Learning complex multi-agent system dynamics from data is crucial across many
domains, such as in physical simulations and material modeling. Extended from
purely data-driven approaches, existing physics-informed approaches such as
Hamiltonian Neural Network strictly follow energy conservation law to introduce
inductive bias, making their learning more sample efficiently. However, many
real-world systems do not strictly conserve energy, such as spring systems with
frictions. Recognizing this, we turn our attention to a broader physical
principle: Time-Reversal Symmetry, which depicts that the dynamics of a system
shall remain invariant when traversed back over time. It still helps to
preserve energies for conservative systems and in the meanwhile, serves as a
strong inductive bias for non-conservative, reversible systems. To inject such
inductive bias, in this paper, we propose a simple-yet-effective
self-supervised regularization term as a soft constraint that aligns the
forward and backward trajectories predicted by a continuous graph neural
network-based ordinary differential equation (GraphODE). It effectively imposes
time-reversal symmetry to enable more accurate model predictions across a wider
range of dynamical systems under classical mechanics. In addition, we further
provide theoretical analysis to show that our regularization essentially
minimizes higher-order Taylor expansion terms during the ODE integration steps,
which enables our model to be more noise-tolerant and even applicable to
irreversible systems. Experimental results on a variety of physical systems
demonstrate the effectiveness of our proposed method. Particularly, it achieves
an MSE improvement of 11.5 % on a challenging chaotic triple-pendulum systems.

本文介绍了一种基于时间反演对称性的自我监督正则化方法，通过连续图神经网络的常微分方程预测前后轨迹，以更准确地模拟广泛范围的经典力学动力学系统，并通过实验证明了该方法的有效性。

TANGO: 多智能体动态系统的时空反转潜在图 ODE

TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems

Models should be able to adapt to unseen data during test-time to avoid
performance drops caused by inevitable distribution shifts in real-world
deployment scenarios. In this work, we tackle the practical yet challenging
test-time adaptation (TTA) problem, where a model adapts to the target domain
without accessing the source data. We propose a simple recipe called
\textit{Data-efficient Prompt Tuning} (DePT) with two key ingredients. First,
DePT plugs visual prompts into the vision Transformer and only tunes these
source-initialized prompts during adaptation. We find such parameter-efficient
finetuning can efficiently adapt the model representation to the target domain
without overfitting to the noise in the learning objective. Second, DePT
bootstraps the source representation to the target domain by memory bank-based
online pseudo-labeling. A hierarchical self-supervised regularization specially
designed for prompts is jointly optimized to alleviate error accumulation
during self-training. With much fewer tunable parameters, DePT demonstrates not
only state-of-the-art performance on major adaptation benchmarks VisDA-C,
ImageNet-C, and DomainNet-126, but also superior data efficiency, i.e.,
adaptation with only 1\% or 10\% data without much performance degradation
compared to 100\% data. In addition, DePT is also versatile to be extended to
online or multi-source TTA settings.

本文提出一种用于数据有效且准确实现目标领域适应的测试时适应问题解决方案 (Data-efficient Prompt Tuning，DePT)，其使用镜像学习方式将交互式提示信息细化到视觉 Transformer 模型中，在测试时自适应调整模型引用的提示以提升模型对目标域的表示，且具有适用于许多测试适应问题的能力。

测试时间域自适应的视觉提示调控

Visual Prompt Tuning for Test-time Domain Adaptation

Recent advances in semi-supervised learning have shown tremendous potential
in overcoming a major barrier to the success of modern machine learning
algorithms: access to vast amounts of human-labeled training data. Previous
algorithms based on consistency regularization can harness the abundance of
unlabeled data to produce impressive results on a number of semi-supervised
benchmarks, approaching the performance of strong supervised baselines using
only a fraction of the available labeled data. In this work, we challenge the
long-standing success of consistency regularization by introducing
self-supervised regularization as the basis for combining semantic feature
representations from unlabeled data. We perform extensive comparative
experiments to demonstrate the effectiveness of self-supervised regularization
for supervised and semi-supervised image classification on SVHN, CIFAR-10, and
CIFAR-100 benchmark datasets. We present two main results: (1) models augmented
with self-supervised regularization significantly improve upon traditional
supervised classifiers without the need for unlabeled data; (2) together with
unlabeled data, our models yield semi-supervised performance competitive with,
and in many cases exceeding, prior state-of-the-art consistency baselines.
Lastly, our models have the practical utility of being efficiently trained
end-to-end and require no additional hyper-parameters to tune for optimal
performance beyond the standard set for training neural networks. Reference
code and data are available at this https URL

本文介绍了一种基于自监督正则化的半监督学习方法，其中利用了大量未标记数据来提高模型的性能，并且可以有效地应用于图像分类任务中，不需要任何补充的超参数进行调整。实验结果表明，该方法优于传统的监督和半监督学习方法。