Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Jicong Fan"'
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 809-827 (2024)
Single-cell RNA sequencing (scRNA-seq) technology measures the expression of thousands of genes at the cellular level. Analyzing single-cell transcriptome allows the identification of heterogeneous cell groups, cellular-level regulations, and the tra
Externí odkaz:
https://doaj.org/article/ec860bb65c1541548b9e2b667e537c06
Publikováno v:
Frontiers in Psychology, Vol 13 (2022)
Emotion regulation is essential for healthy living. Previous studies have found that mental training such as compassion meditation could help with emotion regulation. However, the underlying neural mechanism and possible intervention strategies of gr
Externí odkaz:
https://doaj.org/article/243630c3d89f4bedbd668bb3bd1ed7f8
Publikováno v:
Knowledge and Information Systems. 65:1287-1315
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d918a703a286d339608288db3c22be22
https://doi.org/10.1007/s10115-022-01795-1
https://doi.org/10.1007/s10115-022-01795-1
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Jan2024, Vol. 18 Issue 1, p1-22, 22p
Publikováno v:
IEEE Transactions on Industrial Informatics. 18:4477-4487
Missing data widely exist in industrial processes and lead to difficulties in modelling, monitoring, fault diagnosis, and control. In this paper, we propose a nonlinear method to handle the missing data problem in the offline modelling stage or/and t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65b23d83fb20aa1be2a6dd06463086ac
https://doi.org/10.1109/tii.2021.3119377
https://doi.org/10.1109/tii.2021.3119377
Autor:
Jicong Fan
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:6587-6596
Classical matrix completion methods focus on data with stationary latent structure and hence are not effective in missing value imputation when the latent structure changes with time. This paper proposes a dynamic nonlinear matrix completion (D-NLMC)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3233080c73313adda8590e4d6c0f10c1
https://doi.org/10.1609/aaai.v36i6.20612
https://doi.org/10.1609/aaai.v36i6.20612
An 8-Bit in Resistive Memory Computing Core With Regulated Passive Neuron and Bitline Weight Mapping
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 30:379-391
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::486b92a7dc69ff289818d08fcf0e255b
https://doi.org/10.1109/tvlsi.2022.3140395
https://doi.org/10.1109/tvlsi.2022.3140395
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-13
The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the tensor in a frequency domain, has been found useful in solving low-rank tensor recovery problems. Existing TNN-based methods use either fixed or data-independ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac41d05695d981017ce7dab065eb3aa5
https://doi.org/10.1109/tnnls.2022.3215974
https://doi.org/10.1109/tnnls.2022.3215974
Single-Cell RNA sequencing (scRNA-seq) technology measures the expression of thousands of genes at the cellular level. Analyzing single cell transcriptome allows the identification of heterogeneous cell groups, cellular-level regulations, and the tra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e065336275998903ebd63e8d2e37f06
https://doi.org/10.1101/2023.01.15.524109
https://doi.org/10.1101/2023.01.15.524109
Post-training quantization (PTQ) can reduce the memory footprint and latency for deep model inference, while still preserving the accuracy of the model, with only a small unlabeled calibration set and without the retraining on full training set. To c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f134d74dbd599d7b4ba67b11bd1b3590
https://doi.org/10.36227/techrxiv.21456291
https://doi.org/10.36227/techrxiv.21456291