Microsoft Research Lab Asia

MobiCom is one of the premier international academic conferences in the field of mobile computing and wireless networks. In this article,  we select several papers from Microsoft Research Asia that were accepted at MobiCom 2024. These papers explore a diverse range of topics, including mobile task automation, remote auscultation, DNN inference, gas sensing, passive sensing, wireless The post MobiCom 2024 highlights from Microsoft Research Asia: Exploring innovations in wireless mobile technology and applications appeared first on Microsoft Research.

Welcome to the first exhibition zone, where you’ll discover Microsoft Research Asia’s groundbreaking advances in foundational AI research, covering areas like model architecture, algorithm optimization, system networks, and data processing. Together, they push the boundaries of AI’s potential and establish a robust foundation for its evolution. Booth A: Capability leap – Innovations in model architecture The post Microsoft Research Asia 2024 annual technology exhibition: An AI journey into the future appeared first on Microsoft Research.

In industry today, research and development (R&D) plays a pivotal role in boosting productivity, especially in the AI era. However, the rapid advance of AI has exposed the limitations of traditional R&D automation methods. These methods often lack the intelligence needed to address the demands of innovative research and complex development tasks, falling short of The post RD-Agent: An open-source solution for smarter R&D appeared first on Microsoft Research.

Since the Industrial Revolution, the burning of fossil fuels and changes in land use, especially deforestation, have driven the rise in atmospheric carbon dioxide (CO2). While terrestrial vegetation and oceans serve as natural carbon sinks, absorbing some of this CO2, emissions have consistently outpaced their annual capacity. This imbalance has led to a continuous rise The post Low latency carbon budget analysis reveals large decline in land carbon sink (2023) appeared first on Microsoft Research.

Although large language models (LLMs) excel in language-focused tasks like news writing, document summarization, customer service, and virtual assistants, they face challenges when it comes to  learning and inference on numeric and structured industry data, such as tabular data and time series. To address these issues, researchers from Microsoft Research Asia have proposed a new The post Towards industrial foundation models: Integrating large language models with industrial data intelligence appeared first on Microsoft Research.

As cloud AI workloads grow in complexity and scale, maintaining high system reliability has become crucial. Traditional methods of ensuring system reliability, such as using redundant components, inadvertently introduce a new problem: subtle performance degradation, also known as “gray failures”. Gray failures are caused by the gradual failure of redundant components and are characterized by The post USENIX ATC 2024 best paper | How Microsoft is improving cloud AI infrastructure reliability appeared first on Microsoft Research.

Author: Machine Learning Group Time-series forecasting is crucial across various industries, including health, energy, commerce, climate, etc. Accurate forecasts over different prediction horizons are essential for both short-term and long-term planning needs across these domains. For instance, during a public health emergency such as the COVID-19 pandemic, projections of infected cases and fatalities over one to The post ProbTS: Unified benchmarking for time-series forecasting appeared first on Microsoft Research.

Author: Youshan Miao Today, deep learning has permeated our daily lives. As the size of models continues to grow, training these models on massive GPU accelerators has become increasingly time-consuming and costly. To effectively harness the power of massive GPUs and enhance efficiency, researchers have been developing various parallel strategies to improve performance across multiple The post nnScaler: Exploring a new paradigm for parallel execution in deep learning appeared first on Microsoft Research.

Author: Shujie Liu In recent years, the rapid advancement of AI has continually expanded the capabilities of Text-to-Speech (TTS) technology. Ongoing optimizations and innovations in TTS have enriched and simplified voice interaction experiences. These research developments hold significant potential across various fields, including education, entertainment, and multilingual communication, etc. Traditional TTS systems, trained with high-quality The post VALL-E 2: Enhancing the robustness and naturalness of text-to-speech models appeared first on Microsoft Research.

Author: Chang Xu Diffusion probabilistic models have the capacity to generate high-fidelity samples for generative time series forecasting. However, they also present issues of instability due to their stochastic nature. In order to tackle this challenge, researchers from Microsoft Research Asia introduce a novel approach called “MG-TSD”. The paper “MG-TSD: Multi-Granularity Time Series Diffusion Models The post MG-TSD: Advancing time series analysis with multi-granularity guided diffusion model appeared first on Microsoft Research.