Research talk: Prompt tuning: What works and what’s next
The AI landscape has been transformed by the advent of large-scale models like BERT, Turing, and, most recently, GPT-3. Researchers have brought language models to new heights in terms of performance, propelling advancements in search, language translation, and more. As models are growing in size and capability, their applications are also expanding. But they are also becoming harder to deploy and adopt efficiently. Join us for a roundtable discussion hosted by Senior Principal Researcher Ahmed Awadallah to discuss the many ways we can improve efficiency and adaptability in next-generation AI. He will be joined by Microsoft Distinguished Scientist Jianfeng Gao, Princeton University Assistant Professor Danqi Chen, and Song Han, Assistant Professor in MIT’s Electrical Engineering and Computer Science program.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- Deep Learning & Large-Scale AI
- Date:
- Speakers:
- Danqi Chen
- Affiliation:
- Princeton University
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Danqi Chen
Assistant Professor
Princeton University
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Deep Learning & Large-Scale AI
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Research talk: Resource-efficient learning for large pretrained models
Speakers:- Subhabrata (Subho) Mukherjee
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Research talk: Prompt tuning: What works and what's next
Speakers:- Danqi Chen
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Research talk: NUWA: Neural visual world creation with multimodal pretraining
Speakers:- Lei Ji,
- Chenfei Wu
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Research talk: Towards Self-Learning End-to-end Dialog Systems
Speakers:- Baolin Peng
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Research talk: WebQA: Multihop and multimodal
Speakers:- Yonatan Bisk
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Research talk: Closing the loop in natural language interfaces to relational databases
Speakers:- Dragomir Radev
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Roundtable discussion: Beyond language models: Knowledge, multiple modalities, and more
Speakers:- Yonatan Bisk,
- Daniel McDuff,
- Dragomir Radev
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