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Effective reasoning is crucial to solving complex problems in fields such as math and programming, and LLMs have shown significant improvements through long -term reasoning. However, transformer -based models face restrictions due to their square calculation complexity and linear memory requirements, making it challenging to treat long sequences effectively. While techniques such as Chain of … Read more

Biophysical modeling acts as a valuable tool for understanding brain function by connecting neural dynamics at the cellular level with large -scale brain activity. These models are controlled by biologically interpretable parameters, many of which can be measured directly through experiments. However, some parameters remain unknown and must be set to adjust simulations with empirical … Read more

Tabular data is used widely in different areas, including scientific research, economics and healthcare. Traditionally, machine learning models such as gradient-boosted decision trees are preferred to analyze tabular data due to their efficiency in handling heterogeneous and structured data sets. Despite their popularity, these methods have remarkable limitations, especially in terms of performance on unseen … Read more

Ragrams have gained attention to their ability to improve LLMs by integrating external knowledge sources, helping to address restrictions such as hallucinations and outdated information. Traditional RAG approaches are often dependent on the surface level documents, despite their potential, lack deeply embedded insights in texts or overlook information spread over several sources. These methods are … Read more