Research talk: Focal Attention: Towards local-global interactions in vision transformers
- Jianwei Yang | Microsoft Research Redmond
- Microsoft Research Summit 2021 | Deep Learning & Large-Scale AI
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, Senior RSDE Baolin Peng and Senior Researcher Jianwei Yang from the Deep Learning Group at Microsoft Research Redmond, will discuss end-to-end dialog systems and new architecture for vision systems, respectively. For dialog systems, an end-to-end learning system is achieved by using self-learning from the conversations with a human in the loop. For vision systems, a sparse attention mechanism has been developed for the Vision Transformer to cope with high-resolution image inputs for image classification, object detection and semantic segmentation.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
Deep Learning & Large-Scale AI
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Opening remarks: Deep Learning and Large-Scale AI
- Ahmed Awadallah
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Roundtable discussion: Efficient and adaptable large-scale AI
- Ahmed Awadallah,
- Jianfeng Gao,
- Danqi Chen
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Panel: Large-scale neural platform models: Opportunities, concerns, and directions
- Eric Horvitz,
- Miles Brundage,
- Yejin Choi
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Research talk: WebQA: Multihop and multimodal
- Yonatan Bisk
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Roundtable discussion: Beyond language models: Knowledge, multiple modalities, and more
- Yonatan Bisk,
- Daniel McDuff,
- Dragomir Radev
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Closing remarks: Deep Learning and Large Scale AI
- Jianfeng Gao