Results 41 to 50 of about 7,468,732 (318)
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference [PDF]
Symposium on Field Programmable Gate Arrays, 2016 Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values.
Yaman Umuroglu +6 more
semanticscholar +1 more source
Mixed-Signal Computing for Deep Neural Network Inference
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2021 Modern deep neural networks (DNNs) require billions of multiply-accumulate operations per inference. Given that these computations demand relatively low precision, it is feasible to consider analog computing, which can be more efficient than digital in ...
B. Murmann
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Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms [PDF]
, 2010 Motivation :Reconstructing the topology of a gene regulatory network is one of the key tasks in systems biology. Despite of the wide variety of proposed methods, very little work has been dedicated to the assessment of their stability properties. Here we
A Barabasi +46 more
core +10 more sources
Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing [PDF]
IEEE Transactions on Wireless Communications, 2019 As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to
En Li, Liekang Zeng, Zhi Zhou, Xu Chen
semanticscholar +1 more source
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2022 We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication ...
T. Xiao +11 more
semanticscholar +1 more source
Wisdom of crowds for robust gene network inference
Nature Methods, 2012 Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference
D. Marbach +9 more
semanticscholar +1 more source
bioRxiv, 2021 Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells.
S. McCalla +7 more
semanticscholar +1 more source
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference [PDF]
, 2017 We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network.
Han, Xiaoguang +4 more
core +2 more sources
Computational Network Inference for Bacterial Interactomics
mSystems, 2022 Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species.
Katherine James, J. Munoz-Munoz
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Decoupling approximation robustly reconstructs directed dynamical networks
New Journal of Physics, 2018 Methods for reconstructing the topology of complex networks from time-resolved observations of node dynamics are gaining relevance across scientific disciplines.
Nikola Simidjievski +5 more
doaj +1 more source