Results 281 to 290 of about 7,468,732 (318)
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[DL] A Survey of FPGA-based Neural Network Inference Accelerators
ACM Transactions on Reconfigurable Technology and Systems, 2019Recent research on neural networks has shown a significant advantage in machine learning over traditional algorithms based on handcrafted features and models.
Kaiyuan Guo +4 more
semanticscholar +1 more source
2014
Network graphs are constructed in all sorts of ways and to varying levels of completeness. In some settings, there is little if any uncertainty in assessing whether or not an edge exists between two vertices and we can exhaustively assess incidence between vertex pairs.
Eric D. Kolaczyk, Gábor Csárdi
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Network graphs are constructed in all sorts of ways and to varying levels of completeness. In some settings, there is little if any uncertainty in assessing whether or not an edge exists between two vertices and we can exhaustively assess incidence between vertex pairs.
Eric D. Kolaczyk, Gábor Csárdi
openaire +1 more source
BENIN: Biologically enhanced network inference
Journal of Bioinformatics and Computational Biology, 2020Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data.
Stephanie Kamgnia, Wonkap +1 more
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nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data
IACR Cryptology ePrint Archive, 2019In previous work, Boemer et al. introduced nGraph-HE, an extension to the Intel nGraph deep learning (DL) compiler, that enables data scientists to deploy models with popular frameworks such as TensorFlow and PyTorch with minimal code changes.
Fabian Boemer +3 more
semanticscholar +1 more source
Motif-aware diffusion network inference
International Journal of Data Science and Analytics, 2018Characterizing and understanding information diffusion over social networks play an important role in various real-world applications. In many scenarios, however, only the states of nodes can be observed while the underlying diffusion networks are unknown.
Qi Tan, Yang Liu, Jiming Liu
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IEEE Network, 2019
With the revolution of smart industry, more and more Industrial Internet of Things (IIoT) devices as well as AI algorithms are deployed to achieve industrial intelligence. While applying computation-intensive deep learning on IIoT devices, however, it is
Liekang Zeng, En Li, Zhi Zhou, Xu Chen
semanticscholar +1 more source
With the revolution of smart industry, more and more Industrial Internet of Things (IIoT) devices as well as AI algorithms are deployed to achieve industrial intelligence. While applying computation-intensive deep learning on IIoT devices, however, it is
Liekang Zeng, En Li, Zhi Zhou, Xu Chen
semanticscholar +1 more source
Bayonet: probabilistic inference for networks
Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2018Network operators often need to ensure that important probabilistic properties are met, such as that the probability of network congestion is below a certain threshold. Ensuring such properties is challenging and requires both a suitable language for probabilistic networks and an automated procedure for answering probabilistic inference queries.
Gehr, Timon +5 more
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Faculty Opinions recommendation of SCENIC: single-cell regulatory network inference and clustering.
Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature, 2021Yongjin P. Park
semanticscholar +1 more source
A Programmable Neural-Network Inference Accelerator Based on Scalable In-Memory Computing
IEEE International Solid-State Circuits Conference, 2021semanticscholar +1 more source
2018
Background: A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little ...
Cecchini, Gloria (Dr.) +3 more
openaire +1 more source
Background: A reliable inference of networks from data is of key interest in the Neurosciences. Several methods have been suggested in the literature to reliably determine links in a network. To decide about the presence of links, these techniques rely on statistical inference, typically controlling the number of false positives, paying little ...
Cecchini, Gloria (Dr.) +3 more
openaire +1 more source