the dataset that was previously obtained and used for the past training. It is
necessary to evaluate the effect of the additional training on the accuracy for
the past dataset. However, evaluation by testing all the input values included
in the past dataset takes time. Therefore, we propose a new method to quickly
evaluate the effect on the accuracy for the past dataset. In the proposed
method, the gradient of the parameter values (such as weight and bias) for the
past dataset is extracted by running the DNN before the training. Then, after
the training, its effect on the accuracy with respect to the past dataset is
calculated from the gradient and update differences of the parameter values. To
show the usefulness of the proposed method, we present experimental results
with several datasets. The results show that the proposed method can estimate
the accuracy change by additional training in a constant time.

通过提取参数值的梯度以及参数值的更新差异，我们提出了一种快速评估深度神经网络在过去数据集上准确性改变的新方法，并通过几个数据集的实验结果表明，该方法能够在恒定时间内估计额外训练对准确性的影响。

增量学习中过去数据集的深度神经网络快速评估

Fast Evaluation of DNN for Past Dataset in Incremental Learning

Inverse reinforcement learning (IRL) aims to infer an agent's preferences
(represented as a reward function $R$) from their behaviour (represented as a
policy $\pi$). To do this, we need a behavioural model of how $\pi$ relates to
$R$. In the current literature, the most common behavioural models are
optimality, Boltzmann-rationality, and causal entropy maximisation. However,
the true relationship between a human's preferences and their behaviour is much
more complex than any of these behavioural models. This means that the
behavioural models are misspecified, which raises the concern that they may
lead to systematic errors if applied to real data. In this paper, we analyse
how sensitive the IRL problem is to misspecification of the behavioural model.
Specifically, we provide necessary and sufficient conditions that completely
characterise how the observed data may differ from the assumed behavioural
model without incurring an error above a given threshold. In addition to this,
we also characterise the conditions under which a behavioural model is robust
to small perturbations of the observed policy, and we analyse how robust many
behavioural models are to misspecification of their parameter values (such as
e.g.\ the discount rate). Our analysis suggests that the IRL problem is highly
sensitive to misspecification, in the sense that very mild misspecification can
lead to very large errors in the inferred reward function.

逆向强化学习（IRL）的关键问题是行为模型的错配性对结果的影响，本研究对此进行敏感性分析，并分析参数值和模型参数错配对结果的影响。

量化逆强化学习对错误规范的敏感性

Quantifying the Sensitivity of Inverse Reinforcement Learning to  Misspecification

We propose an efficient method for simultaneously optimizing both the
structure and parameter values of quantum circuits with only a small
computational overhead. Shallow circuits that use structure optimization
perform significantly better than circuits that use parameter updates alone,
making this method particularly suitable for noisy intermediate-scale quantum
computers. We demonstrate the method for optimizing a variational quantum
eigensolver for finding the ground states of Lithium Hydride and the Heisenberg
model in simulation, and for finding the ground state of Hydrogen gas on the
IBM Melbourne quantum computer.

我们提出了一种有效的方法，同时优化量子电路的结构和参数值，具有较小的计算开销，适用于噪声中等规模的量子计算机。我们在模拟中优化了变分量子本征求解器，以找到氢化锂和海森堡模型的基态，并在 IBM Melbourne 量子计算机上找到氢气的基态。