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.

这项研究的目的是开发改善AI系统与人类之间相互作用的方法。 AI系统在决策过程中越来越普遍，例如，用于避免交通拥堵的导航系统。但是，尽管人们可以向同伴寻求建议及其背后的原因，但今天的AI系统通常无法为其产出提供理由。因此，用户必须1）盲目相信系统建议，2）单独验证合理性，或3）忽略建议。为了解决这三个选项都不是可取的局限性，可以解释其产出的研究开发模型，可以控制的研究开发算法以及允许与模型相互作用的研究开发方法。在提出建议时，该研究旨在通过提取数据的哪些部分提供最多的证据来获得解释性，控制能力和互动性的启发。为此，我们使用AI系统中的概率分布。此外，这项研究将支持伊利诺伊大学Urbana-Champaign大学的一系列博士学位和本科生的发展，在当地社区中的外展活动以及两个类别的发展：入门级机器学习的新本科课程和在AI系统中的分布的新颖级别的毕业生课程。网络； 2）控制通用模型中的采样过程； 3）在增强学习系统中相互作用。这项研究的技术目的分为三个推力。第一个推力量表的注意机制是包含多种模态的数据，并开发了算法，这些算法可以更好地捕获这些高维设置中的概率分布。第二个推力将这些算法推广到更复杂的数据结构，并利用这些结果为AI系统产生高维输出的AI系统，例如图像的描述。该奖项反映了NSF的法定任务，人类与AI系统之间的第三个推力研究互动。通过基金会的知识分子优点和更广泛的影响评估标准，NSF的法定任务被认为是珍贵的支持。

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

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

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