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Unified Ferroelectric/Memristive Memory for Neural Network Inference and Training

Vianello E, Martemucci M, Rummens F, Malot Y, Hirtzlin T, Guille O, Martin S, Carabasse C, Vincent A, Saighi S, Grenouillet L, Querlioz D.   +11 more
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Guided Attention Inference Network

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
With only coarse labels, weakly supervised learning typically uses top-down attention maps generated by back-propagating gradients as priors for tasks such as object localization and semantic segmentation. While these attention maps are intuitive and informative explanations of deep neural network, there is no effective mechanism to manipulate the ...
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, Yun Fu   +4 more
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Inferring regulatory networks

Frontiers in Bioscience, 2008
The discovery of regulatory networks is an important aspect in the post genomic research. The process requires integrated efforts of experimental and computational strategies by employing the systems biology approach. This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other ...
Huai, Li, Jianhua, Xuan, Yue, Wang, Ming, Zhan   +3 more
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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
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Biological Network, Gene Regulatory Network Inference Using Causal Inference Approach

Revue d'Intelligence Artificielle, 2022
In system biology inference from gene regulatory network (GRN) is a challenging task. There exist different computational techniques to analyze the causal relationships between the pair of genes and to understand the significance of causal relationship in gene regulatory network.
Saroj Shambharkar, K. Vaishali, Rachna Somkunwar, Yogeshri Choudhari, Jyotsna Gawai   +4 more
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INFERENCE OF GENE REGULATORY NETWORKS USING BOOLEAN-NETWORK INFERENCE METHODS

Journal of Bioinformatics and Computational Biology, 2009
The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks.
Graham J, Hickman, T Charlie, Hodgman
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Learning Layer-Skippable Inference Network

IEEE Transactions on Image Processing, 2020
The process of learning good representations for machine learning tasks can be very computationally expensive. Typically, we facilitate the same backbones learned on the training set to infer the labels of testing data. Interestingly, This learning and inference paradigm, however, is quite different from the typical inference scheme of human biological
Yu-Gang Jiang, Changmao Cheng, Hangyu Lin, Yanwei Fu   +3 more
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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
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Nonmonotonic Inferences and Neural Networks

Synthese, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Inference Networks for Document Retrieval

ACM SIGIR Forum, 1989
The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.
H. Turtle, W. B. Croft
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