Closing remarks: Deep Learning and Large Scale AI
AI has undergone a transformation in recent years with the emergence of large-scale models and deep learning. AI models continue to be able to achieve in areas like language and vision. Large AI models also create new challenges, from cost and sustainability of models to their privacy and trustworthiness. This track will explore advancing deep learning and large-scale AI in language, vision, and multimodality responsibly while considering its future impact on people and society.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
- 轨迹:
- Deep Learning & Large-Scale AI
- 日期：
- 演讲者：
- Jianfeng Gao
- 所属机构：
- Microsoft Research Redmond
-
-
Jianfeng Gao
Distinguished Scientist & Vice President
-
-
Deep Learning & Large-Scale AI
-
-
-
Research talk: Resource-efficient learning for large pretrained models
Speakers:- Subhabrata (Subho) Mukherjee
-
-
-
Research talk: Prompt tuning: What works and what's next
Speakers:- Danqi Chen
-
-
-
Research talk: NUWA: Neural visual world creation with multimodal pretraining
Speakers:- Lei Ji,
- Chenfei Wu
-
-
-
-
Research talk: Towards Self-Learning End-to-end Dialog Systems
Speakers:- Baolin Peng
-
Research talk: WebQA: Multihop and multimodal
Speakers:- Yonatan Bisk
-
Research talk: Closing the loop in natural language interfaces to relational databases
Speakers:- Dragomir Radev
-
Roundtable discussion: Beyond language models: Knowledge, multiple modalities, and more
Speakers:- Yonatan Bisk,
- Daniel McDuff,
- Dragomir Radev
-
