Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.

Diffusion models have shown great promise for image generation, beating GANs in terms of generation diversity, with comparable image quality. However, their application to 3D shapes has been limited to point or voxel representations that can in practice not accurately represent a 3D surface. We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder. This allows us to generate diverse and high quality 3D surfaces. We additionally show that we can condition our model on images or text to enable image-to-3D generation and text-to-3D generation using CLIP embeddings. Furthermore, adding noise to the latent codes of existing shapes allows us to explore shape variations.

使用通过组成可逆层获得的地图进行标准化模型复杂概率分布。特殊的线性层（例如蒙版和1x1卷积）在现有体系结构中起着关键作用，因为它们在具有可拖动的Jacobians和倒置的同时增加表达能力。我们提出了一个基于蝴蝶层的新的可逆线性层家族，理论上捕获复杂的线性结构，包括排列和周期性，但可以有效地倒置。这种代表力是我们方法的关键优势，因为这些结构在许多现实世界数据集中很常见。根据我们的可逆蝴蝶层，我们构建了一个新的称为蝴蝶流的归一化流量模型。从经验上讲，我们证明蝴蝶不仅可以在MNIST，CIFAR-10和Imagenet 32​​x32等自然图像上实现强密度估计结果，而且还可以在结构化数据集中获得明显更好的对数可能性，例如Galaxy图像和Mimic-III患者群体 - - 同时，在记忆和计算方面比相关基线更有效。

The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a novel grasping network named TGCNN is proposed for grasping position detection, showing good results in both synthetic and real scenes. In tactile calibration, inspired by human grasping, a fully convolutional network based tactile feature extraction method and a central location based adaptive grasping strategy are designed, improving the success rate by 36.7% compared to direct grasping. Furthermore, a visual-tactile fusion method is proposed for transparent objects classification, which improves the classification accuracy by 34%. The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects.

Humans can balance very well during walking, even when perturbed. But it seems difficult to achieve robust walking for bipedal robots. Here we describe the simplest balance controller that leads to robust walking for a linear inverted pendulum (LIP) model. The main idea is to use a linear function of the body velocity to determine the next foot placement, which we call linear foot placement control (LFPC). By using the Poincar\'e map, a balance criterion is derived, which shows that LFPC is stable when the velocity-feedback coefficient is located in a certain range. And that range is much bigger when stepping faster, which indicates "faster stepping, easier to balance". We show that various gaits can be generated by adjusting the controller parameters in LFPC. Particularly, a dead-beat controller is discovered that can lead to steady-state walking in just one step. The effectiveness of LFPC is verified through Matlab simulation as well as V-REP simulation for both 2D and 3D walking. The main feature of LFPC is its simplicity and inherent robustness, which may help us understand the essence of how to maintain balance in dynamic walking.

基于嵌入的神经主题模型可以通过将它们嵌入均匀的特征空间来明确表示单词和主题，从而显示出更高的解释性。但是，嵌入训练没有明确的限制，从而导致更大的优化空间。此外，仍然缺乏对嵌入的变化以及对模型性能的影响的清晰描述。在本文中，我们提出了一个嵌入式化的神经主题模型，该模型应用于单词嵌入和主题嵌入的特殊设计的训练约束，以减少参数的优化空间。为了揭示嵌入的变化和角色，我们将\ textbf {均匀性}引入基于嵌入的神经主题模型中，作为嵌入空间的评估度量。在此基础上，我们描述了嵌入在训练过程中如何通过嵌入均匀性的变化而变化。此外，我们通过消融研究证明了基于嵌入的神经主题模型中嵌入的变化的影响。在两个主流数据集上实验的结果表明，我们的模型在主题质量和文档建模之间的和谐方面显着优于基线模型。这项工作是利用统一性来探索基于嵌入的神经主题模型嵌入的变化及其对模型性能的影响，从而获得了我们的最佳知识。

通信瓶颈和数据隐私是联邦多武装强盗（MAB）问题中的两个至关重要的问题，例如通过无线连接车辆的决策和建议的情况。在本文中，我们在这些问题中设计了隐私保留的通信有效的算法，并在遗憾方面研究隐私，沟通和学习绩效之间的互动。具体而言，我们设计隐私保留的学习算法和通信协议，并在网络私人代理在主工作人员，分散和混合结构中进行在线强盗学习时，从而导出学习遗憾。我们的强盗学习算法基于每个代理和代理在每个时代结束时与服务器/彼此交换学习知识的庞大的子最优手臂。此外，我们采用差异隐私（DP）方法在交换信息时保护每个代理人的数据隐私;并且我们通过减少频繁的沟通与较少的代理商参与来缩短沟通成本。通过分析我们拟议的算法框架，在硕士劳动，分散和混合结构中的暗示框架，理论上显示了遗憾和沟通成本/隐私之间的权衡。最后，我们经验展示了与我们理论分析一致的这些权衡。

对话语义作用标签（CSRL）被认为是对话理解的关键步骤。但是，对于现有的CSRL解析器来处理会话结构信息仍然是一个重大挑战。在本文中，我们为CSRL提出了一个简单有效的架构，旨在解决这个问题。我们的模型基于对话结构感知的图形网络，该图网络网络明确地编码了扬声器相关信息。我们还提出了一种多任务学习方法来进一步改进模型。基准数据集的实验结果表明，我们的模型与我们建议的培训目标显着优于以前的基准。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.