Synthetic data could potentially help address some privacy concerns with AI model development and training, but it comes with limitations. Researchers at Microsoft are exploring techniques for producing more realistic data with strong privacy protections.
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Join us for Research Forum (opens in new tab), an event series that explores recent research advances, bold…
Digital pathology helps decode tumor microenvironments for precision immunotherapy. In joint work with Providence and UW, we’re sharing Prov-GigaPath, the first whole-slide pathology foundation model, for advancing clinical research.
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Large language models (LLMs) have shown remarkable performance in generating text similar to that created by people, proving…
From immersive virtual experiences to interactive design tools, Microsoft Research is at the frontier of exploring how people engage with technology. Discover our latest breakthroughs in human-computer interaction research at CHI 2024.
RASCAL is an untethered robot with a modular design, allowing it to move flexibly along and between evenly spaced storage shelves. Discover how it can address the availability and scalability challenges of existing automated storage and retrieval systems.
Property prediction for materials under realistic conditions has been a long-standing challenge within the digital transformation of materials design. MatterSim investigates atomic interactions from the very fundamental principles of quantum mechanics.
Autoscaling can optimize cloud resource usage and costs by adjusting to demand. VASIM shows that simplifying testing and refinement of autoscaling algorithms can enable rapid development and evaluation of more efficient & cost-effective autoscaling strategies.
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.
LoftQ boosts LLM efficiency by streamlining the fine-tuning process, reducing computational demands while preserving high performance. Innovations like this can help make AI technology more energy-efficient.
In this edition: Can LLMs transform natural language into formal method postconditions; Semantically aligned question + code generation for automated insight generation; Explaining CLIP performance disparities on blind/low vision data; plus recent news.
From AI and deep learning to innovations in infrastructure, researchers from Microsoft are bridging the gap between architecture, programming languages, and operating systems to advance the state of the art at ASPLOS 2024.
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