Jianfeng Gao
Technical Fellow & Corporate Vice President
Nouvelles et reportages
Microsoft Research Forum Episode 4: The future of multimodal models, a new “small” language model, and other AI updates
Explore multimodal & small language models, plus advanced benchmarks for AI evaluation. Microsoft researchers are working on breakthroughs in weather prediction, materials design, even a new kind of computer for AI inference and hard optimization problems.
LLM profiling guides KV cache optimization
| Liyuan Liu et Jianfeng Gao
LLMs rely on memory-intensive mechanisms like the key-value (KV) cache to store and quickly retrieve data. FastGen optimizes KV cache usage, reducing LLM memory demands by up to 50% while maintaining performance.
Research Focus: Week of February 5, 2024
Research Focus: New Research Forum series explores bold ideas in the era of AI; LASER improves reasoning in language models; Cache-Efficient Top-k Aggregation over High Cardinality Large Datasets; Six Microsoft researchers named 2023 ACM Fellows.
Jianfeng Gao, Sumit Gulwani, Nicole Immorlica, Stefan Saroiu, Manik Varma, and Xing Xie are among the new class of 68 Association for Computing Machinery (ACM) fellows for their transformative contributions to computing science and technology.
Data Formulator: A concept-driven, AI-powered approach to data visualization
| Chenglong Wang, Bongshin Lee, John Thompson, Steven Drucker, et Jianfeng Gao
Visualization is vital for understanding complex data, but existing tools require “tidy data,” adding extra steps. Learn how Data Formulator transforms concepts into visuals, promoting collaboration between analysts and AI agents.
GODEL: Combining goal-oriented dialog with real-world conversations
| Baolin Peng, Michel Galley, Lars Liden, Chris Brockett, Zhou Yu, et Jianfeng Gao
They make restaurant recommendations, help us pay bills, and remind us of appointments. Many people have come to rely on virtual assistants and chatbots to perform a wide range of routine tasks. But what if a single dialog agent, the…
Dans l’actualité | Analytics India
Interview with the team behind Microsoft’s µTransfer
Recently, researchers – Edward Hu, Greg Yang, Jianfeng Gao from Microsoft, introduced µ-Parametrization, which offers maximal feature learning even in infinite-width limit.
µTransfer: A technique for hyperparameter tuning of enormous neural networks
| Edward Hu, Greg Yang, et Jianfeng Gao
Great scientific achievements cannot be made by trial and error alone. Every launch in the space program is underpinned by centuries of fundamental research in aerodynamics, propulsion, and celestial bodies. In the same way, when it comes to building large-scale…
You get what you measure: New NLU benchmarks for few-shot learning and robustness evaluation
| Jianfeng Gao et Ahmed Awadallah
Recent progress in natural language understanding (NLU) has been driven in part by the availability of large-scale benchmarks that provide an environment for researchers to test and measure the performance of AI models. Most of these benchmarks are designed for…