We consider the problem of using SciML to predict solutions of high Mach
fluid flows over irregular geometries. In this setting, data is limited, and so
it is desirable for models to perform well in the low-data setting. We show
that Neural Basis Functions (NBF), which learns a basis of behavior modes from
the data and then uses this basis to make predictions, is more effective than a
basis-unaware baseline model. In addition, we identify continuing challenges in
the space of predicting solutions for this type of problem.

使用 SciML 预测不规则几何体上的高 Mach 气体流动的解决方案问题，并通过学习行为模式基础从数据中进行预测的 Neural Basis Functions 模型，在低数据情况下表现更加有效，同时识别了该类型问题中持续存在的挑战。

用于解决高马赫数流体流动问题的高效数据学习

Data-efficient operator learning for solving high Mach number fluid flow  problems

Syntactically controlled paraphrase generation requires language models to
generate paraphrases for sentences according to specific syntactic structures.
Existing fine-tuning methods for this task are costly as all the parameters of
the model need to be updated during the training process. Inspired by recent
studies on parameter-efficient learning, we propose Parse-Instructed Prefix
(PIP), a novel adaptation of prefix-tuning to tune large pre-trained language
models on syntactically controlled paraphrase generation task in a low-data
setting with significantly less training cost. We introduce two methods to
instruct a model's encoder prefix to capture syntax-related knowledge: direct
initiation (PIP-Direct) and indirect optimization (PIP-Indirect). In contrast
to traditional fine-tuning methods for this task, PIP is a compute-efficient
alternative with 10 times less learnable parameters. Compared to existing
prefix-tuning methods, PIP excels at capturing syntax control information,
achieving significantly higher performance at the same level of learnable
parameter count.

提出一种名为 PIP 的新型前缀调整模型，可以在较低计算成本和较少的学习参数的情况下，在特定的句法结构下执行重述生成任务，并显著提高语法控制信息的捕获和性能。

PIP: 基于解析指导的前缀法用于句法控制的释义生成

PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase  Generation

Dialogue State Tracking (DST) forms a core component of automated chatbot
based systems designed for specific goals like hotel, taxi reservation, tourist
information, etc. With the increasing need to deploy such systems in new
domains, solving the problem of zero/few-shot DST has become necessary. There
has been a rising trend for learning to transfer knowledge from resource-rich
domains to unknown domains with minimal need for additional data. In this work,
we explore the merits of meta-learning algorithms for this transfer and hence,
propose a meta-learner D-REPTILE specific to the DST problem. With extensive
experimentation, we provide clear evidence of benefits over conventional
approaches across different domains, methods, base models, and datasets with
significant (5-25%) improvement over the baseline in a low-data setting. Our
proposed meta-learner is agnostic of the underlying model and hence any
existing state-of-the-art DST system can improve its performance on unknown
domains using our training strategy.

本文提出了一种基于元学习算法的 D-REPTILE 模型，用于解决自动聊天机器人中少样本语料下的对话状态跟踪问题，并在不同领域、方法、基准模型和数据集中进行了广泛实验，结果表明该方法在低数据情况下具有显著的（5-25%）改进，并且适用于各种状态跟踪模型。