Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels.

机器学习中的许多基本问题可以通过convex程序\ [\ min _ {\ theta \ in r^d} \ sum_ {i = 1}^{n} f_ {i}（\ theta），\]每个$ f_i $都是一个凸，Lipschitz函数在$ \ theta $的$ d_i $坐标的子集中支持。以随机梯度下降为例，解决此问题的一种常见方法涉及在每次迭代时对一个$ f_i $术语进行采样以取得进展。这种方法至关重要地依赖于$ f_i $的均匀性概念，该概念正式通过其状况编号捕获。在这项工作中，我们给出了一种将上述凸公式最小化为$ \ epsilon $ -Accuracy in $ \ widetilde {o}（\ sum_ {i = 1}^n d_i \ log（1 /\ epsilon）$计算，没有关于条件号的假设。以前的最佳算法独立于条件编号是标准切割平面方法，它需要$ o（nd \ log（1/\ epsilon））$渐变计算。作为推论，我们改善了Axiotis等人的评估甲骨文的复杂性，可分解性下的最小化。 （ICML 2021）。我们的主要技术贡献是一种自适应程序，可以通过切割平面和内点方法的新型组合在每次迭代中选择$ f_i $项。

尽管近年来从CT/MRI扫描中自动腹部多器官分割取得了很大进展，但由于缺乏各种临床方案的大规模基准，对模型的能力的全面评估受到阻碍。收集和标记3D医学数据的高成本的限制，迄今为止的大多数深度学习模型都由具有有限数量的感兴趣或样品器官的数据集驱动，这仍然限制了现代深层模型的力量提供各种方法的全面且公平的估计。为了减轻局限性，我们提出了AMO，这是一个大规模，多样的临床数据集，用于腹部器官分割。 AMOS提供了从多中心，多供应商，多模式，多相，多疾病患者收集的500 CT和100次MRI扫描，每个患者均具有15个腹部器官的体素级注释，提供了具有挑战性的例子，并提供了挑战性的例子和测试结果。在不同的目标和场景下研究健壮的分割算法。我们进一步基准了几种最先进的医疗细分模型，以评估此新挑战性数据集中现有方法的状态。我们已公开提供数据集，基准服务器和基线，并希望激发未来的研究。信息可以在https://amos22.grand-challenge.org上找到。

本文提出了一种基于逆变器的Volt-VAR控制（IB-VVC）的一步两级深度强化学习（OSTC-DRL）方法。首先，考虑IB-VVC可以作为单周期优化问题进行配制，我们将IB-VVC作为单步马尔可夫决策过程而不是标准的Markov决策过程，从而简化了DRL学习任务。然后，我们设计了单步角色批判性DRL方案，该方案是最近DRL算法的简化版本，它可以成功地避免了Q值高估的问题。此外，考虑VVC的两个目标：最大程度地减少功率损耗并消除违反电压，我们利用两个批评家分别近似两个目标的回报。它简化了每个评论家的近似任务，并避免了评论家学习过程中两个目标之间的相互作用效果。 OSTC-DRL方法集成了单步角色批判性DRL方案和两批评技术。基于OSTC-DRL，我们设计了两种集中式DRL算法。此外，我们将OSTC-DRL扩展到分散的IB-VVC的多代理OSTC-DRL并设计两个多代理DRL算法。模拟表明，所提出的OSTC-DRL具有更快的收敛速度和更好的控制性能，并且多代理OSTC-DRL适用于分散的IB-VVC问题。

部分微分方程（PDES）在科学和工程的许多学科中都是普遍的，难以解决。通常，PDE的闭合形式溶液不可用，数值近似方法是计算昂贵的。 PDE的参数在许多应用中是可变的，例如逆问题，控制和优化，风险评估和不确定性量化。在这些应用程序中，我们的目标是解决参数PDE而不是其中一个实例。我们所提出的方法，称为元 - 自动解码器（MAD），将参数PDES作为元学习问题求解，并利用\ Cite {Park2019DeepsDF}中的自动解码器结构来处理不同的任务/ PDE。从PDE管理方程和边界条件诱导的物理知识损失被用作不同任务的培训损失。疯狂的目标是学习一个良好的模型初始化，可以概括不同的任务，最终使未能学习的任务能够更快地学习。疯狂的灵感来自于（猜想）参数PDE解决方案的低维结构，并从流形学习的角度解释了我们的方法。最后，我们展示了疯狂的力量，虽然广泛的数值研究，包括汉堡等式，拉普尔斯方程和时域麦克斯韦方程。与其他深度学习方法相比，MAD表现出更快的收敛速度而不会失去准确性。

向外配送（OOD）数据的概括是现代机器学习中的核心问题之一。最近，试图提出主要建立在提取不变特征的想法上的算法。虽然直观地合理，但理论上了解如何保证ood泛化仍然有限，并且任意分配的概括显然是不可能的。在这项工作中，我们将第一步迈向严格和定量定义1）什么是ood; 2）通过说ood问题是学习的，这是什么意思。我们还介绍了扩展功能的新概念，其特征在于训练域的测试域中的方差在多大程度上放大，因此提供了不变特征的定量含义。基于这些，我们证明了ood泛化误差界限。事实证明，OOD泛化在很大程度上取决于扩展功能。正如Gulrajani和Lopez-PAZ（2020）所指出的那样，任何没有模型选择模块的学习算法都是不完整的。我们的理论自然地诱导了模型选择标准。基准OOD数据集的广泛实验表明，我们的模型选择标准在基线上具有显着的优势。

Accurate determination of a small molecule candidate (ligand) binding pose in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamics. We develop a tiered tensor transform (3T) algorithm to rapidly generate diverse protein-ligand complex conformations for both pose and affinity estimation in drug screening, requiring neither machine learning training nor lengthy dynamics computation, while maintaining both coarse-grain-like coordinated protein dynamics and atomistic-level details of the complex pocket. The 3T conformation structures we generate are closer to experimental co-crystal structures than those generated by docking software, and more importantly achieve significantly higher accuracy in active ligand classification than traditional ensemble docking using hundreds of experimental protein conformations. 3T structure transformation is decoupled from the system physics, making future usage in other computational scientific domains possible.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

In contrast to the control-theoretic methods, the lack of stability guarantee remains a significant problem for model-free reinforcement learning (RL) methods. Jointly learning a policy and a Lyapunov function has recently become a promising approach to ensuring the whole system with a stability guarantee. However, the classical Lyapunov constraints researchers introduced cannot stabilize the system during the sampling-based optimization. Therefore, we propose the Adaptive Stability Certification (ASC), making the system reach sampling-based stability. Because the ASC condition can search for the optimal policy heuristically, we design the Adaptive Lyapunov-based Actor-Critic (ALAC) algorithm based on the ASC condition. Meanwhile, our algorithm avoids the optimization problem that a variety of constraints are coupled into the objective in current approaches. When evaluated on ten robotic tasks, our method achieves lower accumulated cost and fewer stability constraint violations than previous studies.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.