RI:Small:Exploring Efficient Bayesian Model-Augmentation Techniques for Decomposible Contrastive Representation Learning

RI:Small：探索可分解对比表示学习的高效贝叶斯模型增强技术

• 批准号: 2223292
• 负责人: Changyou Chen
• 金额: $ 41.57万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223292&HistoricalAwards=false
• 关键词: RI; Small; Exploring; Efficient; Bayesian

Modern deep learning models that trains large on web-scale data with a heavy computational load are achieving state-of-the-art performance on many real-world problems. The ability to learning good representations from the large-scale unlabeled data is the key to the success. Although many methods have been developed, their underlying properties and limitations are still not well understood. Contrastive Representation Learning (CRL) is a method of learning to represent the data such that similar data are close to each other while dissimilar data are far apart. This project investigates the limitations of CRL in order to make it more scalable to big data and be appropriately applied to real-world problems such as computer vision. The project will also support the continued development of machine-learning related courses for undergraduate and graduate students at University at Buffalo as well as various outreach activities to expose K-12 students to the field of computer science.This research develops a scalable conditional decomposable contrastive learning framework from the Bayesian principle and extend it to the federated learning and multi-model learning. First, an augmentation technique will be applied to decouple the entanglement of positive-negative samples, leading to a conditional decomposable loss that can be optimized with unbiased stochastic gradients. Second, the technique will be further refined to develop a communication-efficient distributed training framework for CRL, where clients no longer need to explicitly communicate with other clients to fetch other negative samples. Third, the improved CRL technique will be further applied to the emerging field of foundation models for vision-and-language modeling. The project will also result in the dissemination of shared data and benchmarks to the broader AI community, for example through Github, presentation and workshop organization.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在具有重大计算负载的网络尺度数据上训练大型训练的现代深度学习模型正在实现许多实际问题的最新性能。从大规模未标记的数据中学习良好表示形式的能力是成功的关键。尽管已经开发了许多方法，但它们的基本属性和局限性仍然不太了解。对比表示学习（CRL）是一种学习来表示数据的方法，使相似的数据彼此接近，而相似的数据则相距甚远。该项目研究了CRL的局限性，以使其更可扩展到大数据，并适当地应用于现实世界中的问题，例如计算机视觉。该项目还将支持布法罗大学大学和研究生的机器学习相关课程以及各种外展活动，以及将K-12学生暴露于计算机科学领域。这项研究开发了可扩展的可扩展性可分解性的对比度学习框架，并将其从贝叶斯原则中扩展到贝叶斯的主要学习框架，并将其扩展到联盟的学习学习和多人学习和多人学习和多人模型。首先，将采用增强技术将阳性阴性样品的纠缠解散，从而导致有条件的可分解损失，可以通过无偏的随机梯度进行优化。其次，将进一步完善该技术，以开发CRL的沟通效率分布式培训框架，在该培训框架中，客户不再需要与其他客户进行明确通信以获取其他负面样本。第三，改进的CRL技术将进一步应用于视觉和语言建模基础模型的新兴领域。该项目还将导致将共享数据和基准传播给更广泛的AI社区，例如通过GitHub，演示和研讨会组织。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来进行评估的。

项目成果

STT: Soft Template Tuning for Few-Shot Adaptation

• DOI: 10.1109/icdmw58026.2022.00122
• 发表时间: 2022-07
• 期刊: 2022 IEEE International Conference on Data Mining Workshops (ICDMW)
• 作者: Ping Yu;Wei Wang-;Chunyuan Li;Ruiyi Zhang;Zhanpeng Jin;Changyou Chen

What Do Audio Transformers Hear? Probing Their Representations For Language Delivery & Structure

• DOI: 10.1109/icdmw58026.2022.00120
• 发表时间: 2022-11
• 期刊: 2022 IEEE International Conference on Data Mining Workshops (ICDMW)
• 作者: Yaman Kumar Singla;Jui Shah;Changyou Chen;R. Shah

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

• DOI: 10.48550/arxiv.2209.15245
• 发表时间: 2022-09
• 作者: Jianyi Zhang;Ang Li;Minxue Tang;Jingwei Sun;Xiang Chen;Fan Zhang;Chang Chen;Yiran Chen;H. Li

Persuasion Strategies in Advertisements

广告中的说服策略

• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Yaman Kumar;Rajat Jha;Arunim Gupta;Milan Aggarwal;Aditya Garg;Tushar Malyan;Ayush Bhardwaj;Rajiv Ratn Shah;Balaji Krishnamurthy;Changyou Chen
• 通讯作者: Changyou Chen