Results 251 to 260 of about 5,161,693 (301)

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Scaling up for end-to-end on-chip photonic neural network inference. [PDF]

Light Sci Appl
Wu B, Huang C, Zhang J, Zhou H, Wang Y, Dong J, Zhang X.   +6 more
europepmc   +1 more source

KEGNI: knowledge graph enhanced framework for gene regulatory network inference. [PDF]

Genome Biol
Li P, Li L, Nan J, Chen J, Sun J, Cao Y.
europepmc   +1 more source

Data driven network inference and longitudinal transcriptomics unveil dynamic regulation in Chronic Lymphocytic Leukaemia models. [PDF]

NPJ Syst Biol Appl
Marku M, Chenel H, Bordenave J, Hurtado M, Domagala M, Raynal F, Poupot M, Ysebaert L, Zinovyev A, Pancaldi V.   +9 more
europepmc   +1 more source

Uncertainty Quantification of Network Inference with Data Sufficiency. [PDF]

IEEE Trans Netw Sci Eng
Singhal B, Ocampo-Espindola JL, Nikhil KL, Herzog ED, Kiss IZ, Li JS.   +5 more
europepmc   +1 more source

CaHoT-GRN: context-aware high-order topology learning for robust single-cell gene regulatory network inference. [PDF]

Brief Bioinform
Yao D, Zhang B, Zhan X, Wang W, Liang N.
europepmc   +1 more source

TRENDY: gene regulatory network inference enhanced by transformer. [PDF]

Bioinformatics
Tian X, Patel Y, Wang Y.
europepmc   +1 more source

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Inference in Bayesian networks

Nature Biotechnology, 2006
Bayesian networks are increasingly important for integrating biological data and for inferring cellular networks and pathways. What are Bayesian networks and how are they used for inference?
Chris J, Needham, James R, Bradford, Andrew J, Bulpitt, David R, Westhead   +3 more
openaire   +2 more sources

Epidemiologic network inference

Statistics and Computing, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Barbillon, Pierre, Schwaller, Loic, Robin, Stéphane, Flachs, Andrew, Stone, Glenn Davis   +4 more
openaire   +5 more sources