我们提出了一种新颖的端到端方法，用于在事件流中进行关键点检测和跟踪，该方法比以前的方法提供了更好的精度和更长的关键点轨道。两项贡献共同努力使这成为可能。首先，我们提出了一个简单的过程来生成稳定的关键点标签，我们用来训练复发架构。该培训数据导致检测随着时间的推移非常一致。此外，我们观察到以前的按键检测方法在一段时间内集成事件的表示形式（例如时间表面）。由于需要这种集成，因此我们声称最好预测时间段的关键点的轨迹，而不是单个位置，如先前的方法中所做的那样。我们以一系列热图的形式预测这些轨迹在整合时间段。这可以改善关键点本地化。我们的体系结构也可以保持非常简单，从而导致非常快的推理时间。我们在HVGA ATIS角数据集以及“事件相机数据集和模拟器”数据集上演示了我们的方法，并将其显示为“关键点”轨道的三倍，几乎是最好的先前最佳先前最佳先前的轨道轨迹。 - 艺术方法。我们认为我们的方法可以推广到其他基于事件的相机问题，并发布我们的源代码以鼓励其他作者探索它。

持续学习旨在使单个模型能够学习一系列任务，而不会造成灾难性的遗忘。表现最好的方法通常需要排练缓冲区来存储过去的原始示例以进行经验重播，但是，由于隐私和内存约束，这会限制其实际价值。在这项工作中，我们提出了一个简单而有效的框架，即DualPrompt，该框架学习了一组称为提示的参数，以正确指示预先训练的模型，以依次学习到达的任务，而不会缓冲过去的示例。 DualPrompt提出了一种新颖的方法，可以将互补提示附加到预训练的主链上，然后将目标提出为学习任务不变和特定于任务的“指令”。通过广泛的实验验证，双启示始终在具有挑战性的课堂开发环境下始终设置最先进的表现。尤其是，双启示的表现优于最近的高级持续学习方法，其缓冲尺寸相对较大。我们还引入了一个更具挑战性的基准Split Imagenet-R，以帮助概括无连续的持续学习研究。源代码可在https://github.com/google-research/l2p上找到。

持续学习背后的主流范例一直在使模型参数调整到非静止数据分布，灾难性遗忘是中央挑战。典型方法在测试时间依赖排练缓冲区或已知的任务标识，以检索学到的知识和地址遗忘，而这项工作呈现了一个新的范例，用于持续学习，旨在训练更加简洁的内存系统而不在测试时间访问任务标识。我们的方法学会动态提示（L2P）预先训练的模型，以在不同的任务转换下顺序地学习任务。在我们提出的框架中，提示是小型可学习参数，这些参数在内存空间中保持。目标是优化提示，以指示模型预测并明确地管理任务不变和任务特定知识，同时保持模型可塑性。我们在流行的图像分类基准下进行全面的实验，具有不同挑战的持续学习环境，其中L2P始终如一地优于现有最先进的方法。令人惊讶的是，即使没有排练缓冲区，L2P即使没有排练缓冲，L2P也能实现竞争力的结果，并直接适用于具有挑战性的任务不可行的持续学习。源代码在https://github.com/google-Research/l2p中获得。

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.

There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions. Our artificial scientists not only learn to answer given questions, but also continually invent new questions, by proposing hypotheses to be verified or falsified through potentially complex and time-consuming experiments, including thought experiments akin to those of mathematicians. While an artificial scientist expands its knowledge, it remains biased towards the simplest, least costly experiments that still have surprising outcomes, until they become boring. We present an empirical analysis of the automatic generation of interesting experiments. In the first setting, we investigate self-invented experiments in a reinforcement-providing environment and show that they lead to effective exploration. In the second setting, pure thought experiments are implemented as the weights of recurrent neural networks generated by a neural experiment generator. Initially interesting thought experiments may become boring over time.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

A statistical ensemble of neural networks can be described in terms of a quantum field theory (NN-QFT correspondence). The infinite-width limit is mapped to a free field theory, while finite N corrections are mapped to interactions. After reviewing the correspondence, we will describe how to implement renormalization in this context and discuss preliminary numerical results for translation-invariant kernels. A major outcome is that changing the standard deviation of the neural network weight distribution corresponds to a renormalization flow in the space of networks.

We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we show that our method retrieves annotations that are at least as accurate as manual annotations, and can thus be used as ground truth without the burden of manually annotating 3D data. We do this using an analysis-by-synthesis approach, which compares renderings of the CAD models with the captured scene. We introduce a 'cloning procedure' that identifies objects that have the same geometry, to annotate these objects with the same CAD models. This allows us to obtain complete annotations for the ScanNet dataset and the recent ARKitScenes dataset.

This article presents a novel review of Active SLAM (A-SLAM) research conducted in the last decade. We discuss the formulation, application, and methodology applied in A-SLAM for trajectory generation and control action selection using information theory based approaches. Our extensive qualitative and quantitative analysis highlights the approaches, scenarios, configurations, types of robots, sensor types, dataset usage, and path planning approaches of A-SLAM research. We conclude by presenting the limitations and proposing future research possibilities. We believe that this survey will be helpful to researchers in understanding the various methods and techniques applied to A-SLAM formulation.

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-off between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.