Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
Tool
RaCT
This repository implements Ranking-Critical Training (RaCT) for Collaborative Filtering, accepted in International Conference on Learning Representations (ICLR), 2020. By using an actor-critic architecture to fine-tune a differentiable collaborative filtering model, we can improve the performance…
Tool
BERT-nmt
BERT-fused NMT is a new algorithm in which we first use BERT to extract representations for an input sequence, and then the representations are fused with each layer of the encoder and decoder of the…
Tool
KG-A2C
KG-A2C is a reinforcement learning agent that builds a dynamic knowledge graph while exploring and generates natural language using a template-based action space – outperforming all current agents on a wide set of text-based games.
Video
Webinar: Designing Computer Vision Algorithms to Describe the Visual World to People Who Are Blind or Low Vision
A common goal in computer vision research is to build machines that can replicate the human vision system (for example, detect an object or scene category, describe an object or scene, or locate an object).…
Video
Neural Learning To Rank by Bhaskar Mitra (Guest lecture at Emory University)
Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as items to be recommended, in response to user’s need. LTR models typically employ training data,…
Microsoft Research Blog
Research Collection: Research Supporting Responsible AI
Microsoft is committed to the advancement and use of AI grounded in principles that put people first and benefit society. We are putting these principles into practice throughout the company by embracing diverse perspectives, fostering…
