Text-to-image diffusion models have made significant strides in generating complex and faithful images from input conditions. Among these, Diffusion Transformers Models (DiTs) have emerged as particularly powerful, with OpenAI’s SoRA being a notable application. DiTs, constructed by stacking multiple transformer blocks, utilize the scaling properties of transformers to achieve enhanced performance through flexible parameter expansion.… →
Large language models (LLMs) have demonstrated remarkable reasoning capabilities across various domains. But do they also possess metacognitive knowledge – an understanding of their thinking processes? This intriguing question is explored in a new paper that investigates the metacognitive capabilities of LLMs, specifically in the context of mathematical problem-solving. A team of researchers from Mila,… →
Computer vision is rapidly transforming industries by enabling machines to interpret and make decisions based on visual data. From autonomous vehicles to medical imaging, its applications are vast and growing. Learning computer vision is essential as it equips you with the skills to develop innovative solutions in areas like automation, robotics, and AI-driven analytics, driving… →
Language models (LMs) have gained significant attention in recent years due to their remarkable capabilities. While training these models, neural sequence models are first pre-trained on a large, minimally curated web text, and then fine-tuned using specific examples and human feedback. However, these models often possess undesirable skills or knowledge creators wish to remove before… →
Training FLUX LoRAs has been challenging for users with limited VRAM resources. The process typically requires significant computational power, with existing solutions often demanding a minimum of 24GB VRAM, making it inaccessible for many users who wish to train their models locally. This limitation has been a barrier for those working with lower VRAM setups,… →
Enhancing B2B Personalization with Human-ML Integration: ML has become crucial for business-to-business (B2B) companies seeking to offer personalized services to their clients. However, while ML can handle large data volumes and detect patterns, it often needs a more nuanced understanding that human insights provide, especially in building relationships and dealing with uncertainties in B2B contexts.… →
Graph neural networks (GNNs) are a powerful tool in materials science, particularly in predicting material properties. GNNs leverage the unique ability of graph representations to capture intricate atomic interactions within various materials. These models encode atoms as nodes and chemical bonds as edges, allowing for a detailed representation of molecular and crystalline structures. This capability… →
Introduction to EXAONE 3.0: The Vision and Objectives EXAONE 3.0 represents a significant milestone in the evolution of language models developed by LG AI Research, particularly within Expert AI. The name “EXAONE” derives from “EXpert AI for EveryONE,” encapsulating LG AI Research‘s commitment to democratizing access to expert-level artificial intelligence capabilities. This vision aligns with… →