Machine learning

Kolmogorov-Arnold Networks (KANs) are a recent development that offer an alternative to Multi-Layer Perceptrons (MLPs) in machine learning. Using the Kolmogorov-Arnold representation theorem, KANs use neurons that carry out simple addition operations. Nonetheless, current models of KANs can pose challenges in real-world application, prompting researchers to explore other multivariate functions that could boost its use…

Multi-layer perceptrons (MLPs) are integral to modern deep learning models for their versatility in replicating nonlinear functions across various tasks. However, interpretation and scalability challenges and reliance on fixed activation functions have raised concerns about their adaptability and scalability. Researchers have explored alternative architectures to overcome these issues, such as Kolmogov-Arnold Networks (KANs). KANs have…

Multimodal AI models, which integrate diverse data types like text and images, are pivotal for tasks such as answering visual questions and generating descriptive text for images. However, optimizing model efficiency remains a significant challenge. Traditional methods, which fuse modality-specific encoders or decoders, often limit the model's ability to combine information across different data types…