从语音音频中删除背景噪音一直是大量研究和努力的主题，尤其是由于虚拟沟通和业余声音录制的兴起，近年来。然而，背景噪声并不是唯一可以防止可理解性的不愉快干扰：混响，剪裁，编解码器工件，有问题的均衡，有限的带宽或不一致的响度同样令人不安且无处不在。在这项工作中，我们建议将言语增强的任务视为一项整体努力，并提出了一种普遍的语音增强系统，同时解决了55种不同的扭曲。我们的方法由一种使用基于得分的扩散的生成模型以及一个多分辨率调节网络，该网络通过混合密度网络进行增强。我们表明，这种方法在专家听众执行的主观测试中大大优于艺术状态。我们还表明，尽管没有考虑任何特定的快速采样策略，但它仅通过4-8个扩散步骤就可以实现竞争性的目标得分。我们希望我们的方法论和技术贡献都鼓励研究人员和实践者采用普遍的语音增强方法，可能将其作为一项生成任务。

上采样器是由问题上采样层引起的，并且由于在上采样时出现的光谱副本。此外，根据所用的上采样层，这种伪像可以是色调的伪像（添加性高频噪声）或过滤伪像（衰减，衰减一些带）。在这项工作中，我们通过研究不同的伪像如何交互和评估模型性能的影响，调查在所产生的音频中具有上采样的伪影的实际意义。为此，我们基准为音乐源分离的大量上采样层：不同的转置和子像素卷积设置，不同的插值上升器（包括基于拉伸和SINC插值的两个新颖的层）和基于不同的基于小波的上升器（包括小说可学习小波层）。我们的研究结果表明，与插值上采样器相关的过滤器件是感知的，即使它们倾向于实现更差的客观分数。

Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential learning capabilities. In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning. A hard attention mask is learned concurrently to every task, through stochastic gradient descent, and previous masks are exploited to condition such learning. We show that the proposed mechanism is effective for reducing catastrophic forgetting, cutting current rates by 45 to 80%. We also show that it is robust to different hyperparameter choices, and that it offers a number of monitoring capabilities. The approach features the possibility to control both the stability and compactness of the learned knowledge, which we believe makes it also attractive for online learning or network compression applications.

Training large, deep neural networks to convergence can be prohibitively expensive. As a result, often only a small selection of popular, dense models are reused across different contexts and tasks. Increasingly, sparsely activated models, which seek to decouple model size from computation costs, are becoming an attractive alternative to dense models. Although more efficient in terms of quality and computation cost, sparse models remain data-hungry and costly to train from scratch in the large scale regime. In this work, we propose sparse upcycling -- a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense checkpoint. We show that sparsely upcycled T5 Base, Large, and XL language models and Vision Transformer Base and Large models, respectively, significantly outperform their dense counterparts on SuperGLUE and ImageNet, using only ~50% of the initial dense pretraining sunk cost. The upcycled models also outperform sparse models trained from scratch on 100% of the initial dense pretraining computation budget.

Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.

Generative Adversarial Networks (GANs) were introduced by Goodfellow in 2014, and since then have become popular for constructing generative artificial intelligence models. However, the drawbacks of such networks are numerous, like their longer training times, their sensitivity to hyperparameter tuning, several types of loss and optimization functions and other difficulties like mode collapse. Current applications of GANs include generating photo-realistic human faces, animals and objects. However, I wanted to explore the artistic ability of GANs in more detail, by using existing models and learning from them. This dissertation covers the basics of neural networks and works its way up to the particular aspects of GANs, together with experimentation and modification of existing available models, from least complex to most. The intention is to see if state of the art GANs (specifically StyleGAN2) can generate album art covers and if it is possible to tailor them by genre. This was attempted by first familiarizing myself with 3 existing GANs architectures, including the state of the art StyleGAN2. The StyleGAN2 code was used to train a model with a dataset containing 80K album cover images, then used to style images by picking curated images and mixing their styles.

Solar forecasting from ground-based sky images using deep learning models has shown great promise in reducing the uncertainty in solar power generation. One of the biggest challenges for training deep learning models is the availability of labeled datasets. With more and more sky image datasets open sourced in recent years, the development of accurate and reliable solar forecasting methods has seen a huge growth in potential. In this study, we explore three different training strategies for deep-learning-based solar forecasting models by leveraging three heterogeneous datasets collected around the world with drastically different climate patterns. Specifically, we compare the performance of models trained individually based on local datasets (local models) and models trained jointly based on the fusion of multiple datasets from different locations (global models), and we further examine the knowledge transfer from pre-trained solar forecasting models to a new dataset of interest (transfer learning models). The results suggest that the local models work well when deployed locally, but significant errors are observed for the scale of the prediction when applied offsite. The global model can adapt well to individual locations, while the possible increase in training efforts need to be taken into account. Pre-training models on a large and diversified source dataset and transferring to a local target dataset generally achieves superior performance over the other two training strategies. Transfer learning brings the most benefits when there are limited local data. With 80% less training data, it can achieve 1% improvement over the local baseline model trained using the entire dataset. Therefore, we call on the efforts from the solar forecasting community to contribute to a global dataset containing a massive amount of imagery and displaying diversified samples with a range of sky conditions.

有效的缩放和灵活的任务接口使大型语言模型能够在许多任务中表现出色。帕利（Pali）根据视觉和文本输入生成文本，并使用该界面以许多语言执行许多视觉，语言和多模式任务。为了训练帕利，我们利用了大型的编码器语言模型和视觉变压器（VITS）。这使我们能够利用其现有能力，并利用培训它们的大量成本。我们发现，视觉和语言组成部分的联合缩放很重要。由于现有的语言变压器比其视觉对应物要大得多，因此我们训练迄今为止最大的VIT（VIT-E），以量化甚至大容量视觉模型的好处。为了训练Pali，我们基于一个新的图像文本训练集，其中包含10B图像和文本，以100多种语言来创建大型的多语言组合。帕利（Pali）在多个视觉和语言任务（例如字幕，视觉问题，索方式，场景文本理解）中实现了最新的，同时保留了简单，模块化和可扩展的设计。

关于人际冲突的研究历史悠久，并包含许多有关冲突类型学的建议。我们将其用作新的注释方案的基础，并发布新的情况和冲突方面注释的新数据集。然后，我们构建一个分类器，以预测某人在给定情况下是否将一个人的行为视为对还是错，从而优于先前的此任务的工作。我们的分析包括冲突方面，但也产生了被人类验证的集群，并根据参与者与作者的关系显示冲突内容的差异。我们的发现对理解冲突和社会规范具有重要意义。

我们研究反对称函数的两个基本模型（或\ emph {ans \“ atze}），即表格$ f的函数$ f $（x _ {\ sigma（1）}，\ ldots，x _ {\ f $sigma（n）}）= \ text {sign}（\ sigma）f（x_1，\ ldots，x_n）$，其中$ \ sigma $是任何置换。这些都是在量子化学的背景下出现的，是基本的建模特定的费米子系统波函数的工具。具体来说，我们考虑了两个流行的反对称ANS \“ atze：Slater代表，它利用了决定因素的交替结构，以及Jastrow Ansatz，它们通过任意对称功能增强了用产品的Slater确定性。我们构建了一个可以更有效地以jastrow形式表达的反对称函数，但是除非有指数（以$ n^2 $为指数）许多术语，否则无法通过Slater决定因素近似。这代表了这两个Ans \“ atze之间的第一个显式定量分离。