最近，神经辐射场（NERF）在重建3D场景并从一组稀疏的2D图像中综合新视图方面表现出了有希望的表演。尽管有效，但NERF的性能受到训练样品质量的很大影响。由于现场有限的图像，Nerf无法很好地概括到新颖的观点，并可能崩溃到未观察到的区域中的琐碎解决方案。这使得在资源约束的情况下不切实际。在本文中，我们提出了一个新颖的学习框架Activenerf，旨在模拟一个3D场景，并具有限制的输入预算。具体而言，我们首先将不确定性估计纳入NERF模型，该模型在很少的观察下确保了鲁棒性，并提供了NERF如何理解场景的解释。在此基础上，我们建议根据积极学习方案将现有的培训设置补充新捕获的样本。通过评估给定新输入的不确定性的降低，我们选择了带来最多信息增益的样本。这样，可以通过最少的额外资源来提高新型视图合成的质量。广泛的实验验证了我们模型在现实和合成场景上的性能，尤其是在稀缺的训练数据中。代码将在\ url {https://github.com/leaplabthu/activenerf}上发布。

早期退出是提高深网推理效率的有效范例。通过构建具有不同资源需求的分类器（出口），此类网络可以在早期出口处输出简单的样本，从而消除了执行更深层的需求。尽管现有作品主要关注多EXIT网络的建筑设计，但此类模型的培训策略在很大程度上没有探索。当前的最新模型在培训期间对所有样品进行了相同的处理。但是，在测试过程中的早期外观行为被忽略了，从而导致训练和测试之间存在差距。在本文中，我们建议通过样品加权来弥合这一差距。从直觉上讲，简单的样品通常在推理期间在网络早期退出，应该为培训早期分类器提供更多贡献。但是，晚期分类器应强调硬样品的培训（主要是从更深层退出）。我们的工作建议采用一个体重预测网络，以加重每个出口处不同训练样本的损失。这个重量预测网络和骨干模型在具有新的优化目标的元学习框架下共同优化。通过将推断期间的适应性行为带入训练阶段，我们表明拟议的加权机制始终提高分类准确性和推理效率之间的权衡。代码可在https://github.com/leaplabthu/l2w-den上找到。

本文提出了一种可扩展的方法，用于同时学习单个令牌和整体实例表示的分布式表示。我们使用自我注意解区块代表分布式令牌，然后是跨注意区块来汇总整体实例。该方法的核心是使用极大的令牌掩蔽（75％-90％）作为监督的数据增加。我们的模型命名为Oxtreara，遵循普通的BYOL方法，其中训练了来自未掩盖子集的实例表示从完整的输入中预测。学习需要模型在实例中捕获信息的变化，而不是鼓励不变。本文有三个贡献：1）随机掩盖是一种强大而有效的数据增强，用于学习可推广的注意力表示。 2）每个实例进行多次抽样，极端掩盖会大大加快学习的速度，并渴望获得更多数据。 3）与蒙版建模中的to徒监督不同，可以单独从实例监督中学到分布式表示形式。

最近的作品表明，通过降低空间冗余，可以显着提高视频识别的计算效率。作为代表性的工作，自适应焦点方法（Adafocus）通过动态识别和参加每个视频帧中的信息区域来实现精度和推理速度之间的有利权衡。然而，除非领需要一个复杂的三阶段训练管道（涉及强化学习），导致收敛缓慢，对从业者不友好。这项工作通过引入基于分配的内插的补丁选择操作来重新重新培训ADAFOCUS作为简单的单级算法，实现有效的端到端优化。我们进一步提出了一种改进的培训计划，以解决一级制定的问题，包括缺乏监督，投入多样性和培训稳定性。此外，提出了一种条件 - 退出技术，用于在没有额外训练的情况下在Adafocus的顶部执行时间自适应计算。在六个基准数据集（即，ActivityNet，FCVID，Mini-Kinetics，Something-V1＆V2和Jesters）上进行了广泛的实验表明，我们的模型显着优于原始的Adafocus和其他竞争基础，同时培训更简单和有效。代码可在https://github.com/leaplabthu/adafocusv2获得。

With the fast development of big data, it has been easier than before to learn the optimal decision rule by updating the decision rule recursively and making online decisions. We study the online statistical inference of model parameters in a contextual bandit framework of sequential decision-making. We propose a general framework for online and adaptive data collection environment that can update decision rules via weighted stochastic gradient descent. We allow different weighting schemes of the stochastic gradient and establish the asymptotic normality of the parameter estimator. Our proposed estimator significantly improves the asymptotic efficiency over the previous averaged SGD approach via inverse probability weights. We also conduct an optimality analysis on the weights in a linear regression setting. We provide a Bahadur representation of the proposed estimator and show that the remainder term in the Bahadur representation entails a slower convergence rate compared to classical SGD due to the adaptive data collection.

Model counting is a fundamental problem which has been influential in many applications, from artificial intelligence to formal verification. Due to the intrinsic hardness of model counting, approximate techniques have been developed to solve real-world instances of model counting. This paper designs a new anytime approach called PartialKC for approximate model counting. The idea is a form of partial knowledge compilation to provide an unbiased estimate of the model count which can converge to the exact count. Our empirical analysis demonstrates that PartialKC achieves significant scalability and accuracy over prior state-of-the-art approximate counters, including satss and STS. Interestingly, the empirical results show that PartialKC reaches convergence for many instances and therefore provides exact model counting performance comparable to state-of-the-art exact counters.

Robots are traditionally bounded by a fixed embodiment during their operational lifetime, which limits their ability to adapt to their surroundings. Co-optimizing control and morphology of a robot, however, is often inefficient due to the complex interplay between the controller and morphology. In this paper, we propose a learning-based control method that can inherently take morphology into consideration such that once the control policy is trained in the simulator, it can be easily deployed to robots with different embodiments in the real world. In particular, we present the Embodiment-aware Transformer (EAT), an architecture that casts this control problem as conditional sequence modeling. EAT outputs the optimal actions by leveraging a causally masked Transformer. By conditioning an autoregressive model on the desired robot embodiment, past states, and actions, our EAT model can generate future actions that best fit the current robot embodiment. Experimental results show that EAT can outperform all other alternatives in embodiment-varying tasks, and succeed in an example of real-world evolution tasks: stepping down a stair through updating the morphology alone. We hope that EAT will inspire a new push toward real-world evolution across many domains, where algorithms like EAT can blaze a trail by bridging the field of evolutionary robotics and big data sequence modeling.

Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.

Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i.e., sim-to-real transfer). Despite considerable progress, the capacity and scalability of traditional neural networks are still limited, which may hinder their applications in more complex environments. In contrast, the Transformer architecture has shown its superiority in a wide range of large-scale sequence modeling tasks, including natural language processing and decision-making problems. In this paper, we propose Terrain Transformer (TERT), a high-capacity Transformer model for quadrupedal locomotion control on various terrains. Furthermore, to better leverage Transformer in sim-to-real scenarios, we present a novel two-stage training framework consisting of an offline pretraining stage and an online correction stage, which can naturally integrate Transformer with privileged training. Extensive experiments in simulation demonstrate that TERT outperforms state-of-the-art baselines on different terrains in terms of return, energy consumption and control smoothness. In further real-world validation, TERT successfully traverses nine challenging terrains, including sand pit and stair down, which can not be accomplished by strong baselines.

Graphene quantum dots provide a platform for manipulating electron behaviors in two-dimensional (2D) Dirac materials. Most previous works were of the "forward" type in that the objective was to solve various confinement, transport and scattering problems with given structures that can be generated by, e.g., applying an external electrical field. There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved: finding a quantum-dot structure according to certain desired functional characteristics. A brute-force search of the system configuration based directly on the solutions of the Dirac equation is computational infeasible. We articulate a machine-learning approach to addressing the inverse-design problem where artificial neural networks subject to physical constraints are exploited to replace the rigorous Dirac equation solver. In particular, we focus on the problem of designing a quantum dot structure to generate both cloaking and superscattering in terms of the scattering efficiency as a function of the energy. We construct a physical loss function that enables accurate prediction of the scattering characteristics. We demonstrate that, in the regime of Klein tunneling, the scattering efficiency can be designed to vary over two orders of magnitudes, allowing any scattering curve to be generated from a proper combination of the gate potentials. Our physics-based machine-learning approach can be a powerful design tool for 2D Dirac material-based electronics.