Existing artificial intelligence (AI) models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability, despite achieving medical-expert-like performance. This opacity makes them challenging to trust in clinical practice. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction by revealing how the prediction is derived, thus promoting the use of AI systems in healthcare. This paper presents the first survey of XAI techniques used for knee OA diagnosis. The XAI techniques are discussed from two perspectives: data interpretability and model interpretability. The aim of this paper is to provide valuable insights into XAI's potential towards a more reliable knee OA diagnosis approach and encourage its adoption in clinical practice.

The growing popularity of Vision Transformers as the go-to models for image classification has led to an explosion of architectural modifications claiming to be more efficient than the original ViT. However, a wide diversity of experimental conditions prevents a fair comparison between all of them, based solely on their reported results. To address this gap in comparability, we conduct a comprehensive analysis of more than 30 models to evaluate the efficiency of vision transformers and related architectures, considering various performance metrics. Our benchmark provides a comparable baseline across the landscape of efficiency-oriented transformers, unveiling a plethora of surprising insights. For example, we discover that ViT is still Pareto optimal across multiple efficiency metrics, despite the existence of several alternative approaches claiming to be more efficient. Results also indicate that hybrid attention-CNN models fare particularly well when it comes to low inference memory and number of parameters, and also that it is better to scale the model size, than the image size. Furthermore, we uncover a strong positive correlation between the number of FLOPS and the training memory, which enables the estimation of required VRAM from theoretical measurements alone. Thanks to our holistic evaluation, this study offers valuable insights for practitioners and researchers, facilitating informed decisions when selecting models for specific applications. We publicly release our code and data at https://github.com/tobna/WhatTransformerToFavor

This paper surveys the current state of the art in document automation (DA). The objective of DA is to reduce the manual effort during the generation of documents by automatically creating and integrating input from different sources and assembling documents conforming to defined templates. There have been reviews of commercial solutions of DA, particularly in the legal domain, but to date there has been no comprehensive review of the academic research on DA architectures and technologies. The current survey of DA reviews the academic literature and provides a clearer definition and characterization of DA and its features, identifies state-of-the-art DA architectures and technologies in academic research, and provides ideas that can lead to new research opportunities within the DA field in light of recent advances in generative AI and large language models.

In the context of Meituan Waimai, user behavior exhibits heterogeneous characteristics, including various behavior subjects, content, scenarios. The current industry approach mostly involves continuously adding various heterogeneous behavior to the traditional recommendation models, which brings two obvious problems. Firstly, the multitude of behavior subjects leads to sparse features that pose challenges to efficient modeling. Secondly, separating the modeling of user, merchant, and commodity behavior ignores the fusion of heterogeneous knowledge among behavior. However, we have noticed that heterogeneous user behavior contain rich semantic knowledge, and using semantics to represent and reason about user behavior can more effectively promote heterogeneous knowledge fusion and capture user interests. LLMs have shown remarkable capabilities in various fields, thanks to rich semantic knowledge and powerful inferential reasoning [1, 10]. We have designed a new user behavior modeling framework via LLM, which extracts and integrates heterogeneous knowledge from heterogeneous behavior information of users, and transforms structured user behavior into unstructured heterogeneous knowledge. In the field of recommendation, there have been some attempts to use LLM for personalized recommendation.

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.

Many natural systems as diverse as social networks (Knoke and Yang, 2019) and proteins (Jha et al., 2022) are characterized by relational structure. This is the structure of interactions between components in the system, such as social interactions between individuals or electrostatic interactions between atoms. In Geometric Deep Learning (Bronstein et al., 2021), Graph Neural Networks (GNNs) (Zhou et al., 2020) have demonstrated remarkable achievements in processing relational data using graphs--mathematical objects commonly used to encode pairwise relations. However, the pairwise structure of graphs is limiting. Social interactions can involve more than two individuals, and electrostatic interactions more than two atoms. Topological Deep Learning (TDL) (Hajij et al., 2023; Bodnar, 2022) leverages more general abstractions to process data with higher-order relational structure. The theoretical guarantees (Bodnar et al., 2021a,b; Huang and Yang, 2021) of its models, Topological Neural Networks (TNNs), lead to state-of-the-art performance on many machine learning tasks (Dong et al., 2020; Hajij et al., 2022a; Barbarossa and Sardellitti, 2020; Chen et al., 2022)--and reveal high potential for the applied sciences and beyond. However, the abstraction and fragmentation of mathematical notation across the TDL literature significantly limits the field's accessibility, while complicating model comparison and obscuring opportunities for innovation. To address this, we present an intuitive and systematic comparison of published TNN architectures.

Panoptic segmentation methods assign a known class to each pixel given in input. Even for state-of-the-art approaches, this inevitably enforces decisions that systematically lead to wrong predictions for objects outside the training categories. However, robustness against out-of-distribution samples and corner cases is crucial in safety-critical settings to avoid dangerous consequences. Since real-world datasets cannot contain enough data points to adequately sample the long tail of the underlying distribution, models must be able to deal with unseen and unknown scenarios as well. Previous methods targeted this by re-identifying already-seen unlabeled objects. In this work, we propose the necessary step to extend segmentation with a new setting which we term holistic segmentation. Holistic segmentation aims to identify and separate objects of unseen, unknown categories into instances without any prior knowledge about them while performing panoptic segmentation of known classes. We tackle this new problem with U3HS, which finds unknowns as highly uncertain regions and clusters their corresponding instance-aware embeddings into individual objects. By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios. Extensive experiments on public data from MS COCO, Cityscapes, and Lost&Found demonstrate the effectiveness of U3HS for this new, challenging, and assumptions-free setting called holistic segmentation. Project page: https://holisticseg.github.io.

The last decade of machine learning has seen drastic increases in scale and capabilities. Deep neural networks (DNNs) are increasingly being deployed in the real world. However, they are difficult to analyze, raising concerns about using them without a rigorous understanding of how they function. Effective tools for interpreting them will be important for building more trustworthy AI by helping to identify problems, fix bugs, and improve basic understanding. In particular, "inner" interpretability techniques, which focus on explaining the internal components of DNNs, are well-suited for developing a mechanistic understanding, guiding manual modifications, and reverse engineering solutions. Much recent work has focused on DNN interpretability, and rapid progress has thus far made a thorough systematization of methods difficult. In this survey, we review over 300 works with a focus on inner interpretability tools. We introduce a taxonomy that classifies methods by what part of the network they help to explain (weights, neurons, subnetworks, or latent representations) and whether they are implemented during (intrinsic) or after (post hoc) training. To our knowledge, we are also the first to survey a number of connections between interpretability research and work in adversarial robustness, continual learning, modularity, network compression, and studying the human visual system. We discuss key challenges and argue that the status quo in interpretability research is largely unproductive. Finally, we highlight the importance of future work that emphasizes diagnostics, debugging, adversaries, and benchmarking in order to make interpretability tools more useful to engineers in practical applications.

With the emergence of generative artificial intelligence, an increasing number of individuals and organizations have begun exploring its potential to enhance productivity and improve product quality across various sectors. The field of education is no exception. However, it is vital to notice that artificial intelligence adoption in education dates back to the 1960s. In light of this historical context, this white paper serves as the inaugural piece in a four-part series that elucidates the role of AI in education. The series delves into topics such as its potential, successful applications, limitations, ethical considerations, and future trends. This initial article provides a comprehensive overview of the field, highlighting the recent developments within the generative artificial intelligence sphere.

Web 3.0 is the new generation of the Internet that is reconstructed with distributed technology, which focuses on data ownership and value expression. Also, it operates under the principle that data and digital assets should be owned and controlled by users rather than large corporations. In this survey, we explore the current development state of Web 3.0 and the application of AI Technology in Web 3.0. Through investigating the existing applications and components of Web 3.0, we propose an architectural framework for Web 3.0 from the perspective of ecological application scenarios. We outline and divide the ecology of Web 3.0 into four layers. The main functions of each layer are data management, value circulation, ecological governance, and application scenarios. Our investigation delves into the major challenges and issues present in each of these layers. In this context, AI has shown its strong potential to solve existing problems of Web 3.0. We illustrate the crucial role of AI in the foundation and growth of Web 3.0. We begin by providing an overview of AI, including machine learning algorithms and deep learning techniques. Then, we thoroughly analyze the current state of AI technology applications in the four layers of Web 3.0 and offer some insights into its potential future development direction.