Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
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Research talk: Causal ML and fairness
Observing heterogeneous treatment effects across different demographic groups is an important mechanism for evaluating fairness. However, relatively little data is available for certain demographics, in which case researchers may combine multiple data sources to increase…
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Keynote: Cloud Intelligence: Infusing AI into cloud computing systems
Cloud computing platforms have become part of the basic infrastructure of the world, bringing unprecedented opportunities of digital transformation to business, society, and human life. Therefore, the quality of cloud computing platforms, including availability, reliability,…
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Closing remarks: Reinforcement Learning
Speaker: John Langford, Partner Research Manager, Microsoft Research NYC Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit
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Research talk: Building towards a responsible data economy
Speaker: Dawn Song, Professor, UC Berkeley Data is a key driver of the modern economy and AI/machine learning. However, a lot of this data is sensitive, and handling sensitive data has caused unprecedented challenges for…
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Research talk: Transformer efficiency: From model compression to training acceleration
At Microsoft Research, we are approaching large-scale AI from many different perspectives, which include not only creating new, bigger models, but also developing unique ways of optimizing AI models from training to deployment. One of…
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Research talk: Towards data-efficient machine learning with meta-learning
At Microsoft Research, we are approaching large-scale AI from many different perspectives, which include not only creating new, bigger models, but also developing unique ways of optimizing AI models from training to deployment. One of…
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Research talk: Automating and Optimizing IT Operations Management with AI
The vision of self-aware, self-healing, and self-managing Information Technology (IT) systems has remained elusive till recently. Recent advancements in Cloud computing, Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) in general, are…
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Research talk: Breaking the deadly triad with a target network
The deadly triad refers to the instability of an off-policy reinforcement learning (RL) algorithm when it employs function approximation and bootstrapping simultaneously, and this is a major challenge in off-policy RL. Join PhD student Shangtong…
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Panel: Challenges and opportunities of causality
What is causal machine learning? Is it the same as causality research? What are the recent advances and future opportunities? This panel is a unique session where you can hear world-leading researchers’ thoughts on causal…
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Research talk: Focal Attention: Towards local-global interactions in vision transformers
At present, deep neural networks have become prevalent for building AI systems for vision, language and multimodality. However, how to build efficient and task-oriented models are still challenging problems for researchers. In these lightning talks,…
