Ood learning
Web31 de out. de 2024 · Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studied a more realistic setting: out-of-distribution (OOD) detection, where test data may come from classes that are unknown during training (i.e., OOD data). WebDetecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. A key challenge of OOD detection is to learn discriminative se-mantic features. Traditional cross-entropy loss only focuses on whether a sample is correctly classified, and does not explicitly distinguish the margins between ...
Ood learning
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WebHá 2 dias · Abstract The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems. Previous methods commonly restrict the region (in feature space) of In-domain (IND) intent features to be compact or simply-connected implicitly, which assumes no OOD intents reside, to learn discriminative semantic features. Web29 de nov. de 2024 · The Purposeful Learning Framework TM is based on 30 years of proven practice. This approach to instructional design is exclusive to O’Donnell Learn. …
Web6 de jun. de 2024 · Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can … WebHá 1 dia · %0 Conference Proceedings %T Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery %A Mou, Yutao %A He, Keqing %A Wang, Pei %A Wu, Yanan %A Wang, Jingang %A Wu, Wei %A Xu, Weiran %S Proceedings of the 2024 Conference on Empirical Methods in Natural Language …
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