Applied AI letters最新文献_第4页

Computer Vision and Machine Learning Techniques for Quantification and Predictive Modeling of Intracellular Anti‐Cancer Drug Delivery by Nanocarriers 计算机视觉和机器学习技术用于纳米载体细胞内抗癌症药物递送的定量和预测建模

Applied AI letters Pub Date : 2021-11-10 DOI: 10.1002/ail2.50

S. Goswami, Kshama D. Dhobale, R. Wavhale, B. Goswami, S. Banerjee

引用次数: 0

How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking 在可解释的智能系统中，解释细节的水平如何影响人类的表现:一项关于可解释事实核查的研究

Applied AI letters Pub Date : 2021-11-08 DOI: 10.1002/ail2.49

Rhema Linder, Sina Mohseni, Fan Yang, Shiva K. Pentyala, Eric D. Ragan, Xia Ben Hu

{"title":"How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking","authors":"Rhema Linder, Sina Mohseni, Fan Yang, Shiva K. Pentyala, Eric D. Ragan, Xia Ben Hu","doi":"10.1002/ail2.49","DOIUrl":"10.1002/ail2.49","url":null,"abstract":"Explainable artificial intelligence (XAI) systems aim to provide users with information to help them better understand computational models and reason about why outputs were generated. However, there are many different ways an XAI interface might present explanations, which makes designing an appropriate and effective interface an important and challenging task. Our work investigates how different types and amounts of explanatory information affect user ability to utilize explanations to understand system behavior and improve task performance. The presented research employs a system for detecting the truthfulness of news statements. In a controlled experiment, participants were tasked with using the system to assess news statements as well as to learn to predict the output of the AI. Our experiment compares various levels of explanatory information to contribute empirical data about how explanation detail can influence utility. The results show that more explanation information improves participant understanding of AI models, but the benefits come at the cost of time and attention needed to make sense of the explanation.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.49","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46870560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

From heatmaps to structured explanations of image classifiers 从热图到图像分类器的结构化解释

Applied AI letters Pub Date : 2021-11-06 DOI: 10.1002/ail2.46

Li Fuxin, Zhongang Qi, Saeed Khorram, Vivswan Shitole, Prasad Tadepalli, Minsuk Kahng, Alan Fern

{"title":"From heatmaps to structured explanations of image classifiers","authors":"Li Fuxin, Zhongang Qi, Saeed Khorram, Vivswan Shitole, Prasad Tadepalli, Minsuk Kahng, Alan Fern","doi":"10.1002/ail2.46","DOIUrl":"10.1002/ail2.46","url":null,"abstract":"This paper summarizes our endeavors in the past few years in terms of explaining image classifiers, with the aim of including negative results and insights we have gained. The paper starts with describing the explainable neural network (XNN), which attempts to extract and visualize several high-level concepts purely from the deep network, without relying on human linguistic concepts. This helps users understand network classifications that are less intuitive and substantially improves user performance on a difficult fine-grained classification task of discriminating among different species of seagulls. Realizing that an important missing piece is a reliable heatmap visualization tool, we have developed integrated-gradient optimized saliency (I-GOS) and iGOS++ utilizing integrated gradients to avoid local optima in heatmap generation, which improved the performance across all resolutions. During the development of those visualizations, we realized that for a significant number of images, the classifier has multiple different paths to reach a confident prediction. This has led to our recent development of structured attention graphs, an approach that utilizes beam search to locate multiple coarse heatmaps for a single image, and compactly visualizes a set of heatmaps by capturing how different combinations of image regions impact the confidence of a classifier. Through the research process, we have learned much about insights in building deep network explanations, the existence and frequency of multiple explanations, and various tricks of the trade that make explanations work. In this paper, we attempt to share those insights and opinions with the readers with the hope that some of them will be informative for future researchers on explainable deep learning.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.46","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73608694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Improving users' mental model with attention-directed counterfactual edits 通过注意力导向的反事实编辑改善用户的心智模型

Applied AI letters Pub Date : 2021-11-06 DOI: 10.1002/ail2.47

Kamran Alipour, Arijit Ray, Xiao Lin, Michael Cogswell, Jurgen P. Schulze, Yi Yao, Giedrius T. Burachas

{"title":"Improving users' mental model with attention-directed counterfactual edits","authors":"Kamran Alipour, Arijit Ray, Xiao Lin, Michael Cogswell, Jurgen P. Schulze, Yi Yao, Giedrius T. Burachas","doi":"10.1002/ail2.47","DOIUrl":"https://doi.org/10.1002/ail2.47","url":null,"abstract":"In the domain of visual question answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain image-question (IQ) pairs. In this work, we show that showing controlled counterfactual IQ examples are more effective at improving the mental model of users as compared to simply showing random examples. We compare a generative approach and a retrieval-based approach to show counterfactual examples. We use recent advances in generative adversarial networks to generate counterfactual images by deleting and inpainting certain regions of interest in the image. We then expose users to changes in the VQA system's answer on those altered images. To select the region of interest for inpainting, we experiment with using both human-annotated attention maps and a fully automatic method that uses the VQA system's attention values. Finally, we test the user's mental model by asking them to predict the model's performance on a test counterfactual image. We note an overall improvement in users' accuracy to predict answer change when shown counterfactual explanations. While realistic retrieved counterfactuals obviously are the most effective at improving the mental model, we show that a generative approach can also be equally effective.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.47","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137648971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural response time analysis: Explainable artificial intelligence using only a stopwatch 神经反应时间分析:可解释的人工智能只用一个秒表

Applied AI letters Pub Date : 2021-11-06 DOI: 10.1002/ail2.48

J. Eric T. Taylor, Shashank Shekhar, Graham W. Taylor

{"title":"Neural response time analysis: Explainable artificial intelligence using only a stopwatch","authors":"J. Eric T. Taylor, Shashank Shekhar, Graham W. Taylor","doi":"10.1002/ail2.48","DOIUrl":"10.1002/ail2.48","url":null,"abstract":"How would you describe the features that a deep learning model composes if you were restricted to measuring observable behaviours? Explainable artificial intelligence (XAI) methods rely on privileged access to model architecture and parameters that is not always feasible for most users, practitioners and regulators. Inspired by cognitive psychology research on humans, we present a case for measuring response times (RTs) of a forward pass using only the system clock as a technique for XAI. Our method applies to the growing class of models that use input-adaptive dynamic inference and we also extend our approach to standard models that are converted to dynamic inference post hoc. The experimental logic is simple: If the researcher can contrive a stimulus set where variability among input features is tightly controlled, differences in RT for those inputs can be attributed to the way the model composes those features. First, we show that RT is sensitive to difficult, complex features by comparing RTs from ObjectNet and ImageNet. Next, we make specific a priori predictions about RT for abstract features present in the SCEGRAM data set, where object recognition in humans depends on complex intrascene object-object relationships. Finally, we show that RT profiles bear specificity for class identity and therefore the features that define classes. These results cast light on the model's feature space without opening the black box.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.48","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47752665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Measuring and characterizing generalization in deep reinforcement learning 深度强化学习中泛化的测量和表征

Applied AI letters Pub Date : 2021-11-05 DOI: 10.1002/ail2.45

Sam Witty, Jun K. Lee, Emma Tosch, Akanksha Atrey, Kaleigh Clary, Michael L. Littman, David Jensen

{"title":"Measuring and characterizing generalization in deep reinforcement learning","authors":"Sam Witty, Jun K. Lee, Emma Tosch, Akanksha Atrey, Kaleigh Clary, Michael L. Littman, David Jensen","doi":"10.1002/ail2.45","DOIUrl":"10.1002/ail2.45","url":null,"abstract":"Deep reinforcement learning (RL) methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has constructed a generalized representation that supports insightful action decisions. We re-examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on-policy, off-policy, and unreachable states. We propose a set of practical methods for evaluating agents with these definitions of generalization. We demonstrate these techniques on a common benchmark task for deep RL, and we show that the learned networks make poor decisions for states that differ only slightly from on-policy states, even though those states are not selected adversarially. We focus our analyses on the deep Q-networks (DQNs) that kicked off the modern era of deep RL. Taken together, these results call into question the extent to which DQNs learn generalized representations, and suggest that more experimentation and analysis is necessary before claims of representation learning can be supported.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.45","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75079317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 46

Remembering for the right reasons: Explanations reduce catastrophic forgetting 记住正确的原因:解释可以减少灾难性的遗忘

Applied AI letters Pub Date : 2021-11-05 DOI: 10.1002/ail2.44

Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, Trevor Darrell

{"title":"Remembering for the right reasons: Explanations reduce catastrophic forgetting","authors":"Sayna Ebrahimi, Suzanne Petryk, Akash Gokul, William Gan, Joseph E. Gonzalez, Marcus Rohrbach, Trevor Darrell","doi":"10.1002/ail2.44","DOIUrl":"https://doi.org/10.1002/ail2.44","url":null,"abstract":"The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has “the right reasons” for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.44","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137488003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Patching interpretable And-Or-Graph knowledge representation using augmented reality 使用增强现实修补可解释的And-Or-Graph知识表示

Applied AI letters Pub Date : 2021-10-20 DOI: 10.1002/ail2.43

Hangxin Liu, Yixin Zhu, Song-Chun Zhu

{"title":"Patching interpretable And-Or-Graph knowledge representation using augmented reality","authors":"Hangxin Liu, Yixin Zhu, Song-Chun Zhu","doi":"10.1002/ail2.43","DOIUrl":"10.1002/ail2.43","url":null,"abstract":"We present a novel augmented reality (AR) interface to provide effective means to diagnose a robot's erroneous behaviors, endow it with new skills, and patch its knowledge structure represented by an And-Or-Graph (AOG). Specifically, an AOG representation of opening medicine bottles is learned from human demonstration and yields a hierarchical structure that captures the spatiotemporal compositional nature of the given task, which is highly interpretable for the users. Through a series of psychological experiments, we demonstrate that the explanations of a robotic system, inherited from and produced by the AOG, can better foster human trust compared to other forms of explanations. Moreover, by visualizing the knowledge structure and robot states, the AR interface allows human users to intuitively understand what the robot knows, supervise the robot's task planner, and interactively teach the robot with new actions. Together, users can quickly identify the reasons for failures and conveniently patch the current knowledge structure to prevent future errors. This capability demonstrates the interpretability of our knowledge representation and the new forms of interactions afforded by the proposed AR interface.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.43","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46548240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Explainable, interactive content-based image retrieval 可解释的，交互式的基于内容的图像检索

Applied AI letters Pub Date : 2021-10-19 DOI: 10.1002/ail2.41

Bhavan Vasu, Brian Hu, Bo Dong, Roddy Collins, Anthony Hoogs

{"title":"Explainable, interactive content-based image retrieval","authors":"Bhavan Vasu, Brian Hu, Bo Dong, Roddy Collins, Anthony Hoogs","doi":"10.1002/ail2.41","DOIUrl":"10.1002/ail2.41","url":null,"abstract":"Quantifying the value of explanations in a human-in-the-loop (HITL) system is difficult. Previous methods either measure explanation-specific values that do not correspond to user tasks and needs or poll users on how useful they find the explanations to be. In this work, we quantify how much explanations help the user through a utility-based paradigm that measures change in task performance when using explanations vs not. Our chosen task is content-based image retrieval (CBIR), which has well-established baselines and performance metrics independent of explainability. We extend an existing HITL image retrieval system that incorporates user feedback with similarity-based saliency maps (SBSM) that indicate to the user which parts of the retrieved images are most similar to the query image. The system helps the user understand what it is paying attention to through saliency maps, and the user helps the system understand their goal through saliency-guided relevance feedback. Using the MS-COCO dataset, a standard object detection and segmentation dataset, we conducted extensive, crowd-sourced experiments validating that SBSM improves interactive image retrieval. Although the performance increase is modest in the general case, in more difficult cases such as cluttered scenes, using explanations yields an 6.5% increase in accuracy. To the best of our knowledge, this is the first large-scale user study showing that visual saliency map explanations improve performance on a real-world, interactive task. Our utility-based evaluation paradigm is general and potentially applicable to any task for which explainability can be incorporated.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.41","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"102959774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

User-guided global explanations for deep image recognition: A user study 深度图像识别的用户导向全局解释:用户研究

Applied AI letters Pub Date : 2021-10-19 DOI: 10.1002/ail2.42

Mandana Hamidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, Prasad Tadepalli

引用次数: 0