Sequence modeling is a critical domain in machine learning, encompassing applications such as reinforcement learning, time series forecasting, and event prediction. These models are designed to handle data where the order of inputs is significant, making them essential for tasks like robotics, financial forecasting, and medical diagnoses. Traditionally, Recurrent Neural Networks (RNNs) have been used… →

Speech recognition technology focuses on converting spoken language into text. It involves processes such as acoustic modeling, language modeling, and decoding, aiming to achieve high accuracy in transcriptions. Significant advancements have been made in this field, driven by machine learning algorithms and large datasets. These advancements enable more accurate and efficient speech recognition systems, crucial… →

The InternLM research team delves into developing and enhancing large language models (LLMs) specifically designed for mathematical reasoning and problem-solving. These models are crafted to bolster artificial intelligence’s capabilities in tackling intricate mathematical tasks, encompassing formal proofs and informal problem-solving. Researchers have noted that current AI models often need to catch up regarding the depth… →

Machine learning research aims to learn representations that enable effective downstream task performance. A growing subfield seeks to interpret these representations’ roles in model behaviors or modify them to enhance alignment, interpretability, or generalization. Similarly, neuroscience examines neural representations and their behavioral correlations. Both fields focus on understanding or improving system computations, abstract behavior patterns… →

In the rapidly developing fields of data science and Artificial Intelligence (AI), the search for increasingly effective systems is also increasing significantly. The development of Agentic Retrieval-Augmented Generation (RAG) is among the most revolutionary developments of recent times. This strategy is set to completely transform the way information is used and managed, offering a substantial… →

Biomedical data is increasingly complex, high-dimensional, and heterogeneous, encompassing sources such as electronic health records (EHRs), imaging, -omics data, sensors, and text. Traditional data mining and statistical methods must improve with this complexity, often requiring extensive feature engineering and domain expertise to extract meaningful insights. Recent advancements in deep learning offer a transformative approach by… →

Large Language Models (LLMs) have advanced rapidly, especially in Natural Language Processing (NLP) and Natural Language Understanding (NLU). These models excel in text generation, summarization, translation, and question answering. With these capabilities, researchers are keen to explore their potential in tasks that require reasoning and planning. This study evaluates the effectiveness of specific prompting techniques… →

Causal models are crucial for explaining the causal relationships among variables. These models help to understand how various factors interact and influence each other in complex systems. However, it is challenging to find the probabilities related to interventions and conditioning at the same time. Moreover, AI research has focused on two types of models: functional… →