Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph structure, facilitating node classification and link prediction tasks. Effective graph pooling is essential for downsizing and learning representations, categorized into global and hierarchical pooling. Hierarchical methods, such as TopK-based and… →

The recent Yi-1.5-34B model introduced by 01.AI has brought about yet another advancement in the field of Artificial Intelligence. Positioned as a major improvement over its predecessors, this unique model bridges the gap between Llama 3 8B and 70B. It promises better performance in a number of areas, such as multimodal capability, code production, and… →

The artificial intelligence and machine learning world has witnessed rapid advancements, with OpenAI at the forefront of these innovations. One of the latest developments is the release of GPT-4o, an optimized version of the highly acclaimed GPT-4. Let’s delve into the key updates in GPT-4o, compare it with its predecessor, and present a comparative table… →

Machine Learning (ML) is everywhere these days, playing a crucial role in countless fields worldwide. Its applications are endless, and we rely on it more than ever. As ML models become more complex, it becomes more challenging to understand and interpret them. Understanding complex machine learning models, especially those with many layers and intricate connections,… →

Data mapping is a critical process in data management, enabling the integration and transformation of data from various sources into a unified format. The concept of data mapping as a search problem provides a unique perspective on efficiently and effectively discovering mappings between data sources. Let’s explore the foundational concepts, challenges, methodologies, and future directions… →

As Artificial Intelligence (AI) systems advance, a fascinating trend has emerged: their representations of data across different architectures, training objectives, and even modalities seem to be converging. Researchers have put forth, as shown in Figure 1, a thought-provoking hypothesis to explain this phenomenon called the “Platonic Representation Hypothesis.” At its core, this hypothesis posits that… →

Although recent multimodal foundation models are extensively utilized, they tend to segregate various modalities, typically employing specific encoders or decoders for each. This approach constrains their capacity to fuse information across modalities effectively and produce multimodal documents comprising diverse sequences of images and text. Consequently, there’s a limitation in their ability to seamlessly integrate different… →