Microsoft at NeurIPS 2023
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Microsoft Research blog
ViSNet: A general molecular geometry modeling framework for predicting molecular properties and simulating molecular dynamics
| Tong Wang, Bin Shao, and Tie-Yan Liu
Molecular geometry modeling is a powerful tool for understanding the intricate relationships between molecular structure and biological activity – a field known as structure-activity relationships (SAR). The main premise of SAR is that the biological activity of a molecule is…
Research at Microsoft 2023: A year of groundbreaking AI advances and discoveries
AI saw unparalleled growth in 2023, reaching millions daily. This progress owes much to the extensive work of Microsoft researchers and collaborators. In this review, learn about the advances in 2023, which set the stage for further progress in 2024.
Abstracts: December 12, 2023
| Gretchen Huizinga, Tao Qin, and Lijun Wu
Members of the research community at Microsoft work continuously to advance their respective fields. Abstracts brings its audience to the cutting edge with them through short, compelling conversations about new and noteworthy achievements. In this episode, Senior Principal Research Manager…
Steering at the Frontier: Extending the Power of Prompting
| Eric Horvitz, Harsha Nori, and Yin Tat Lee
We’re seeing exciting capabilities of frontier foundation models, including intriguing powers of abstraction, generalization, and composition across numerous areas of knowledge and expertise. Even seasoned AI researchers have been impressed with the ability to steer the models with straightforward, zero-shot prompts. Beyond…
Phi-2: The surprising power of small language models
| Mojan Javaheripi and Sébastien Bubeck
Phi-2 is now accessible on the Azure model catalog. Its compact size and new innovations in model scaling and training data curation make it ideal for exploration around mechanistic interpretability, safety improvements, and fine-tuning experimentation on a variety of tasks.
Abstracts: December 11, 2023
| Gretchen Huizinga and Alessandro Sordoni
By treating language models as layers in a network and prompts as learnable parameters, researchers aim for more adaptable, reusable LLM architectures. Check out the work in the “Abstracts” podcast series with guest Alessandro Sordoni and at #NeurIPS2023:
NeurIPS 2023 highlights breadth of Microsoft’s machine learning innovation
We’re proud to have 100+ accepted papers At NeurIPS 2023, plus 18 workshops. Several submissions were chosen as oral presentations and spotlight posters, reflecting groundbreaking concepts, methods, or applications. Here’s an overview of those submissions.
MatterGen: Property-guided materials design
| Andrew Fowler, Matthew Horton, Ryota Tomioka, Robert Pinsler, Tian Xie, Claudio Zeni, and Daniel Zügner
The central problem in materials science is to discover materials with desired properties. MatterGen enables broad property-guided materials design.
Research Focus: Week of December 4, 2023
Research Focus: Using LLMs in a Rust-based formal verification framework; Rethinking network measurements with user feedback; 3D telemedicine using HoloportationTM communication technology could enhance overseas surgical visits.