Neural networks and in particular the attention mechanism have brought
significant advances to the field of Automated Essay Scoring. Many of these
systems use a regression-based model which may be prone to underfitting when
the model only predicts the mean of the training data. In this paper, we
present a dynamic loss function that creates an incentive for the model to
predict with the correct distribution, as well as predicting the correct
values. Our loss function achieves this goal without sacrificing any
performance achieving a Quadratic Weighted Kappa score of 0.752 on the
Automated Student Assessment Prize Automated Essay Scoring dataset.

该研究利用动态损失函数建立基于神经网络和注意机制的自动化作文评分模型，能够避免回归模型预测训练样本均值的欠拟合现象，并在测试集上取得 Quadratic Weighted Kappa 得分 0.752。

神经网络自动化评分中动态损失函数的有效性

The Effectiveness of a Dynamic Loss Function in Neural Network Based  Automated Essay Scoring

The goal of visual answering localization (VAL) in the video is to obtain a
relevant and concise time clip from a video as the answer to the given natural
language question. Early methods are based on the interaction modelling between
video and text to predict the visual answer by the visual predictor. Later,
using the textual predictor with subtitles for the VAL proves to be more
precise. However, these existing methods still have cross-modal knowledge
deviations from visual frames or textual subtitles. In this paper, we propose a
cross-modal mutual knowledge transfer span localization (MutualSL) method to
reduce the knowledge deviation. MutualSL has both visual predictor and textual
predictor, where we expect the prediction results of these both to be
consistent, so as to promote semantic knowledge understanding between
cross-modalities. On this basis, we design a one-way dynamic loss function to
dynamically adjust the proportion of knowledge transfer. We have conducted
extensive experiments on three public datasets for evaluation. The experimental
results show that our method outperforms other competitive state-of-the-art
(SOTA) methods, demonstrating its effectiveness.

本文提出了一种跨模态互相知识传递的跨模态互知跨度本地化方法 (MutualSL)，它包含视觉预测器和文本预测器两个部分，旨在通过构建一种双向动态损失函数以调整传递比例来提高语义知识的理解，以获取视频中自然语言问题的相关和简明时间剪辑答案。实验结果表明，该方法优于其他竞争同类方法，展示了其有效性。

跨模态相互知识迁移的视觉答案定位

Visual Answer Localization with Cross-modal Mutual Knowledge Transfer

Real-world problems are often multi-objective with decision-makers unable to
specify a priori which trade-off between the conflicting objectives is
preferable. Intuitively, building machine learning solutions in such cases
would entail providing multiple predictions that span and uniformly cover the
Pareto front of all optimal trade-off solutions. We propose a novel approach
for multi-objective training of neural networks to approximate the Pareto front
during inference. In our approach, the neural networks are trained
multi-objectively using a dynamic loss function, wherein each network's losses
(corresponding to multiple objectives) are weighted by their hypervolume
maximizing gradients. We discuss and illustrate why training processes to
approximate Pareto fronts need to optimize on fronts of individual training
samples instead of on only the front of average losses. Experiments on three
multi-objective problems show that our approach returns outputs that are
well-spread across different trade-offs on the approximated Pareto front
without requiring the trade-off vectors to be specified a priori. Further,
results of comparisons with the state-of-the-art approaches highlight the added
value of our proposed approach, especially in asymmetric Pareto fronts.

提出了一种使用动态损失函数来进行多目标训练神经网络以逼近 Pareto 前沿的新方法，在三个多目标问题上的实验表明，本方法无需预先指定权衡向量即可以返回分布在不同权衡方案上的输出，并且与现有技术相比具有更多的优势，尤其是对于非对称 Pareto 前沿。