我们开发了一种通用机制，用于从概率的驾驶行为基础模型中生成车辆型特定路线序列。许多基础行为模型都经过了不包括车辆信息的数据培训，这些数据限制了其在下游应用程序（例如计划）中的实用性。我们的新方法有条件地将这种行为预测模型专门为媒介物类型，通过利用用于生产特定车辆控制器的增强学习算法的副产品。我们展示了如何使用通用的概率行为模型组成车辆特定的价值函数估计，以生成车辆型特定的路线序列，而这些序列序列更可能在物理上是可行的，而不是其车辆敏捷的序列。
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我们提出了具有可拖动的对数密度的集合数据值数据的新型，有条件的生成概率模型。该模型是由置换模化动力学控制的连续归一化流。这些动力学是由可学习的每集元素项和成对相互作用的驱动的，均通过深神经网络参数化。我们通过应用程序说明了该模型的实用性，包括（1）以视觉上指定的地图信息为条件的复杂交通场景生成，以及（2）直接在图像上调节的对象边界框生成。我们借助罚款，可确保动力学平稳并因此有效解决，我们通过最大程度地提高标记有条件数据标记的条件数据的预期可能性来训练我们的模型。我们的方法在对数的可能性和特定于域特异性指标（越野，碰撞和违规违规）方面极大地超过了非渗透不变基线，从而产生了很难与真实数据区分的现实样本。
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我们为视频建模提供了一个框架，该框架基于deo的扩散概率模型，该模型在各种现实的环境中产生长期视频完成。我们介绍了一个生成模型，该模型可以在测试时间样本中任何任意子集的视频帧的任何任意子集，该视频框架以其他任何子集为条件，并为此提供了适合此目的的体系结构。这样做可以使我们有效地比较和优化各种时间表，以对长视频中的帧进行采样，并在先前采样的帧上使用选择性稀疏和长距离调节。我们证明了对许多数据集的先前工作的改进的视频建模，并在25分钟内进行了临时连贯的视频。我们还根据Carla自动驾驶汽车模拟器中生成的视频发布了一个新的视频建模数据集和语义上有意义的指标。
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联想记忆在人类智力中起着重要作用，其机制与机器学习中的注意力有关。尽管机器学习社区对关联记忆的兴趣最近被重新燃起，但大多数工作都集中在内存学习（$ read $）上（$ write $）。在本文中，我们介绍了BayESPCN，这是一种层次结构的关联记忆，能够执行连续的单发记忆而没有元学习。此外，Bayespcn能够逐渐忘记过去的观察结果（$忘记$）来释放其内存。实验表明，BayESPCN可以回忆起损坏的I.I.D.高维数据观察到数百个“时间段”，与最先进的离线学习的关联记忆模型相比，召回能力没有明显下降的能力下降。
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现代深度学习需要大规模广泛标记的数据集进行培训。少量学习旨在通过有效地从少数标记的例子中学习来缓解这个问题。在先前提出的少量视觉分类器中，假设对分类器决定的特征歧管具有不相关的特征尺寸和均匀特征方差。在这项工作中，我们专注于通过提出以低标签制度运行的差异敏感的模型来解决这一假设引起的限制。第一种方法简单的CNAP，采用基于分层正规的Mahalanobis距离基于距离的分类器，与现有神经自适应特征提取器的状态相结合，以在元数据集，迷你成像和分层图像基准基准上实现强大性能。我们进一步将这种方法扩展到转换学习设置，提出转导压盖。这种转换方法将软k-means参数细化过程与两步任务编码器相结合，以实现使用未标记数据的改进的测试时间分类精度。转导CNAP在元数据集上实现了最先进的性能。最后，我们探讨了我们的方法（简单和转换）的使用“开箱即用”持续和积极的学习。大规模基准的广泛实验表明了这一点的鲁棒性和多功能性，相对说话，简单的模型。所有培训的模型检查点和相应的源代码都已公开可用。
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Recent work has shown the benefits of synthetic data for use in computer vision, with applications ranging from autonomous driving to face landmark detection and reconstruction. There are a number of benefits of using synthetic data from privacy preservation and bias elimination to quality and feasibility of annotation. Generating human-centered synthetic data is a particular challenge in terms of realism and domain-gap, though recent work has shown that effective machine learning models can be trained using synthetic face data alone. We show that this can be extended to include the full body by building on the pipeline of Wood et al. to generate synthetic images of humans in their entirety, with ground-truth annotations for computer vision applications. In this report we describe how we construct a parametric model of the face and body, including articulated hands; our rendering pipeline to generate realistic images of humans based on this body model; an approach for training DNNs to regress a dense set of landmarks covering the entire body; and a method for fitting our body model to dense landmarks predicted from multiple views.
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Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is challenging and cannot be guaranteed in a finite time. We propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.
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We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic Inference (A-NeSI): a new framework for PNL that uses neural networks for scalable approximate inference. A-NeSI 1) performs approximate inference in polynomial time without changing the semantics of probabilistic logics; 2) is trained using data generated by the background knowledge; 3) can generate symbolic explanations of predictions; and 4) can guarantee the satisfaction of logical constraints at test time, which is vital in safety-critical applications. Our experiments show that A-NeSI is the first end-to-end method to scale the Multi-digit MNISTAdd benchmark to sums of 15 MNIST digits, up from 4 in competing systems. Finally, our experiments show that A-NeSI achieves explainability and safety without a penalty in performance.
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Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became the main benchmark. In this work, we are going to show how two simple methods: pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.
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The Makespan Scheduling problem is an extensively studied NP-hard problem, and its simplest version looks for an allocation approach for a set of jobs with deterministic processing times to two identical machines such that the makespan is minimized. However, in real life scenarios, the actual processing time of each job may be stochastic around the expected value with a variance, under the influence of external factors, and the actual processing times of these jobs may be correlated with covariances. Thus within this paper, we propose a chance-constrained version of the Makespan Scheduling problem and investigate the theoretical performance of the classical Randomized Local Search and (1+1) EA for it. More specifically, we first study two variants of the Chance-constrained Makespan Scheduling problem and their computational complexities, then separately analyze the expected runtime of the two algorithms to obtain an optimal solution or almost optimal solution to the instances of the two variants. In addition, we investigate the experimental performance of the two algorithms for the two variants.
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