{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:37:29Z","timestamp":1730230649212,"version":"3.28.0"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,14]]},"DOI":"10.1109\/icassp48485.2024.10446778","type":"proceedings-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T18:56:31Z","timestamp":1710788191000},"page":"6210-6214","source":"Crossref","is-referenced-by-count":0,"title":["FW-Shapley: Real-Time Estimation of Weighted Shapley Values"],"prefix":"10.1109","author":[{"given":"Pranoy","family":"Panda","sequence":"first","affiliation":[{"name":"Fujitsu Research,India"}]},{"given":"Siddharth","family":"Tandon","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology,Hyderabad"}]},{"given":"Vineeth N","family":"Balasubramanian","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology,Hyderabad"}]}],"member":"263","reference":[{"article-title":"A value for n-person games. contributions to the theory of games","year":"1953","author":"Shapely","key":"ref1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3321\/j.issn:0529-6579.2007.z1.029"},{"key":"ref3","first-page":"9259","article-title":"The shapley taylor interaction index","volume-title":"International conference on machine learning","author":"Sundararajan"},{"key":"ref4","first-page":"3457","article-title":"Improving kernelshap: Practical shapley value estimation using linear regression","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Covert"},{"key":"ref5","first-page":"5922","article-title":"Neuron shapley: Discovering the responsible neurons","volume":"33","author":"Ghorbani","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref6","first-page":"793","article-title":"Efficient computation and analysis of distributional shapley values","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Kwon"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482341"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482302"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05297-5"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.223"},{"article-title":"Beta shapley: a unified and noise-reduced data valuation framework for machine learning","year":"2021","author":"Kwon","key":"ref11"},{"key":"ref12","first-page":"34363","article-title":"Weightedshap: analyzing and improving shapley based feature attributions","volume":"35","author":"Kwon","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Fastshap: Real-time shapley value estimation","volume-title":"International Conference on Learning Representations","author":"Jethani","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-009-3677-5_7"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"article-title":"Shapley explainability on the data manifold","year":"2021","author":"Frye","key":"ref16"},{"key":"ref17","first-page":"1459","article-title":"Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Jethani"},{"issue":"1","key":"ref18","first-page":"9477","article-title":"Explaining by removing: A unified framework for model explanation","volume":"22","author":"Covert","year":"2021","journal-title":"The Journal of Machine Learning Research"},{"journal-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref19"},{"article-title":"Reading digits in natural images with unsupervised feature learning","year":"2011","author":"Netzer","key":"ref20"},{"article-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"Xiao","key":"ref21"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref23","first-page":"3319","article-title":"Axiomatic attribution for deep networks","volume-title":"Proceedings of the 34th International Conference on Machine Learning","volume":"70","author":"Sundararajan"},{"article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","year":"2013","author":"Simonyan","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342637"},{"key":"ref26","first-page":"2242","article-title":"Data shapley: Equitable valuation of data for machine learning","volume-title":"International conference on machine learning","author":"Ghorbani"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00814"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Captum: A unified and generic model interpretability library for pytorch","year":"2020","author":"Kokhlikyan","key":"ref29"},{"article-title":"Rise: Randomized input sampling for explanation of black-box models","year":"2018","author":"Petsiuk","key":"ref30"},{"key":"ref31","article-title":"A benchmark for interpretability methods in deep neural networks","volume":"32","author":"Hooker","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3806"},{"article-title":"L-shapley and c-shapley: Efficient model interpretation for structured data","year":"2018","author":"Chen","key":"ref33"},{"key":"ref34","first-page":"272","article-title":"Explaining deep neural networks with a polynomial time algorithm for shapley value approximation","volume-title":"International Conference on Machine Learning","author":"Ancona"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"article-title":"Learning to estimate shapley values with vision transformers","year":"2022","author":"Covert","key":"ref36"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-57321-8_6"}],"event":{"name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2024,4,14]]},"location":"Seoul, Korea, Republic of","end":{"date-parts":[[2024,4,19]]}},"container-title":["ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10445798\/10445803\/10446778.pdf?arnumber=10446778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T04:39:53Z","timestamp":1722573593000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10446778\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icassp48485.2024.10446778","relation":{},"subject":[],"published":{"date-parts":[[2024,4,14]]}}}