NSF-BSF: RI: Small: Structured Distributions in Deep Nets

NSF-BSF：RI：小型：深度网络中的结构化分布

• 批准号: 2008387
• 负责人: Alexander Schwing
• 金额: $ 45万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008387&HistoricalAwards=false
• 关键词: NSF; BSF; RI; Small; Structured

The goal of this research is to develop methods that improve interaction between AI systems and humans. AI systems are increasingly more prevalent in decision making processes, for instance, in navigation systems for avoiding traffic jams. However, while one could ask a peer for a recommendation and the reason behind it, today’s AI systems often cannot provide a justification for their output. Hence, users have to 1) trust the system recommendation blindly, 2) verify plausibility individually, or 3) ignore the recommendation. To address the limitation that none of those three options is desirable, the research develops models which can explain their output, the research develops algorithms which can be controlled, and the research develops methods which permit interaction with the model. Inspired by a human focusing on subsets of the data when making a recommendation, the research seeks to obtain explain-ability, control-ability and interact-ability by extracting which parts of the data provided most evidence. For this we use probability distributions inside AI systems. Furthermore, this research will support development of a cohort of PhD and undergraduate students at the University of Illinois at Urbana-Champaign, outreach activities in the local neighborhood and development of two classes: a novel undergrad class on entry-level machine learning and a novel grad class on distributions in AI systems.Technically, distributions inside AI systems are often referred to as attention. Attention provides a compelling framework 1) to explain the decisions formed in discriminative networks; 2) to control the sampling process in generative models; and 3) to interact in reinforcement learning systems. The technical aims of this research are divided into three thrusts. The first thrust scales attention mechanisms to data that comprises multiple modalities and develops algorithms which better capture probability distributions in those high-dimensional settings. The second thrust generalizes those algorithms to more complex data structures and leverages those results for AI systems which generate high-dimensional output, e.g., a description of an image. The third thrust studies interaction between humans and AI systems that leverage distributions.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.

这项研究的目标是开发改善人工智能系统和人类之间交互的方法，这些方法在决策过程中越来越普遍，例如在避免交通拥堵的导航系统中。由于推荐及其背后的原因，当今的人工智能系统通常无法为其输出提供合理性，因此，用户必须 1）盲目信任系统推荐，2）单独验证合理性，或 3）忽略推荐。这三个选项中没有一个是可取的，该研究开发了可以解释其输出的模型，该研究开发了可以控制的算法，并且该研究开发了允许与模型交互的方法受人类在提出建议时关注数据子集的启发，该研究旨在通过提取提供最多证据的数据部分来获得解释能力、控制能力和交互能力。此外，这项研究将支持一批博士生和本科生的发展。伊利诺伊大学在厄巴纳-香槟分校，在当地社区开展外展活动并开发两个课程：一个关于入门级机器学习的新型本科课程和一个关于人工智能系统分布的新型研究生课程。从技术上讲，人工智能系统内部的分布通常被称为注意力。注意力提供了一个令人信服的框架：1）解释判别网络中形成的决策；2）控制生成模型中的采样过程；3）在强化学习系统中进行交互。第一个推力将注意力机制扩展到包含多种模态的数据，并开发能够更好地捕获这些高维设置中的概率分布的算法，第二个推力将这些机制推广到更复杂的数据结构，并利用这些结果生成高维算法的人工智能系统，例如，图像的描述。第三个重点研究利用分布的人类和人工智能系统之间的交互。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Context-Aware Relative Object Queries to Unify Video Instance and Panoptic Segmentation

• DOI: 10.1109/cvpr52729.2023.00617
• 发表时间: 2023-06
• 期刊: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Anwesa Choudhuri;Girish V. Chowdhary;A. Schwing

DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data

• DOI: 10.48550/arxiv.2211.14694
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Tiantian Fang;Ruoyu Sun;A. Schwing

Learning to Decompose Visual Features with Latent Textual Prompts

• DOI: 10.48550/arxiv.2210.04287
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Feng Wang;Manling Li;Xudong Lin;Hairong Lv;A. Schwing;Heng Ji

CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations

• DOI: 10.48550/arxiv.2210.09496
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Kai Yan;A. Schwing;Yu-Xiong Wang

Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks

• DOI: 10.48550/arxiv.2210.08001
• 发表时间: 2022-10
• 期刊: ArXiv
• 作者: Renan A. Rojas-Gomez;Teck-Yian Lim;A. Schwing;M. Do;Raymond A. Yeh