Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang, Yingji"'
Autor:
Zhang, Yingjie
A rapid and accurate method to determine or predict cattle diet quality is essential to effectively manage free-ranging cattle production. One popular tool currently available for predicting cattle diet quality is fecal Near Infrared Reflectance Spec
Externí odkaz:
http://hdl.handle.net/1969.1/ETD-TAMU-3061
Autor:
Zhang, Yingjie
ESR quantum computing presents faster means to perform gates on nuclear spins than the traditional NMR methods. This means ESR is a test-bed that can potentially be useful in ways that are not possible with NMR. The first step is to demonstrate unive
Externí odkaz:
http://hdl.handle.net/10012/5572
Autor:
Zhang, Yingjie
CCAAT/Enhancer Binding Protein Delta (C/EBPD) gene transcription is highly induced in G0 growth arrested mammary epithelial cells and “loss of function” alterations in C/EBPD have been reported in human breast cancer. This work investigated three
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1157134598
Autor:
Zhang, Yingjie
Nontypeable Haemophilus influenzae (NTHi) is one of the major causes of otitis media in children. The molecular basis of NTHi pathogenesis is however poorly understood. We have developed a signature-tagged mutagenesis (STM) strategy to identify NTHi
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1407950104
Achieving precise semantic control over the latent spaces of Variational AutoEncoders (VAEs) holds significant value for downstream tasks in NLP as the underlying generative mechanisms could be better localised, explained and improved upon. Recent re
Externí odkaz:
http://arxiv.org/abs/2402.00723
Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces
Externí odkaz:
http://arxiv.org/abs/2312.13208
The injection of syntactic information in Variational AutoEncoders (VAEs) has been shown to result in an overall improvement of performances and generalisation. An effective strategy to achieve such a goal is to separate the encoding of distributiona
Externí odkaz:
http://arxiv.org/abs/2311.08579
Explainable natural language inference aims to provide a mechanism to produce explanatory (abductive) inference chains which ground claims to their supporting premises. A recent corpus called EntailmentBank strives to advance this task by explaining
Externí odkaz:
http://arxiv.org/abs/2308.03581
Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and more controllable data generation. While this has been well investigated in Computer Vision, in tasks such as
Externí odkaz:
http://arxiv.org/abs/2305.01713
Formal/symbolic semantics can provide canonical, rigid controllability and interpretability to sentence representations due to their \textit{localisation} or \textit{composition} property. How can we deliver such property to the current distributiona
Externí odkaz:
http://arxiv.org/abs/2210.06230