The visual medium (images and videos) naturally contains a large amount of information redundancy, thereby providing a great opportunity for leveraging efficiency in processing. While Vision Transformer (ViT) based models scale effectively to large data regimes, they fail to capitalize on this inherent redundancy, leading…
Importance Rates of maternal obesity are increasing in the US. Although obesity is a well-documented risk factor for numerous poor pregnancy outcomes, it is not currently a recognized risk factor for sudden unexpected infant death (SUID). Objective To determine whether maternal obesity is a risk factor for…
Bhaskar Mitra recently received the ACM SIGIR Early Career Researcher Awards for the following two categories: Excellence in Research: "For high-impact work, including research in neural IR and the establishing the MS-MARCO ranking benchmark" and Excellence in Community Engagement: "For a strong record of creating…
In the realm of reinforcement learning (RL), accounting for risk is crucial for making decisions under uncertainty, particularly in applications where safety and reliability are paramount. In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures…
The theory of arithmetic is integral to many uses of SMT solvers. Z3 has implemented native solvers for arithmetic reasoning since its first release. We present a full re-implementation of Z3’s original arithmetic solver. It is based on substantial experiences from user feedback, engineering and…
Redmond opens up to discuss its new tool, which can extract data from unstructured text using large language models. Microsoft's GraphRAG is a new approach to Retrieval-Augmented Generation (RAG) that Redmond has described a "significant advance in enhancing the capability of LLMs."
RepoClassBench (RCB): is a repository-level code-generation benchmark. Retrieve-RepoTools-Reflect (RRR) is a framework for code generation using Language Models (LLMs) with static-analysis tools in an agent setup.
Introducing Trace, Microsoft and Stanford University's novel AI optimization framework, now available as a Python library. Trace adapts dynamically and optimizes a wide range of applications from language models to robot control.
This report presents the most recent findings of Microsoft’s research initiative on AI and Productivity, which seeks to measure and understand the productivity gains associated with LLM-powered productivity tools like Microsoft Copilot. The report synthesizes research results from over a dozen recent studies conducted by…
Let’s imagine a scenario—inspired by true events—in which a company has deployed an AI-powered system in a hospital. The system provides recommendations for treatment plans. Some clinicians find that the new system requires them to change their routine and significantly adds to their workload, so…
The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about medical interventions. Many published studies are flawed because they violate these assumptions and entail biases such as residual confounding, selection bias,…
Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback (like numerical rewards or losses, natural language text, compiler errors, etc.). Trace generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Trace is implemented as a PyTorch-like…
