我们使用来自多种传感模式的数据，即加速度计和全球导航卫星系统（GNSS）来对动物行为进行分类。我们从GNSS数据中提取三个新功能，即距水点，中值和中位数估计的水平位置误差的距离。我们考虑了将加速度计和GNSS数据可用信息组合的两种方法。第一种方法是基于从传感器数据中提取的特征并将串联特征向量馈入多层感知器（MLP）分类器中的串联。第二种方法是基于将两个MLP分类器预测的后验概率融合，每个概率每个都以从一个传感器的数据为输入中提取的功能。我们使用两个通过智能牛领和耳号收集的现实世界数据集评估了开发的多模式动物行为行为分类算法的性能。一对一的动物交叉验证结果表明，这两种方法都可以显着改善分类性能，而仅使用一种传感模式的数据，特别是对于步行和饮酒的不经常但重要的行为。基于两种方法开发的算法都需要相当小的计算和内存资源，因此适合于我们的衣领和耳罩的嵌入式系统实现。但是，基于后验概率融合的多模式动物行为分类算法比基于特征串联的算法更可取，因为它提供了更好的分类精度，具有较低的计算和记忆复杂性，对传感器数据失败更强大，并且享受更好的模块化。 。

我们通过各种经常性神经网络（RNN）模型来研究使用加速度数据的动物行为的分类。我们评估所考虑模型的分类性能和复杂性，该模型具有长短短时间内存储器（LSTM）或具有不同深度和宽度的GET的经常性单元（GRU）架构，使用来自牛或耳朵标签获取的四个数据集。我们还包括两种最先进的卷积神经网络（CNN）基本的时间级分类模型在评估中。结果表明，与基于CNN的模型相比，基于RNN的模型可以实现相似或更高的分类精度，同时具有较少的计算和内存要求。我们还观察到，尽管不太复杂，但是Gru架构的模型通常以LSTM架构的架构优于架构。具有64个隐藏单元的单层单向GRU模型似乎在准确性和复杂性之间提供了良好的平衡，使其适合在边缘/嵌入式设备上实现。

我们开发了一种基于端到端的深神经网络基于基于网络的基于网络行为，用于使用安装在可穿戴式衣领标签中的嵌入式系统的嵌入式系统上的加速度数据进行分类动物行为。该算法共同执行利用一组无限脉冲响应（IIR）和有限脉冲响应（FIR）滤波器与多层的感知响应（FIR）滤波器共同执行特征提取和分类。使用的IIR和FIR滤波器可以分别被视为特定类型的复发和卷积神经网络层。我们通过从放牧牛收集的两个现实世界数据集评估所提出的算法的性能。结果表明，该算法提供了良好的数据集和数据集良好的分类准确性，并且优于其最接近的竞争者，包括基于两个最先进的卷积神经网络的时间序列分类算法，这些分类算法显着更复杂。我们在套领标签的AIOT设备的嵌入式系统上实施了所提出的算法，以便对动物行为的原位分类。我们从加速度数据中实现了实时的原位行为，而不会对嵌入式系统的可用计算，内存或能量资源产生任何应变。

我们探索了知识蒸馏（KD）的使用来学习紧凑和准确的模型，这些模型可以从可穿戴设备上的加速度计算数据中分类动物行为。为此，我们采用了一个深厚而复杂的卷积神经网络，称为残留神经网络（RESNET）作为教师模型。 RESNET专为多元时间序列分类而设计。我们使用Resnet将动物行为分类数据集的知识歪曲到软标签中，其中由每个数据点的每个类别的伪概率组成。然后，我们使用软标签来训练我们的复杂学生模型，这些模型基于门控复发单元（GRU）和多层感知器（MLP）。使用两个现实世界动物行为分类数据集的评估结果表明，学生GRU-MLP模型的分类准确性通过KD明显改善，接近教师Resnet模型的分类精度。为了进一步减少使用KD训练的学生模型执行推理的计算和记忆要求，我们通过适当修改模型的计算图来利用动态定量量化。我们在我们专门构建的衣领的嵌入式系统和耳牌设备的嵌入式系统上实施了未量化和量化的版本，以实时和实时对动物行为进行分类。结果证实了KD和量化在分类准确性以及计算和记忆效率方面提高推理性能的有效性。

由于其宽度趋于无穷大，如果梯度下降下的深度神经网络的行为可以简化和可预测（例如，如果神经切线核（NTK）给出，则如果适当地进行了参数化（例如，NTK参数化）。但是，我们表明，神经网络的标准和NTK参数化不接受可以学习特征的无限宽度限制，这对于训练和转移学习至关重要。我们对标准参数化提出了简单的修改，以允许在极限内进行特征学习。使用 * Tensor程序 *技术，我们为此类限制提供了明确的公式。在Word2Vec和Omniglot上通过MAML进行的几杆学习，这是两个依赖特征学习的规范任务，我们准确地计算了这些限制。我们发现它们的表现都优于NTK基准和有限宽度网络，后者接近无限宽度的特征学习表现，随着宽度的增加。更普遍地，我们对神经网络参数化的自然空间进行分类，该空间概括了标准，NTK和平均场参数化。我们显示1）该空间中的任何参数化都可以接受特征学习或具有内核梯度下降给出的无限宽度训练动力学，但并非两者兼而有之； 2）可以使用Tensor程序技术计算任何此类无限宽度限制。可以在github.com/edwardjhu/tp4上找到我们的实验代码。

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.

In the past years, deep learning has seen an increase of usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their own uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole-Slide-Images under domain shift using the H\&E stained Camelyon17 breast cancer dataset. Although it is known that histopathological data can be subject to strong domain shift and label noise, to our knowledge this is the first work that compares the most common methods for uncertainty estimation under these aspects. In our experiments, we compare Stochastic Variational Inference, Monte-Carlo Dropout, Deep Ensembles, Test-Time Data Augmentation as well as combinations thereof. We observe that ensembles of methods generally lead to higher accuracies and better calibration and that Test-Time Data Augmentation can be a promising alternative when choosing an appropriate set of augmentations. Across methods, a rejection of the most uncertain tiles leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. We observe that the border regions of the Camelyon17 dataset are subject to label noise and evaluate the robustness of the included methods against different noise levels. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.

In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in large-scale, multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? In this paper, we investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our main technical contribution is to show that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. We then extend our results significantly to nonlinear stochastic approximation algorithms and multi-agent settings. In particular, we prove that for multi-agent TD learning, one can achieve linear convergence speedups in the number of agents while communicating just $\tilde{O}(1)$ bits per agent at each time step. Our work is the first to provide finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our analysis hinges on studying the drift of a novel Lyapunov function that captures the dynamics of a memory variable introduced by error feedback.

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.

Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-response assessments. The purpose of this study is to describe and evaluate three active learning methods than can be used to minimize the number of essays that must be scored by human raters while still providing the data needed to train a modern automated essay scoring system. The three active learning methods are the uncertainty-based, the topological-based, and the hybrid method. These three methods were used to select essays included as part of the Automated Student Assessment Prize competition that were then classified using a scoring model that was training with the bidirectional encoder representations from transformer language model. All three active learning methods produced strong results, with the topological-based method producing the most efficient classification. Growth rate accuracy was also evaluated. The active learning methods produced different levels of efficiency under different sample size allocations but, overall, all three methods were highly efficient and produced classifications that were similar to one another.