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…
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.
Garnet is a cache-store system that addresses growing demand for data storage to support interactive web applications and services. Offering several advantages over legacy cache-stores, Garnet is now available as an open-source download.
Structural Understanding Capabilities is a new benchmark for evaluating and improving LLM comprehension of structured table data. This advance can help LLMs process and analyze data more effectively, broadening their applicability in real-world tasks.
In this issue: CaaSPER: vertical autoscaling algorithm dynamically maintains optimal CPU utilization; Improved scene landmark detection for camera localization runs faster, uses less storage; ESUS simplifies usability questionnaires for technical products and services.
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.
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 Microsoft Research Forum (opens in new tab) for a continuous exchange of ideas about science and technology…
AI-backed virtual assistants face challenges in handling complex data structures. TaskWeaver helps users build assistants that understand diverse domain questions, follow examples, and efficiently execute customizable algorithms on complex data structures.
In this issue of Research Focus: Optimized exit-augmented models for scalable efficient inference; NeurIPS LLM Efficiency Challenge; LLM-empowered automated data exploration; Boosting cloud efficiency with data-driven decision-making and optimization.
This position research paper was presented at the 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing (opens in new tab) (CSCW 2023), a premier venue for research on the design and use of technologies that affect groups, organizations, and communities. In the business…
Research Focus: Principal researcher Lester Mackey recognized for pioneering statistical and ML techniques; Pareto frontiers in neural feature learning; structural inequality in the influencer industry; new research on cardinality estimation.
In this issue: Efficient polyglot analytics on semantic data aids query performance; generative retrieval for conversational question answering improves dialogue-based interfaces; a new tool uses ML to address capacity degradation in lithium-ion batteries.
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