自从各种任务的自动化开始以来，自动驾驶车辆一直引起人们的兴趣。人类容易疲惫，在道路上的响应时间缓慢，最重要的是，每年约有135万道路交通事故死亡，这已经是一项危险的任务。预计自动驾驶可以减少世界上驾驶事故的数量，这就是为什么这个问题对研究人员感兴趣的原因。目前，自动驾驶汽车在使车辆自动驾驶时使用不同的算法来实现各种子问题。我们将重点关注增强学习算法，更具体地说是Q学习算法和增强拓扑的神经进化（NEAT），即进化算法和人工神经网络的组合，以训练模型代理，以学习如何在给定路径上驱动。本文将重点介绍上述两种算法之间的比较。
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With time, machine learning models have increased in their scope, functionality and size. Consequently, the increased functionality and size of such models requires high-end hardware to both train and provide inference after the fact. This paper aims to explore the possibilities within the domain of model compression, discuss the efficiency of combining various levels of pruning and quantization, while proposing a quality measurement metric to objectively decide which combination is best in terms of minimizing the accuracy delta and maximizing the size reduction factor.
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Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.
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从几个培训示例中不断学习新课程，而不忘记以前的旧课程需要一个灵活的体系结构，而不可避免地会增加部分存储，其中可以逐步存储并有效地检索新的示例和类。一个可行的架构解决方案是将固定的深神经网络紧密融合到动态发展的明确记忆（EM）。作为该体系结构的核心，我们提出了一个EM单元，该单元在持续学习操作过程中利用节能中的内存计算（IMC）核心。我们首次证明了EM单元如何使用基于IMC Core上的操作（PCM）上的IMC核心操作，在推理期间进行了多个训练示例，扩展以适应看不见的类并进行相似性搜索。具体而言，通过PCM设备的原位进行性结晶实现了一些编码训练示例的物理叠加。与不断学习的最新完整精确基线软件模型相比，IMC核心上达到的分类精度在1.28％ - 2.5％范围内保持在2.5％之内。在60个旧课程的顶部，新颖的课程（每班只有五个示例）。
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