Microsoft Research Blog

Artificial intelligence

  1. Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent 

    September 1, 2022 | [Guest] Ajaykrishna Karthikeyan (ajaykrishna-karthikeyan), Naman Jain, Nagarajan Natarajan, and Prateek Jain

    Decision trees provide a rich family of highly non-linear but efficient models, due to which they continue to be the go-to family of predictive models by practitioners across domains. But learning trees is a challenging problem due to their highly discrete and non-differentiable decision boundaries.…

  2. DeepDev-PERF: A Deep Learning-Based Approach for Improving Software Performance 

    August 1, 2022

    Improving software performance is an important yet challenging part of the software development cycle. Today, the majority of performance inefficiencies are identified and patched by performance experts. Recent advancements in deep learning approaches and the wide-spread availability of open source data creates a great opportunity…

  3. AutoCoMet: Smart Neural Architecture Search via Co-Regulated Shaping Reinforcement 

    August 1, 2022

    Designing suitable deep model architectures, for AI-driven on-device apps and features, at par with rapidly evolving mobile hardware and increasingly complex target scenarios is a difficult task. Though Neural Architecture Search (NAS/AutoML) has made this easier by shifting paradigm from extensive manual effort to automated…

  6. Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programming 

    July 1, 2022 | Supriyo GHOSH, Laura Wynter, Shiau Hong Lim, and Duc Thien Nguyen

    We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of…

  7. MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control 

    July 1, 2022

    Control of simulated humanoid characters is a challenging benchmark for sequential decision-making methods, as it assesses a policy’s ability to drive an inherently unstable, discontinuous, and high-dimensional physical system. One widely studied approach is to utilize motion capture (MoCap) data to teach the humanoid agent…

  8. RetroGraph: Retrosynthetic Planning with Graph Search 

    June 23, 2022

    Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted many research interests and shown promising results for…

  10. CLIP-Event: Connecting Text and Images with Event Structures 

    June 19, 2022

    Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of…

  11. RegionCLIP: Region-based Language-Image Pretraining 

    June 16, 2022

    Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift:…

  12. DETReg: Unsupervised Pretraining with Region Priors for Object Detection 

    June 7, 2022

    Unsupervised pretraining has recently proven beneficial for computer vision tasks, including object detection. However, previous self-supervised approaches are not designed to handle a key aspect of detection: localizing objects. Here, we present DETReg, an unsupervised pretraining approach for object DEtection with TRansformers using Region priors.…