Generative AI, including Language Models (LMs), holds the promise to reshape key sectors like education, healthcare, and law, which rely heavily on skilled professionals to navigate complex ...

In the realm of machine learning, addressing weight-space features like weights, gradients, or sparsity masks of neural networks is often pivotal. Recent endeavors have yielded encouraging progress in ...

Meta-learning stands out as a potent strategy to facilitate the rapid acquisition of new skills by AI systems, even with limited data. This methodology fosters the exploration of representations and ...

Large language models (LLMs) have dominated the natural language processing (NLP) field. But building an NLP system in traditional way however requires substantial efforts. To address this issue, ...

Pre-trained Large Language Models (LLMs) have surged in popularity for their efficacy in addressing various natural language tasks. More recently, their potential in guiding autonomous web navigation ...

Despites that Large Language Models (LLMs) become the status quo in Natural Language Processing community, most of the existing state-of-the-art models need to be trained directly on language tasks to ...

FastSAM Drastically Reduces Cost to Provide Real-Time Solution for Segment Anything Model In a new paper Fast Segment Anything, a research team from Chinese Academy of Sciences, University of Chinese ...

Google & Waterloo U Scales Generative Retrieval to Handle 8.8M Passages In a new paper How Does Generative Retrieval Scale to Millions of Passages? a research team from Google Research and University ...

Large language models (LLMs) pretrained on massive data are being used in countless real-world applications. However — as computer scientists have known for decades — not all data is equal, and this ...

The 19th-century British philosopher Thomas Carlyle ascribed human progress to a key historical development: “Man is a tool-using animal. Without tools he is nothing, with tools he is all.” While ...

Transformer-based models debuted in 2017 and have come to dominate the natural language processing (NLP) domain. Transformers convert their text inputs into tokens representing words, subwords, ...