It is designed to help students and researchers to quickly familiarize themselves with the area and to provide a foundation for the development of university courses on the mathematics of deep learning. Our main goal in the composition of this book was to present various rigorous, but easy to grasp, results that help to build an understanding of fundamental mathematical concepts in deep learning. To achieve this, we prioritize simplicity over generality. As a mathematical introduction to deep learning, this book does not aim to give an exhaustive survey of the entire (and rapidly growing) field, and some important research directions are missing. In particular, we have favored mathematical results over empirical research, even though an accurate account of the theory of deep learning requires both.

Deep learning model design, development, and debugging is a process driven by best practices, guidelines, trial-and-error, and the personal experiences of model developers. At multiple stages of this process, performance and internal model data can be logged and made available. However, due to the sheer complexity and scale of this data and process, model developers often resort to evaluating their model performance based on abstract metrics like accuracy and loss. We argue that a structured analysis of data along the model's architecture and at multiple abstraction levels can considerably streamline the debugging process. Such a systematic analysis can further connect the developer's design choices to their impacts on the model behavior, facilitating the understanding, diagnosis, and refinement of deep learning models. Hence, in this paper, we (1) contribute a conceptual framework structuring the data space of deep learning experiments. Our framework, grounded in literature analysis and requirements interviews, captures design dimensions and proposes mechanisms to make this data explorable and tractable. To operationalize our framework in a ready-to-use application, we (2) present the iNNspector system. iNNspector enables tracking of deep learning experiments and provides interactive visualizations of the data on all levels of abstraction from multiple models to individual neurons. Finally, we (3) evaluate our approach with three real-world use-cases and a user study with deep learning developers and data analysts, proving its effectiveness and usability.

Rapid development of disease detection models using computer vision is crucial in responding to medical emergencies, such as epidemics or bioterrorism events. Traditional data collection methods are often too slow in these scenarios, requiring innovative approaches for quick, reliable model generation from minimal data. Our study introduces a novel approach by constructing a comprehensive computer vision model to detect Mpox lesions using only synthetic data. Initially, these models generated a diverse set of synthetic images representing Mpox lesions on various body parts (face, back, chest, leg, neck, arm) across different skin tones as defined by the Fitzpatrick scale (fair, brown, dark skin). Subsequently, we trained and tested a vision model with this synthetic dataset to evaluate the diffusion models' efficacy in producing high-quality training data and its impact on the vision model's medical image recognition performance. The results were promising; the vision model achieved a 97% accuracy rate, with 96% precision and recall for Mpox cases, and similarly high metrics for normal and other skin disorder cases, demonstrating its ability to correctly identify true positives and minimize false positives. The model achieved an F1-Score of 96% for Mpox cases and 98% for normal and other skin disorders, reflecting a balanced precision-recall relationship, thus ensuring reliability and robustness in its predictions. Our proposed SynthVision methodology indicates the potential to develop accurate computer vision models with minimal data input for future medical emergencies.

The integration of large language models (LLMs) with vision-language (VL) tasks has been a transformative development in the realm of artificial intelligence, highlighting the potential of LLMs as a versatile general-purpose chatbot. However, the current trend in this evolution focuses on the integration of vision and language to create models that can operate in more diverse and real-world contexts. We present a novel approach, termed Bottleneck Adapter, specifically crafted for enhancing the multimodal functionalities of these complex models, enabling joint optimization of the entire multimodal LLM framework through a process known as Multimodal Model Tuning (MMT). Our approach utilizes lightweight adapters to connect the image encoder and LLM without the need for large, complex neural networks. Unlike the conventional modular training schemes, our approach adopts an end-to-end optimization regime, which, when combined with the adapters, facilitates the joint optimization using a significantly smaller parameter set. Our method exhibits robust performance with 90.12\% accuracy, outperforming both human-level performance (88.4\%) and LaVIN-7B (89.41\%).

Off-policy evaluation (OPE) is widely applied in sectors such as pharmaceuticals and e-commerce to evaluate the efficacy of novel products or policies from offline datasets. This paper introduces a causal deepset framework that relaxes several key structural assumptions, primarily the mean-field assumption, prevalent in existing OPE methodologies that handle spatio-temporal interference. These traditional assumptions frequently prove inadequate in real-world settings, thereby restricting the capability of current OPE methods to effectively address complex interference effects. In response, we advocate for the implementation of the permutation invariance (PI) assumption. This innovative approach enables the data-driven, adaptive learning of the mean-field function, offering a more flexible estimation method beyond conventional averaging. Furthermore, we present novel algorithms that incorporate the PI assumption into OPE and thoroughly examine their theoretical foundations. Our numerical analyses demonstrate that this novel approach yields significantly more precise estimations than existing baseline algorithms, thereby substantially improving the practical applicability and effectiveness of OPE methodologies.

We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses bias and propaganda annotation in multilingual news posts. We focus on the early days of the Israel War on Gaza as a case study. The task aims to foster collaboration in developing annotation guidelines for subjective tasks by creating frameworks for analyzing diverse narratives highlighting potential bias and propaganda. In a spirit of fostering and encouraging diversity, we address the problem from a multilingual perspective, namely within five languages: English, French, Arabic, Hebrew, and Hindi. A total of 17 teams participated in two annotation subtasks: bias (16 teams) and propaganda (6 teams). The teams competed in four evaluation tracks: guidelines development, annotation quality, annotation quantity, and consistency. Collectively, the teams produced 129,800 data points. Key findings and implications for the field are discussed.

The idea of smart cities (SCs) has gained substantial attention in recent years. The SC paradigm aims to improve citizens' quality of life and protect the city's environment. As we enter the age of next-generation SCs, it is important to explore all relevant aspects of the SC paradigm. In recent years, the advancement of Information and Communication Technologies (ICT) has produced a trend of supporting daily objects with smartness, targeting to make human life easier and more comfortable. The paradigm of SCs appears as a response to the purpose of building the city of the future with advanced features. SCs still face many challenges in their implementation, but increasingly more studies regarding SCs are implemented. Nowadays, different cities are employing SC features to enhance services or the residents quality of life. This work provides readers with useful and important information about Amman Smart City.

Developing models that can learn to reason is a notoriously challenging problem. We focus on reasoning in relational domains, where the use of Graph Neural Networks (GNNs) seems like a natural choice. However, previous work on reasoning with GNNs has shown that such models tend to fail when presented with test examples that require longer inference chains than those seen during training. This suggests that GNNs lack the ability to generalize from training examples in a systematic way, which would fundamentally limit their reasoning abilities. A common solution is to instead rely on neuro-symbolic methods, which are capable of reasoning in a systematic way by design. Unfortunately, the scalability of such methods is often limited and they tend to rely on overly strong assumptions, e.g.\ that queries can be answered by inspecting a single relational path. In this paper, we revisit the idea of reasoning with GNNs, showing that systematic generalization is possible as long as the right inductive bias is provided. In particular, we argue that node embeddings should be treated as epistemic states and that GNN should be parameterised accordingly. We propose a simple GNN architecture which is based on this view and show that it is capable of achieving state-of-the-art results. We furthermore introduce a benchmark which requires models to aggregate evidence from multiple relational paths. We show that existing neuro-symbolic approaches fail on this benchmark, whereas our considered GNN model learns to reason accurately.

In recent years, variational quantum circuits (VQCs) have been widely explored to advance quantum circuits against classic models on various domains, such as quantum chemistry and quantum machine learning. Similar to classic machine-learning models, VQCs can be optimized through gradient-based approaches. However, the gradient variance of VQCs may dramatically vanish as the number of qubits or layers increases. This issue, a.k.a. Barren Plateaus (BPs), seriously hinders the scaling of VQCs on large datasets. To mitigate the exponential gradient vanishing, extensive efforts have been devoted to tackling this issue through diverse strategies. In this survey, we conduct a systematic literature review of recent works from both investigation and mitigation perspectives. Besides, we propose a new taxonomy to categorize most existing mitigation strategies. At last, we provide insightful discussion for future directions of BPs.

However, to this day, physical paper documents still play an important role in business operations, as they are a key means of communication related to transactions both within and between organizations [120]. The processing of such documents is an essential yet time-consuming task that offers a high potential for automation due to the high workload involved as well as the critical nature of information transfer between different information systems [19, 130]. At the same time, it can be observed that the ongoing digital transformation of business operations is leading to an increase in the digital processing of documents. This trend reinforces the need - but also the potential - for automated document processing, as more and more documents are available in digital form [113].