Microsoft Research Lab – Asia
新闻与深度文章
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…
| Dongsheng Li, Dongqi Han, 和 Yansen Wang
Researchers and their collaborators are drawing inspiration from the brain to develop more sustainable AI models. Projects like CircuitNet and CPG-PE improve performance and energy efficiency by mimicking the brain’s neural patterns.
Learn what’s next for AI at Research Forum on Sept. 3; WizardArena simulates human-annotated chatbot games; MInference speeds pre-filling for long-context LLMs via dynamic sparse attention; Reef: Fast succinct non-interactive zero-knowledge regex proofs.
In this issue: Research Forum Ep. 4 explores multimodal AI. Registration is now open; Surveying developers’ AI needs; SuperBench improves cloud AI infrastructure reliability; Virtual Voices: Exploring factors influencing participation in virtual meetings.
Abstracts: July 29, 2024
| Gretchen Huizinga 和 Li Lyna Zhang
A lack of appropriate data, decreased model performance, and other obstacles have made it difficult to expand the input language models can receive. Li Lyna Zhang introduces LongRoPE, a method capable of extending content windows to more than 2 million…
The competitive dynamics of AI agents and a method for learning and applying temporal action abstractions represent just some of Microsoft’s contributions to ICML 2024.
Advancing time series analysis with multi-granularity guided diffusion model; An algorithm-system co-design for fast, scalable MoE inference; What makes a search metric successful in large-scale settings; learning to solve PDEs without simulated data.
Microsoft Research and Nissan Motor Corporation have collaborated to develop a machine learning model that improves the accuracy of predicting EV battery degradation by 80%. Learn how this collaboration supports long-term sustainability goals.
Unified databases offer better knowledge transfer between multimodal data types. They provide substantial corpus support for large language models and are poised to drive innovation in underlying hardware, laying the foundation for data-enhanced AI.