机器学习的最新进展表明，通过自我监督的学习获得的预训练表示形式可以通过小型培训数据实现高精度。与视觉和自然语言处理域不同，基于IMU的应用程序的预培训是具有挑战性的，因为只有少数公开可用的数据集具有足够的规模和多样性来学习可推广的表示。为了克服这个问题，我们提出了IMG2IMU，这是一种新颖的方法，可以适应从大规模图像到不同弹药的IMU感应任务的预训练表示。我们将传感器数据转换为可解释的频谱图，以便模型利用从视觉中获得的知识。此外，我们将对比度学习应用于我们旨在学习用于解释传感器数据的表示形式。我们对五个IMU感应任务的广泛评估表明，IMG2IMU始终优于基准，这说明视力知识可以纳入一些用于IMU感应任务的学习环境中。

测试时间适应（TTA）是一个新兴范式，可解决培训和测试阶段之间的分布变化，而无需其他数据采集或标签成本；仅使用未标记的测试数据流进行连续模型适应。以前的TTA方案假设测试样本是独立的，并且分布相同（i.i.d.），即使它们在应用程序方案中通常在时间上相关（non-i.i.d。），例如自动驾驶。我们发现，在这种情况下，大多数现有的TTA方法急剧失败。由此激励，我们提出了一种新的测试时间适应方案，该方案对非I.I.D具有强大的态度。测试数据流。我们的新颖性主要是两倍：（a）纠正分布样本的归一化的实例感知批归归量表（IABN），以及（b）模拟I.I.D.的预测均衡储层采样（PBRS）。来自非i.i.d的数据流。以班级平衡的方式流式传输。我们对各种数据集的评估，包括现实世界非i.i.d。流，表明所提出的强大TTA不仅优于非i.i.d的最先进的TTA算法。设置，但也可以实现与I.I.D.下的这些算法相当的性能。假设。

联合学习（FL）在分布式客户端上培训机器学习模型，而不会暴露单个数据。与通常基于仔细组织的数据的集中培训不同，FL处理通常不混阻和不平衡的设备数据。因此，处理所有数据的传统流行训练协议同样地导致浪费本地计算资源，并减慢全局学习过程。为此，我们提出了一个系统性的FLBalancer，它积极选择客户的培训样本。我们的示例选择策略在尊重客户端的隐私和计算能力的同时优先确定更多“信息性”数据。为了更好地利用样本选择来加速全球培训，我们进一步推出了一种自适应截止日期控制方案，该方案预测每个轮的最佳截止日期，具有不同的客户端列车数据。与具有截止日期配置方法的现有流算法相比，我们对三个不同域的五个数据集的评估表明，FedBalancer将时间至准确性的性能提高1.22〜4.62倍，同时提高模型精度1.0〜3.3％。我们还表明，通过展示在与三种不同的FL算法共同运行时，FedBalancer提高了收敛速度和准确性，可以随时适用于其他流动方法。

许多利用移动设备中的传感器的应用以及应用机器学习以提供新颖的服务。然而，诸如不同的用户，设备，环境和超参数之类的各种因素影响了这种应用的性能，从而使域移位（即，来自训练源数据集的目标用户的分发偏移）是一个重要问题。虽然最近的域适应技术试图解决这个问题，但各种因素之间的复杂相互作用通常会限制其有效性。我们认为，准确估算未训练的域中的性能可能会显着降低性能不确定性。我们呈现Dapper（域适配性能估计器），其估计目标域中的适应性能，只有未标记的目标数据。我们的直觉是目标数据上模型的输出提供了模型在目标域中的实际性能的线索。 Dapper不需要昂贵的标签成本，也不需要在部署后涉及额外的培训。与四个基线相比，我们与四个真实世界传感数据集进行了评估，表明，估计精度平均17％平均占据了基线的表现。此外，我们的On-Device实验表明，与基线相比，Dapper达到了多达216倍的计算开销。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, to assign multiple objects into semantic categories. Thus, we extend discrete diffusion models to learn scene-scale categorical distributions. In addition, we validate that a latent diffusion model can reduce computation costs for training and deploying. To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale. We further propose to perform semantic scene completion (SSC) by learning a conditional distribution using our diffusion model, where the condition is a partial observation in a sparse point cloud. In experiments, we empirically show that our diffusion models not only generate reasonable scenes, but also perform the scene completion task better than a discriminative model. Our code and models are available at https://github.com/zoomin-lee/scene-scale-diffusion

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.