Microsoft Research Blog

Artificial intelligence

  1. Evaluation of synthetic and experimental training data in supervised machine learning applied to charge state detection of quantum dots 

    May 15, 2020 | Jana Darulova, Matthias Troyer, and Maja C. Cassidy

    Automated tuning of gate-defined quantum dots is a requirement for large scale semiconductor based qubit initialisation. An essential step of these tuning procedures is charge state detection based on charge stability diagrams. Using supervised machine learning to perform this task requires a large dataset for…

  2. MOReL : Model-Based Offline Reinforcement Learning 

    May 11, 2020 | Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims

    In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to train RL policies offline can greatly expand the applicability of RL, its data efficiency, and its experimental…

  4. CONFIG: Controllable Neural Face Image Generation 

    May 5, 2020

    Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind. If this new technology is to find practical uses, we need to achieve…

  5. Parallelizing Adam Optimizer with Blockwise Model-Update Filtering 

    May 4, 2020 | Kai Chen, Haisong Ding, and Qiang Huo

    Recently Adam has become a popular stochastic optimization method in deep learning area. To parallelize Adam in a distributed system, synchronous stochastic gradient (SSG) technique is widely used, which is inefficient due to heavy communication cost. In this paper, we attempt to parallelize Adam with…

  6. Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings 

    May 3, 2020 | Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, and Andreas Stolcke

    Involvement hot spots have been proposed as a useful concept for meeting analysis and studied off and on for over 15 years. These are regions of meetings that are marked by high participant involvement, as judged by human annotators. However, prior work was either not…

  7. Supervised Deep Hashing for Efficient Audio Event Retrieval 

    May 3, 2020 | Arindam Jati and Dimitra Emmanouilidou

    Efficient retrieval of audio events can facilitate real-time implementation of numerous query and search-based systems. This work investigates the potency of different hashing techniques for efficient audio event retrieval. Multiple state-of-the-art weak audio embeddings are employed for this purpose. The performance of four classical unsupervised…

  9. Code-mixed parse trees and how to find them 

    May 1, 2020 | Anirudh Srinivasan, Sandipan Dandapat, and Monojit Choudhury

    In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using…

  10. A New Dataset for Natural Language Inference from Code-mixed Conversations 

    May 1, 2020 | Simran Khanuja, Sandipan Dandapat, Sunayana Sitaram, and Monojit Choudhury

    Natural Language Inference (NLI) is the task of inferring the logical relationship, typically entailment or contradiction, between a premise and hypothesis. Code-mixing is the use of more than one language in the same conversation or utterance, and is prevalent in multilingual communities all over the…

  11. AIBench Training: Balanced Industry-Standard AI Training Benchmarking 

    April 30, 2020

    Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks, while using a few AI component benchmarks alone in the other stages may lead to misleading conclusions. This paper proposes a balanced benchmarking methodology. Performing an exhaustive survey on Internet service AI domains, we…