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.

This paper tackles the challenging problem of automating code updates to fix deprecated API usages of open source libraries by analyzing their release notes. Our system employs a three-tier architecture: first, a web crawler service retrieves deprecation documentation from the web; then a specially built parser processes those text documents into tree-structured representations; finally, a client IDE plugin locates and fixes identified deprecated usages of libraries in a given codebase. The focus of this paper in particular is the parsing component. We introduce a novel transition-based parser in two variants: based on a classical feature engineered classifier and a neural tree encoder. To confirm the effectiveness of our method, we gathered and labeled a set of 426 API deprecations from 7 well-known Python data science libraries, and demonstrated our approach decisively outperforms a non-trivial neural machine translation baseline.

Generalisation to unseen contexts remains a challenge for embodied navigation agents. In the context of semantic audio-visual navigation (SAVi) tasks, the notion of generalisation should include both generalising to unseen indoor visual scenes as well as generalising to unheard sounding objects. However, previous SAVi task definitions do not include evaluation conditions on truly novel sounding objects, resorting instead to evaluating agents on unheard sound clips of known objects; meanwhile, previous SAVi methods do not include explicit mechanisms for incorporating domain knowledge about object and region semantics. These weaknesses limit the development and assessment of models' abilities to generalise their learned experience. In this work, we introduce the use of knowledge-driven scene priors in the semantic audio-visual embodied navigation task: we combine semantic information from our novel knowledge graph that encodes object-region relations, spatial knowledge from dual Graph Encoder Networks, and background knowledge from a series of pre-training tasks -- all within a reinforcement learning framework for audio-visual navigation. We also define a new audio-visual navigation sub-task, where agents are evaluated on novel sounding objects, as opposed to unheard clips of known objects. We show improvements over strong baselines in generalisation to unseen regions and novel sounding objects, within the Habitat-Matterport3D simulation environment, under the SoundSpaces task.

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.

Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups involve multiple related networks and, moreover, it is often the case that only a subset of nodes is observed while the rest remain hidden. Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables. Intuitively, the presence of the hidden nodes renders the inference task ill-posed and challenging to solve, so we overcome this detrimental influence by harnessing the similarity of the estimated graphs. To that end, we assume that the observed signals are drawn from a Gaussian Markov random field with latent variables and we carefully model the graph similarity among hidden (latent) nodes. Then, we exploit the structure resulting from the previous considerations to propose a convex optimization problem that solves the joint graph learning task by providing a regularized maximum likelihood estimator. Finally, we compare the proposed algorithm with different baselines and evaluate its performance over synthetic and real-world graphs.

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.

我们考虑了从节点观测值估算多个网络拓扑的问题，其中假定这些网络是从相同（未知）随机图模型中绘制的。我们采用图形作为我们的随机图模型，这是一个非参数模型，可以从中绘制出潜在不同大小的图形。图形子的多功能性使我们能够解决关节推理问题，即使对于要恢复的图形包含不同数量的节点并且缺乏整个图形的精确比对的情况。我们的解决方案是基于将最大似然惩罚与Graphon估计方案结合在一起，可用于增强现有网络推理方法。通过引入嘈杂图抽样信息的强大方法，进一步增强了所提出的联合网络和图形估计。我们通过将其性能与合成和实际数据集中的竞争方法进行比较来验证我们提出的方法。

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。

随着跨领域的机器人在共享环境中开始与人类合作，使他们能够推理人类意图的算法对于实现安全的相互作用很重要。在我们的工作中，我们通过预测动态环境中的轨迹的问题来研究人类的意图。我们探索导航准则相对严格定义但在其物理环境中没有明确标记的域。我们假设在这些领域内，代理人倾向于表现出短期运动模式，这些模式揭示了与代理人的一般方向，中间目标和运动规则相关的上下文信息，例如社会行为。从这种直觉中，我们提出了社交模式，这是一种复发，多模式轨迹预测的算法，该预测利用运动模式来编码上述上下文。我们的方法通过学习预测短期运动模式来指导长期的轨迹预测。然后，它从模式中提取次目标信息，并将其汇总为社会环境。我们评估了跨三个领域的方法：人类人群，体育中的人类和码头领空中的载人飞机，以实现最先进的表现。