铺子
发表于 2025-3-23 12:37:47
,Bayesian Tensor CPD: Performance and Real-World Applications,.e., the variance of the rank-1 component coefficient) learned through MGP, the inference algorithm is capable of learning low tensor rank, but it has the tendency to underestimate the tensor rank when the ground-truth rank is high, making it not very flexible in the high-rank regime. Numerical resu
Nostalgia
发表于 2025-3-23 17:46:18
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COLON
发表于 2025-3-23 19:56:57
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很像弓]
发表于 2025-3-24 01:02:31
Bayesian Tensor Decomposition for Signal Processing and Machine Learning978-3-031-22438-6
笨拙处理
发表于 2025-3-24 03:14:07
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Excitotoxin
发表于 2025-3-24 09:47:39
https://doi.org/10.1007/978-3-319-20612-7.e., the variance of the rank-1 component coefficient) learned through MGP, the inference algorithm is capable of learning low tensor rank, but it has the tendency to underestimate the tensor rank when the ground-truth rank is high, making it not very flexible in the high-rank regime. Numerical resu
Neuralgia
发表于 2025-3-24 12:00:18
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精致
发表于 2025-3-24 16:50:08
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复习
发表于 2025-3-24 19:43:24
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caldron
发表于 2025-3-25 03:07:09
Yuki Hayashi,Yuji Ogawa,Yukiko I. Nakanolection, are highlighted. These merits shed light on the design of sparsity-promoting prior for automating the model pruning in recent machine learning models, including deep neural networks, Gaussian processes, and tensor decompositions. Then, we introduce the variational inference framework for al