Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
Highlights
Helping first responders achieve more with autonomous systems and AirSim
Read more about Helping first responders achieve more with autonomous systems and AirSim
Webinar: Transparency and Intelligibility Throughout the Machine Learning Life Cycle
Read more about Webinar: Transparency and Intelligibility Throughout the Machine Learning Life Cycle
Watch Katja Hofmann's webinar on reinforcement learning methods from algorithm to application
Read more about Watch Katja Hofmann's webinar on reinforcement learning methods from algorithm to application
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5,722 results found
Publication
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread of pre-training models for NLP applications, they almost focused on text-level manipulation, while neglecting the layout and style information that is vital for document image understanding. In this paper, we propose the LayoutLM to jointly model the interaction between text and layout information across scanned document images, which is beneficial for a great number of real-world document…
Publication
Intelligent Virtual Machine Provisioning in Cloud Computing
Virtual machine (VM) provisioning is a common and critical problem in cloud computing. In industrial cloud platforms, there are a huge number of VMs provisioned per day. Due to the complexity and resource constraints, it needs to be carefully optimized to make cloud platforms effectively utilize the resources. Moreover, in practice, provisioning a VM from scratch requires fairly long time, which would degrade the customer experience. Hence, it is advisable to provision VMs ahead for…
Publication
A New Local Search Framework and Inference Rules for Dominating Set
The problem of finding a minimum dominating set (MinDS) in a graph is a well known combinatorial optimization problem with wide applications. Due to its NP-hardness, for large and hard instances one must resort to heuristic approaches to obtain good solutions within reasonable time. This paper develops an efficient local search algorithm for MinDS, which has two main ideas. The first one is a novel local search framework, while the second is a construction procedure…
Publication
A Federated Approach in Training Acoustic Models
In this paper, a novel platform for Acoustic Model training based on Federated Learning (FL) is described. This is the first attempt to introduce Federated Learning techniques in Speech Recognition (SR) tasks. Besides the novelty of the task, the paper describes an easily generalizable FL platform and presents the design decisions used for this task. Amongst the novel algorithms introduced is a hierarchical optimization scheme employing pairs of optimizers and an algorithm for gradient selection,…
Publication
GAN-based Data Generation for Speech Emotion Recognition
In this work, we propose a GAN-based method to generate synthetic data for speech emotion recognition. Specifically, we investigate the usage of GANs for capturing the data manifold when the data is eyes-off, i.e., where we can train networks using the data but cannot copy it from the clients. We propose a CNN-based GAN with spectral normalization on both the generator and discriminator, both of which are pre-trained on large unlabeled speech corpora. We show…
Publication
Sequence-level self-learning with multi-task learning framework
In this work, we develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR). For untranscribed speech data, the hypothesis from an ASR system must be used as a label. However, the imperfect ASR result makes unsupervised learning difficult to consistently improve recognition performance especially in the case that multiple powerful teacher models are unavailable. In contrast to conventional unsupervised learning approaches, we adopt the multi-task learning(MTL) framework where then…
Event
Causal Machine Learning Day 2020
The Causal ML Day 2020 conference will bring together academics, researchers, and business practitioners who are developing innovative ways to use machine learning techniques to improve causal inference.
Event
AI and Gaming Research Conference
Engage, Learn, and Share at the Microsoft AI and Gaming Research Conference 2020. We invite you to Microsoft in Seattle to join researchers and practitioners from academia, game studios, Gaming and Xbox at Microsoft, and Microsoft Research to engage and share ideas about how AI and machine learning is transforming the landscape of gaming.
Event
AI Institute “Real-World Reinforcement Learning” 2020
The AI Institute "Real-World Reinforcement Learning" 2020 workshop brings together folks working on Reinforcement Learning topics, both theoretical and empirical.
Event
Microsoft HoloLens 2 and Kinect for Azure as tools for computer vision research – Tutorial @ ECCV 2020
Microsoft HoloLens 2 and Kinect for Azure as tools for computer vision research – Tutorial @ ECCV 2020