GNNs have excelled in analyzing structured data but face challenges with dynamic, temporal graphs. Traditional forecasting, often used in fields like economics and biology, relied on statistical models for time-series data. Deep learning, particularly GNNs, shifted focus to non-Euclidean data like social and biological networks. However, applying GNNs to dynamic graphs, where relationships constantly evolve,… →

Neural Architecture Search (NAS) has emerged as a powerful tool for automating the design of neural network architectures, providing a clear advantage over manual design methods. It significantly reduces the time and expert effort required in architecture development. However, traditional NAS faces significant challenges as it depends on extensive computational resources, particularly GPUs, to navigate… →

Microsoft addresses the complex challenges of integrating geospatial data into machine learning workflows. Working with such data is difficult due to its heterogeneity, coming in multiple formats and varying resolutions, and its complexity, involving features like occlusions, scale variations, and atmospheric interference. Additionally, geospatial datasets are large and computationally expensive to process, while a lack… →

Adapting 2D-based segmentation models to effectively process and segment 3D data presents a significant challenge in the field of computer vision. Traditional approaches often struggle to preserve the inherent spatial relationships in 3D data, leading to inaccuracies in segmentation. This challenge is critical for advancing applications like autonomous driving, robotics, and virtual reality, where a… →

Social network generation finds numerous applications in various fields, such as epidemic modeling, social media simulations, and understanding social phenomena like polarization. Creating realistic social networks is crucial when real networks cannot be directly observed due to privacy concerns or other constraints. These generated networks are vital for accurately modeling interactions and predicting outcomes in… →

Large language models (LLMs) have significantly progressed in various domains, including natural language understanding and code generation. These models can generate coherent text and solve complex tasks. However, LLMs face challenges when applied to more specialized areas such as competitive programming and code generation. This field focuses on improving the models’ ability to generate diverse,… →

Graph neural networks (GNNs) have emerged as powerful tools for capturing complex interactions in real-world entities and finding applications across various business domains. These networks excel at generating effective graph entity embeddings by encoding both node features and structural insights, making them invaluable for numerous downstream tasks. GNNs have succeeded in node-level financial fraud detection,… →

Artificial intelligence (AI) and database management systems have increasingly converged, with significant potential to improve how users interact with large datasets. Recent advancements aim to allow users to pose natural language questions directly to databases and retrieve detailed, complex answers. However, current tools are limited in addressing real-world demands. Traditional AI models, such as language… →