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Found 8 papers, 1 papers with code
Data Scarcity in Recommendation Systems: A Survey
The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations.
Large Language Models in Law: A Survey
With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry.
Multimodal Large Language Models: A Survey
By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains.
Large Language Models in Education: Vision and Opportunities
By reviewing existing research, this article provides guidance and insights for educators, researchers, and policy-makers to gain a deep understanding of the potential and challenges of LLM4Edu.
Maximizing the performance for microcomb based microwave photonic transversal signal processors
Microwave photonic (MWP) transversal signal processors offer a compelling solution for realizing versatile high-speed information processing by combining the advantages of reconfigurable electrical digital signal processing and high-bandwidth photonic processing.
AI-Generated Content (AIGC): A Survey
To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged.
Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis
We found that models trained using our framework, as well as other regularization methods and adversarial training support our hypothesis of data sparsity and that models trained with these methods learn to have decision boundaries more similar to the aforementioned ideal decision boundary.
11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy.
