Zobrazeno 1 - 10
of 70
pro vyhledávání: '"CHEN Danlu"'
Publikováno v:
Dizhi lixue xuebao, Vol 29, Iss 2, Pp 202-219 (2023)
This study aims to evaluate landslide susceptibility and explain the internal mechanism of gentle hill-valley through SHAP partial interpretation and PDP partial dependency map based on the random forest-recursive feature elimination model to provide
Externí odkaz:
https://doaj.org/article/03d07570054041b1b699cb88fb4a67fc
Publikováno v:
ACL 2024, long paper
Standard natural language processing (NLP) pipelines operate on symbolic representations of language, which typically consist of sequences of discrete tokens. However, creating an analogous representation for ancient logographic writing systems is an
Externí odkaz:
http://arxiv.org/abs/2408.04628
Autor:
Xiao, Chenghao, Huang, Zhuoxu, Chen, Danlu, Hudson, G Thomas, Li, Yizhi, Duan, Haoran, Lin, Chenghua, Fu, Jie, Han, Jungong, Moubayed, Noura Al
Pretrained language models are long known to be subpar in capturing sentence and document-level semantics. Though heavily investigated, transferring perturbation-based methods from unsupervised visual representation learning to NLP remains an unsolve
Externí odkaz:
http://arxiv.org/abs/2402.08183
In this paper, we introduce the concept of crossed module for Hom-Leibniz-Rinehart algebras. We study the cohomology and extension theory of Hom-Leibniz-Rinehart algebras. It is proved that there is one-to-one correspondence between equivalence class
Externí odkaz:
http://arxiv.org/abs/2202.09224
Publikováno v:
In Journal of Hazardous Materials 5 August 2024 474
Automatic evaluation of text generation tasks (e.g. machine translation, text summarization, image captioning and video description) usually relies heavily on task-specific metrics, such as BLEU and ROUGE. They, however, are abstract numbers and are
Externí odkaz:
http://arxiv.org/abs/1909.05424
Deep learning based approaches have achieved significant progresses in different tasks like classification, detection, segmentation, and so on. Ensemble learning is widely known to further improve performance by combining multiple complementary model
Externí odkaz:
http://arxiv.org/abs/1905.04641
Akademický článek
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Autor:
Pleiss, Geoff, Chen, Danlu, Huang, Gao, Li, Tongcheng, van der Maaten, Laurens, Weinberger, Kilian Q.
The DenseNet architecture is highly computationally efficient as a result of feature reuse. However, a naive DenseNet implementation can require a significant amount of GPU memory: If not properly managed, pre-activation batch normalization and conti
Externí odkaz:
http://arxiv.org/abs/1707.06990
Autor:
Huang, Gao, Chen, Danlu, Li, Tianhong, Wu, Felix, van der Maaten, Laurens, Weinberger, Kilian Q.
In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction for a test example is progressively updated, facilitating the output o
Externí odkaz:
http://arxiv.org/abs/1703.09844