In artificial intelligence, a significant focus has been on developing models that simultaneously process and interpret multiple forms of data. These multimodal models are designed to analyze and synthesize information from various sources, such as text, images, and audio, mimicking human sensory and cognitive processes. The main challenge in this field is developing systems that… →

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Alongside topological structure, nodes often possess textual features providing context. Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. With… →

Web automation technologies are vital in streamlining complex tasks that traditionally require human intervention. These technologies automate actions within web-based platforms, enhancing efficiency and scalability across various digital operations. Traditionally, web automation relies heavily on scripts or software, known as wrappers, to extract data from websites. While effective in consistent, unchanging environments, this method struggles… →

Have you ever wondered how we can determine the true impact of a particular intervention or treatment on certain outcomes? This is a crucial question in fields like medicine, economics, and social sciences, where understanding cause-and-effect relationships is essential. Researchers have been grappling with this challenge, known as the “Fundamental Problem of Causal Inference,” –… →