The seamless integration of Large Language Models (LLMs) into the fabric of specialized scientific research represents a pivotal shift in the landscape of computational biology, chemistry, and beyond. Traditionally, LLMs excel in broad natural language processing...

Neural operators, specifically the Fourier Neural Operators (FNO), have revolutionized how researchers approach solving partial differential equations (PDEs), a cornerstone problem in science and engineering. These operators have shown exceptional promise in learning...

Posted by Nishant Jain, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research The constantly changing nature of the world around us poses a significant challenge for the development of AI models. Often, models are trained on longitudinal...

In recent years, notable advancements in the design and training of deep learning models have led to significant improvements in image recognition performance, particularly on large-scale datasets. Fine-Grained Image Recognition (FGIR) represents a specialized domain...

The study diverges from previous approaches by concentrating on aligning long context, specifically by fine-tuning language models to interpret lengthy user prompts. Challenges include the absence of extensive datasets for supervised fine-tuning, difficulties in...

One of the main hurdles in achieving high forecast accuracy is dealing with data with multiple seasonality patterns. This means that the data might show variations daily, weekly, monthly, or yearly, making it tricky to predict future trends accurately. Some tools and...