CMU researchers propose OpenFLAME: a federated and decentralized location service
Maps are widely used today and are useful in numerous location-based applications, including navigation, ride-sharing, fitness tracking, gaming, robotics, and ...
Tag: CMU
Snowflake and CMU researchers present SuffixDecoding: a new model-free approach to accelerate large language model (LLM) inference using speculative decoding
Large Language Models (LLM) have quickly become a critical component of today's consumer and enterprise applications. However, the need for ...
CMU researchers explore expert guidance and strategic deviations in multi-agent imitation learning
The problem of a mediator learning to coordinate a group of strategic agents is posed through recommendations of actions without ...
CMU researchers propose XEUS: a multilingual universal speech encoder trained on over 4000 languages
Self-supervised learning (SSL) has expanded the reach of speech technologies to many languages by minimizing the need for labeled data. ...
This AI paper from CMU and Google DeepMind studies the role of synthetic data in improving the mathematical reasoning abilities of LLM students
Large language models (LLMs) face a critical challenge in their training process: the looming shortage of high-quality Internet data. Predictions ...
CMU Researchers Introduce TriForce: A Hierarchical Speculative Decoding AI System That Is Scalable to Long Sequence Generation
With the widespread implementation of large language models (LLMs) for long content generation, there is a growing need for efficient ...
CMU researchers present 'Echo Embeddings': an embedding strategy designed to address an architectural limitation of autoregressive models
Neural text embeddings play a critical role in many modern natural language processing (NLP) applications. These embeddings are like fingerprints ...
CMU researchers present FlexLLM: an artificial intelligence system that can handle inference requests and efficient parameter tuning in the same iteration
In artificial intelligence, the rise of large language model (LLM) development has significantly transformed the way machines understand and generate ...
This CMU AI and Meta AI article introduces pre-instruction tuning (PIT): a game changer for training language models on factual knowledge
In the fast-paced world of artificial intelligence, the challenge of keeping large language models (LLMs) up to date with the ...
CMU Researchers Introduce VisualWebArena: An AI Benchmark Designed to Evaluate the Performance of Multimodal Web Agents on Realistic, Visually Stimulating Challenges
The field of artificial intelligence (ai) has always had the goal of automating everyday computing operations using autonomous agents. Basically, ...
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