The effectiveness of univariate forecasting models is often hampered by
conditions that cause them stress. A model is considered to be under stress if
it shows a negative behaviour, such as higher-than-usual errors or increased
uncertainty. Understanding the factors that cause stress to forecasting models
is important to improve their reliability, transparency, and utility. This
paper addresses this problem by contributing with a novel framework called MAST
(Meta-learning and data Augmentation for Stress Testing). The proposed approach
aims to model and characterize stress in univariate time series forecasting
models, focusing on conditions where they exhibit large errors. In particular,
MAST is a meta-learning approach that predicts the probability that a given
model will perform poorly on a given time series based on a set of statistical
time series features. MAST also encompasses a novel data augmentation technique
based on oversampling to improve the metadata concerning stress. We conducted
experiments using three benchmark datasets that contain a total of 49.794 time
series to validate the performance of MAST. The results suggest that the
proposed approach is able to identify conditions that lead to large errors. The
method and experiments are publicly available in a repository.

本文提出了一种名为 MAST（元学习和数据增强用于压力测试）的新型框架，旨在对单变量时间序列预测模型中的压力进行建模和表征，重点关注其在出现大误差的条件下的表现；该方法基于一组统计时间序列特征，通过元学习预测给定时间序列上给定模型表现不佳的概率，同时还包括一种基于过采样的新型数据增强技术，以改进有关压力的元数据。通过使用包含 49,794 个时间序列的三个基准数据集进行实验证明，该方法能够识别导致大误差的条件。

元学习和数据增强用于压力测试预测模型

Meta-learning and Data Augmentation for Stress Testing Forecasting  Models

Recent Self-Supervised Learning (SSL) methods are able to learn feature
representations that are invariant to different data augmentations, which can
then be transferred to downstream tasks of interest. However, different
downstream tasks require different invariances for their best performance, so
the optimal choice of augmentations for SSL depends on the target task. In this
paper, we aim to learn self-supervised features that generalize well across a
variety of downstream tasks (e.g., object classification, detection and
instance segmentation) without knowing any task information beforehand. We do
so by Masked Augmentation Subspace Training (or MAST) to encode in the single
feature space the priors from different data augmentations in a factorized way.
Specifically, we disentangle the feature space into separate subspaces, each
induced by a learnable mask that selects relevant feature dimensions to model
invariance to a specific augmentation. We show the success of MAST in jointly
capturing generalizable priors from different augmentations, using both unique
and shared features across the subspaces. We further show that MAST benefits
from uncertainty modeling to reweight ambiguous samples from strong
augmentations that may cause similarity mismatch in each subspace. Experiments
demonstrate that MAST consistently improves generalization on various
downstream tasks, while being task-agnostic and efficient during SSL. We also
provide interesting insights about how different augmentations are related and
how uncertainty reflects learning difficulty.

本文提出了一种名为 MAST 的方法，通过学习不同数据增强的先验知识来在单个特征空间中根据任务要求建模不变性，实现在不知道任务信息的前提下进行自监督特征学习，并通过不确定性建模来解决强增强的相似性失配问题，结果表明该方法可显著提高自监督学习通用性和泛化性能。

MAST: 带掩码的数据增强子空间训练，用于具有一般性自监督先验知识

MAST: Masked Augmentation Subspace Training for Generalizable  Self-Supervised Priors

This paper presents MAST, a new model for Multimodal Abstractive Text
Summarization that utilizes information from all three modalities -- text,
audio and video -- in a multimodal video. Prior work on multimodal abstractive
text summarization only utilized information from the text and video
modalities. We examine the usefulness and challenges of deriving information
from the audio modality and present a sequence-to-sequence trimodal
hierarchical attention-based model that overcomes these challenges by letting
the model pay more attention to the text modality. MAST outperforms the current
state of the art model (video-text) by 2.51 points in terms of Content F1 score
and 1.00 points in terms of Rouge-L score on the How2 dataset for multimodal
language understanding.

本文提出了 MAST，一种新的多模态抽象文本摘要模型，它利用来自文本、音频和视频三种模态的信息。MAST 通过让模型更多地关注文本模态来解决从音频模态中提取信息的有用性和挑战，并在 How2 数据集上在多模态语言理解方面以 2.51 分的内容 F1 分数和 1.00 分的 Rouge-L 分数优于目前的基于视频和文本的最佳模型。