当人类的学习者比平常更慢或更快时，人类的学习者可以轻松理解语音或旋律。尽管深度卷积神经网络（CNN）在提取时间序列的信息方面非常有力，但它们需要明确的培训才能推广到不同的时间尺度。本文提出了一个深入的CNN，其中包含了受神经科学最新发现启发的时间表示。在哺乳动物的大脑中，时间由具有时间接受场的神经元群体表示。至关重要的是，接收场的峰形成了几何序列，因此人口在日志时间内代码一组时间基础函数。因为最近的记忆是对数时间的函数，因此重新缩放输入导致内存翻译。比例不变的时间历史卷积网络（SITHCON）在该对数分布的时间内存上构建了卷积层。 Max-Pool操作导致一个网络，该网络是时间模量边缘效应的重新缩放。我们将SITHCON的性能与时间卷积网络（TCN）进行比较。尽管两个网络都可以在单变量和多变量时间序列F（t）上学习分类和回归问题，但仅Sithcon概括为recalings f（at）。这一属性受到当代神经科学的发现的启发，并且与认知心理学的发现一致，可以使网络能够以更少的培训示例，减少体重更少，并且更强大地从样本数据中概括。
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Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is especially important for semantic segmentation tasks involving 3D datasets that are often significantly smaller and more challenging to annotate than their image-based counterparts. Self-supervised pre-training on large unlabelled datasets is one way to reduce the amount of manual annotations needed. Previous work has focused on pre-training with point cloud data exclusively; this approach often requires two or more registered views. In the present work, we combine image and point cloud modalities, by first learning self-supervised image features and then using these features to train a 3D model. By incorporating image data, which is often included in many 3D datasets, our pre-training method only requires a single scan of a scene. We demonstrate that our pre-training approach, despite using single scans, achieves comparable performance to other multi-scan, point cloud-only methods.
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与痴呆症相关的认知障碍（CI）在全球范围内影响超过5500万人，并且每3秒钟以一个新病例的速度迅速增长。随着临床试验反复出现的失败，早期诊断至关重要，但是在低水平和中等收入国家中，全球75％的痴呆症病例未被诊断为90％。众所周知，当前的诊断方法是复杂的，涉及对医学笔记，大量认知测试，昂贵的脑部扫描或脊柱液体测试的手动审查。与CI相关的信息经常在电子健康记录（EHR）中找到，并且可以为早期诊断提供重要线索，但是专家的手动审查是繁琐的，并且容易发生。该项目开发了一种新型的最新自动筛选管道，用于可扩展和高速发现EHR中的CI。为了了解EHR中复杂语言结构的语言环境，构建了一个8,656个序列的数据库，以训练基于注意力的深度学习自然语言处理模型以对序列进行分类。使用序列级别分类器开发了基于逻辑回归的患者级别预测模型。深度学习系统的精度达到了93％，AUC = 0.98，以识别其EHR中没有较早诊断，与痴呆有关的诊断代码或与痴呆有关的药物的患者。否则，这些患者将未被发现或检测到太晚。 EHR筛选管道已部署在Neurahealthnlp中，这是一种用于自动化和实时CI筛选的Web应用程序，只需将EHR上传到浏览器中即可。 Neurahealthnlp更便宜，更快，更容易获得，并且胜过当前的临床方法，包括基于文本的分析和机器学习方法。它使得早期诊断可在稀缺的医疗服务中可行，但可访问的互联网或蜂窝服务。
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痴呆症是一种神经退行性疾病，导致认知下降，并影响全世界超过5000万人。痴呆症是由医疗保健专业人士诊断的 - 只有患有痴呆症的四个人中只有一名诊断出来。即使制造诊断，也可能无法作为患者图表中的疾病（ICD）诊断码的结构化国际分类。与认知障碍（CI）有关的信息通常在电子健康记录（EHR）中发现，但专家临床医生票据的手工审查既耗时，往往容易出错。本票据的自动化挖掘为在EHR数据中标记有认知障碍患者的机会。我们开发了自然语言处理（NLP）工具，以识别具有认知障碍的患者，并证明语言背景提高了认知障碍分类任务的性能。我们微调我们的注意力深入学习模型，可以从复杂的语言结构中学习，并且相对于基线NLP模型的精度（0.93）大大提高（0.84）。此外，我们表明深度学习NLP可以成功识别没有痴呆相关的ICD代码或药物的痴呆症患者。
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While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.
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Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.
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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.
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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.
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KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.
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Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.
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