Results 21 to 30 of about 38,613 (267)
Lifelong Zero-Shot Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Zero-Shot Learning (ZSL) handles the problem that some testing classes never appear in training set. Existing ZSL methods are designed for learning from a fixed training set, which do not have the ability to capture and accumulate the knowledge of multiple training sets, causing them infeasible to many real-world applications. In this paper, we propose
Kun Wei, Cheng Deng 0002, Xu Yang 0019
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Advances in Neural Information Processing Systems 36, 2023 Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it.
Hamed Nilforoshan +7 more
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CoRR, 2021 This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach. Given a new dataset and a well-defined machine learning task, humans begin by reading a description of the dataset and ...
Nikhil Singh 0003 +5 more
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CoRR, 2016 This paper provides a framework to hash images containing instances of unknown object classes. In many object recognition problems, we might have access to huge amount of data. It may so happen that even this huge data doesn't cover the objects belonging to classes that we see in our day to day life.
Shubham Pachori, Shanmuganathan Raman
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Bayesian Zero-Shot Learning [PDF]
, 2020 Object classes that surround us have a natural tendency to emerge at varying levels of abstraction. We propose a Bayesian approach to zero-shot learning (ZSL) that introduces the notion of meta-classes and implements a Bayesian hierarchy around these classes to effectively blend data likelihood with local and global priors.
Badirli, Sarkhan +2 more
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IEEE Transactions on Information Forensics and Security, 2023 Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models. Due to an increasing need for regulatory compliance required for ML applications, machine unlearning is ...
Vikram S. Chundawat +3 more
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CoRR, 2021 Speech Translation (ST) is the task of translating speech in one language into text in another language. Traditional cascaded approaches for ST, using Automatic Speech Recognition (ASR) and Machine Translation (MT) systems, are prone to error propagation.
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Zero-Shot Kernel Learning [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 IEEE Conference on Computer Vision and Pattern Recognition ...
Hongguang Zhang, Piotr Koniusz
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Zero-Shot Visual Imitation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 Oral presentation at ICLR 2018. Website at https://pathak22.github.io/zeroshot-imitation/
Deepak Pathak +9 more
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Active Zero-Shot Learning [PDF]
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016 In multi-label classification in the big data age, the number of classes can be in thousands, and obtaining sufficient training data for each class is infeasible. Zero-shot learning aims at predicting a large number of unseen classes using only labeled data from a small set of classes and external knowledge about class relations. However, previous zero-
Sihong Xie, Shaoxiong Wang, Philip S. Yu
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