AI

Using advanced artificial intelligence models, video generation involves creating moving images from textual descriptions or static images. This area of research seeks…

Large language models (LLMs) have become a pivotal part of artificial intelligence, enabling systems to understand, generate, and respond to human language….

Previous 3D model generation from single images faced challenges. Feed-forward architectures produced simplistic objects due to limited 3D data. Gaussian splatting provided…

Test-time aggregation strategies, such as generating and combining multiple answers, can enhance LLM performance but eventually hit diminishing returns. Refinement, where model…

Collaborative Multi-Agent Reinforcement Learning (MARL) has emerged as a powerful approach in various domains, including traffic signal control, swarm robotics, and sensor…

One of the main challenges in cellular automata (CA) systems, particularly in Conway’s Game of Life (Life), lies in predicting their emergent…

Federated learning (FL) is a powerful ML paradigm that enables multiple data owners to train models without centralizing their data collaboratively. This…

Governments and humanitarian organizations need reliable data on building and infrastructure changes over time to manage urbanization, allocate resources, and respond to…

Integer Linear Programming (ILP) is the foundation of combinatorial optimization, which is extensively applied across numerous industries to resolve challenging decision-making issues….

Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding…