基于得分的扩散模型是一类生成模型，其动力学由将噪声映射到数据中的随机微分方程描述。尽管最近的作品已经开始为这些模型奠定理论基础，但仍缺乏对扩散时间t的作用的分析理解。当前的最佳实践提倡大型T，以确保正向动力学使扩散足够接近已知和简单的噪声分布。但是，对于更好的分数匹配目标和更高的计算效率，应优选较小的t值。从扩散模型的各种解释开始，在这项工作中，我们量化了这一权衡，并提出了一种新方法，通过采用较小的扩散时间来提高培训和采样的质量和效率。实际上，我们展示了如何使用辅助模型来弥合理想和模拟正向动力学之间的间隙，然后进行标准的反向扩散过程。经验结果支持我们的分析；对于图像数据，我们的方法是竞争性W.R.T.根据标准样本质量指标和对数可能的样本。

在多任务学习（MTL）中，通过优化目标函数来训练多任务网络是一种常见的做法，这是特定于任务的目标函数的加权平均值。尽管该策略的计算优势很明显，但在文献中尚未研究所得损失格局的复杂性。可以说，它的优化可能比对特定于任务的目标的单独优化更加困难。在这项工作中，我们通过在不同特定于任务的目标函数上交替进行独立的梯度下降步骤来研究这种替代方案的好处，并制定了一种新颖的方式，将这种方法与最先进的优化者相结合。由于特定于任务的目标的分离是以增加计算时间为代价的，因此我们提出了一个随机的任务分组，作为更好的优化和计算效率之间的权衡。与平均目标函数和其他最先进的MTL方法相比，三个众所周知的视觉MTL数据集的实验结果在损失和标准指标上显示出更好的总体绝对性能。特别是，我们的方法在处理不同性质的任务时显示出最大的好处，并且可以更广泛地探索共享参数空间。我们还表明，我们的随机分组策略允许在这些好处和计算效率之间进行权衡。

我们重新审视汉密尔顿随机微分方程（SDES）的理论属性，为贝叶斯后部采样，我们研究了来自数值SDE仿真的两种类型的误差：在数据附带的上下文中，离散化误差和由于噪声渐变估计而导致的错误。我们的主要结果是对迷你批次通过差分操作员分裂镜片影响的新颖分析，修改了先前的文献结果。Hamiltonian SDE的随机分量与梯度噪声分离，我们没有常规假设。这导致识别收敛瓶颈：在考虑迷你批次时，最佳可实现的错误率是$ \ mathcal {o}（\ eta ^ 2）$，带有$ \ eta $是集成器步长。我们的理论结果得到了贝叶斯神经网络各种回归和分类任务的实证研究。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.

Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views and do not operate in the 3D space. We propose a new method to perform self-supervised keypoint discovery in 3D from multi-view videos of behaving agents, without any keypoint or bounding box supervision in 2D or 3D. Our method uses an encoder-decoder architecture with a 3D volumetric heatmap, trained to reconstruct spatiotemporal differences across multiple views, in addition to joint length constraints on a learned 3D skeleton of the subject. In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.

Artificial intelligence is set to be deployed in operating rooms to improve surgical care. This early-stage clinical evaluation shows the feasibility of concurrently attaining real-time, high-quality predictions from several deep neural networks for endoscopic video analysis deployed for assistance during three laparoscopic cholecystectomies.

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses automatic metrics, which compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This practical experience report analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene with a disentangled latent scene graph representation, which we can then process using a graph neural network. Unlike previous approaches using graph representations, we explicitly encode in our graphs semantic information such as object locations and shapes, class probabilities and visual features. We also incorporate an auxiliary image reconstruction objective to help train the latent graph representations. We demonstrate the value of these components through comprehensive ablation studies and achieve state-of-the-art results for critical view of safety prediction across multiple experimental settings.

Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.