arXiv ·
MBZUAI researchers introduce LLMVoX, a 30M-parameter, LLM-agnostic, autoregressive streaming text-to-speech (TTS) system that generates high-quality speech with low latency. The system preserves the capabilities of the base LLM and achieves a lower Word Error Rate compared to speech-enabled LLMs. LLMVoX supports seamless, infinite-length dialogues and generalizes to new languages with dataset adaptation, including Arabic.
arXiv ·
This paper introduces a unified deep autoregressive model (UAE) for cardinality estimation that learns joint data distributions from both data and query workloads. It uses differentiable progressive sampling with the Gumbel-Softmax trick to incorporate supervised query information into the deep autoregressive model. Experiments show UAE achieves better accuracy and efficiency compared to state-of-the-art methods.
arXiv ·
The paper introduces Duet, a hybrid neural relation understanding method for cardinality estimation. Duet addresses limitations of existing learned methods, such as high costs and scalability issues, by incorporating predicate information into an autoregressive model. Experiments demonstrate Duet's efficiency, accuracy, and scalability, even outperforming GPU-based methods on CPU.
KAUST ·
KAUST alumna Yuan Yan received an honorable mention from the American Statistical Association (ASA) for her paper on "Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid." Yan, who graduated from KAUST in 2018, was part of Professor Marc Genton's Spatio-Temporal Statistics & Data Science group. She is now a postdoctoral fellow at Dalhousie University, researching fisheries science using spatial statistical models. Why it matters: This recognition highlights the quality of research and education at KAUST, especially in the field of spatio-temporal statistics, and its impact on addressing real-world sustainability challenges.