大脑磁共振成像（MRI）扫描的自动分割和体积对于诊断帕金森氏病（PD）和帕金森氏症综合症（P-Plus）至关重要。为了提高诊断性能，我们在大脑分割中采用了深度学习（DL）模型，并将其性能与金标准的非DL方法进行了比较。我们收集了健康对照组（n = 105）和PD患者（n = 105），多个全身性萎缩（n = 132）和渐进性超核麻痹（n = 69）的大脑MRI扫描。 2020.使用金标准的非DL模型FreeSurfer（FS），我们对六个脑结构进行了分割：中脑，PON，CAUDATE，CAUDATE，PUTATATE，pALLIDUM和THIRD CNTRICLE，并将其视为DL模型的注释数据，代表性V -net和unet。计算了分化正常，PD和P-Plus病例的曲线下的骰子分数和面积。每位患者六个大脑结构的V-NET和UNETR的分割时间分别为3.48 +-0.17和48.14 +-0.97 s，比FS（15,735 +-1.07 s）快至少300倍。两种DL模型的骰子得分都足够高（> 0.85），它们的疾病分类AUC优于FS。为了分类正常与P-Plus和PD与多个全身性萎缩（小脑型）的分类，DL模型和FS显示出高于0.8的AUC。 DL显着减少了分析时间，而不会损害大脑分割和差异诊断的性能。我们的发现可能有助于在临床环境中采用DL脑MRI分割并提高大脑研究。

图像分类器通常过于依赖于与目标类（即数据集偏差）在预测时具有很强相关性的外围属性。最近，无数的研究着重于缓解此类数据集偏见，其任务被称为偏见。但是，这些偏见方法通常具有不一致的实验设置（例如数据集和神经网络体系结构）。此外，大多数先前关于辩护方面的研究都没有指定它们如何选择涉及早期停止和超参数调整的模型参数。本文的目的是标准化不一致的实验设置，并提出一个用于脱缩的一致模型参数选择标准。基于这种统一的实验设置和模型参数选择标准，我们构建了一个名为DebiasBench的基准测试，其中包括五个数据集和七个Debiasing方法。我们仔细地在各个方面进行了广泛的实验，并表明不同的最新方法分别在不同的数据集中最有效。即使，没有任何依据模块的方法，也显示出低偏置严重程度的数据集中的竞争结果。我们公开释放DebiasBench中现有的辩论方法的实施，以鼓励未来的研究人员进行辩护，以进行公平的比较并进一步推动最先进的表现。

在图像分类中，“ debiasing”旨在训练分类器，以免对数据集偏差，数据样本的外围属性与目标类别之间的强相关性。例如，即使数据集中的青蛙类主要由具有沼泽背景的青蛙图像组成（即，偏见与一致的样本），也应该能够在海滩上正确地对青蛙进行正确分类（即，偏见的样品， ）。最近的辩论方法通常使用两个组件进行偏见，一个有偏见的模型$ f_b $和一个模型$ f_d $。 $ f_b $经过培训，可以专注于偏见的样本（即过度适合偏见），而$ f_d $主要通过专注于$ f_b $未能学习的样品，主要接受了偏见的样本培训，导致$ f_d $。不太容易受到数据集偏差的影响。虽然最先进的偏见技术旨在更好地培训$ f_d $，但我们专注于培训$ f_b $，这是迄今为止被忽视的组件。我们的实证分析表明，从$ f_b $的培训设置中删除偏见的样本对于改善$ f_d $的偏见性能很重要。这是由于以下事实：偏置冲突样品会干扰$ f_b $的偏见，因为这些样本不包括偏差属性。为此，我们提出了一种简单而有效的数据样本选择方法，该方法可以删除偏置冲突的样本，以构建一个偏置放大数据集用于培训$ f_b $。我们的数据示例选择方法可以直接应用于现有的基于重新加权的偏差方法，从而获得一致的性能提升并实现合成和现实世界数据集的最新性能。

随着3D扫描技术的发展，3D视觉任务已成为一个流行的研究区域。由于由传感器获得的大量数据，无监督的学习对于理解和利用点云而没有昂贵的注释过程至关重要。在本文中，我们提出了一种新颖的框架和一个名为“PSG-Net”的有效自动编码器架构，用于重建基于点云的学习。与使用固定或随机2D点使用的现有研究不同，我们的框架为潜在集合生成输入依赖的点亮功能。 PSG-Net使用编码输入来通过种子生成模块产生点明智的特征，并通过逐渐应用种子特征传播模块逐渐增加分辨率的多个阶段中提取更丰富的特征。我们通过实验证明PSG-Net的有效性; PSG-Net显示了点云重建和无监督分类的最先进的性能，并在监督完成中实现了对应于对应方法的可比性。

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.