由于语音分离的表现非常适合两个说话者完全重叠的语音，因此研究的注意力已转移到处理更现实的场景。然而，由于因素，例如说话者，内容，渠道和环境等因素引起的训练/测试情况之间的领域不匹配仍然是言语分离的严重问题。演讲者和环境不匹配已在现有文献中进行了研究。然而，关于语音内容和渠道不匹配的研究很少。此外，这些研究中语言和渠道的影响大多是纠结的。在这项研究中，我们为各种实验创建了几个数据集。结果表明，与不同渠道的影响相比，不同语言的影响足以忽略。在我们的实验中，Android手机记录的数据培训可提供最佳的概括性。此外，我们通过评估投影提供了一种新的解决方案，以测量通道相似性并用于有效选择其他训练数据以提高野外测试数据的性能。

通道不匹配和噪声干扰的补偿对于强大的自动语音识别至关重要。增强的语音已引入声学模型的多条件训练中，以提高其概括能力。在本文中，提出了一个基于两个级联神经结构的噪音感知训练框架，以共同优化语音增强和语音识别。功能增强模块由多任务自动编码器组成，嘈杂的语音被分解为干净的语音和噪声。通过将其增强的，吸引噪音的和嘈杂的特征连接起来，通过优化预测的无晶格最大互信息和预测状态序列之间的无晶格最大互助和交叉熵，声音模块将每个特征型仪表型映射到Triphone状态。除了分解时间延迟神经网络（TDNN-F）及其卷积变体（CNN-TDNNF），均具有Specaug，两个提议的系统的单词错误率（WER）分别为3.90％和3.55％ Aurora-4任务。与使用BigRAM和Trigram语言模型进行解码的最佳现有系统相比，拟议的基于CNN-TDNNF的系统的相对降低分别为15.20％和33.53％。此外，提出的基于CNN-TDNNF的系统还优于AMI任务上的基线CNN-TDNNF系统。

在我们以前的工作中，我们提出了一个歧视性自动编码器（DCAE）进行语音识别。 DCAE将两个训练方案结合在一起。首先，由于DCAE的目标是学习编码器映射，因此重建语音和输入语音之间的平方误差被最小化。其次，在代码层中，基于框架的语音嵌入是通过最小化地面真相标签和预测的Triphone-State分数之间的分类跨熵来获得的。 DCAE是根据Kaldi工具包开发的，通过将各种TDNN模型视为编码器。在本文中，我们进一步提出了三个新版本的DCAE。首先，使用了一个新的目标函数，该函数使用了地面真相和预测的Triphone-State序列之间的分类跨膜和相互信息。所得的DCAE称为基于链的DCAE（C-DCAE）。为了应用于强大的语音识别，我们将C-DCAE进一步扩展到层次结构和平行结构，从而导致HC-DCAE和PC-DCAE。在这两个模型中，重建的嘈杂语音与输入嘈杂语音以及增强语音和参考清洁语音之间的误差之间的误差都归功于目标函数。 WSJ和Aurora-4 Corpora的实验结果表明，我们的DCAE模型优于基线系统。

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.

We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.