In the evolving field of artificial intelligence, a major challenge has been building models that excel in specific tasks while also being capable of understanding and reasoning across multiple data types, such as text, images, and audio. Traditional large language models have been successful in natural language processing (NLP) tasks, but they often struggle to…
In today’s increasingly interconnected world, effective communication across languages is essential. However, many natural language processing (NLP) models still struggle with less common languages. This challenge is particularly evident for low-resource languages such as Thai, Mongolian, and Khmer, which lack the data and processing infrastructure available for languages like English or Chinese. Traditional NLP models…
The development of vision-language models (VLMs) has faced challenges in handling complex visual question-answering tasks. Despite substantial advances in reasoning capabilities by large language models like OpenAI’s GPT-o1, VLMs still struggle with systematic and structured reasoning. Current models often lack the ability to organize information and engage in logical, sequential reasoning, limiting their effectiveness for…
In recent years, the development of large language models has significantly advanced natural language processing (NLP). These models, trained on extensive datasets, can generate, understand, and analyze human language with remarkable proficiency. However, building such models requires substantial amounts of data, and access to high-quality multilingual datasets remains a considerable challenge. The scarcity of openly…
The field of artificial intelligence is advancing rapidly, yet significant challenges remain in developing and applying AI systems, particularly in complex reasoning. Many current AI solutions, including advanced models like GPT-4 and Claude 3.5 Sonnet, still struggle with intricate coding tasks, deep conversations, and mathematical reasoning. The limitations of individual models—no matter how sophisticated—lead to…
Recommender systems have been widely applied for studying user preferences; however, they face significant challenges in accurately capturing user preferences, particularly in the context of neural graph collaborative filtering. While these systems use interaction histories between users and items through Graph Neural Networks (GNNs) to mine latent information and capture high-order interactions, the quality of…
Identifying gene deletion strategies for growth-coupled production in genome-scale metabolic models presents significant computational challenges. Growth-coupled production, which links cell growth to the synthesis of target metabolites, is essential for metabolic engineering applications. However, deriving gene deletion strategies for large-scale models places high computational demand since there is a massive search space combined with the…
In recent times, Retrieval-augmented generation (RAG) has become popular due to its ability to solve challenges using Large Language Models, such as hallucinations and outdated training data. A RAG pipeline consists of two components: a retriever and a reader. The retriever component finds useful information from an exterior knowledge base, which is then included alongside…
Self-supervised learning on offline datasets has permitted large models to reach remarkable capabilities both in text and image domains. Still, analogous generalizations for agents acting sequentially in decision-making problems are difficult to attain. The environments of classical Reinforcement Learning (RL) are mostly narrow and homogeneous and, consequently, hard to generalize. Current reinforcement learning (RL) methods…
Support Vector Machines (SVMs) are a powerful and versatile supervised machine learning algorithm primarily used for classification and regression tasks. They excel in high-dimensional spaces and are particularly effective when dealing with complex datasets. The core principle behind SVM is to identify the optimal hyperplane that effectively separates data points into different classes while maximizing…