Results 181 to 190 of about 6,914 (235)

Simultaneous Bilateral Pediatric Nephrectomies: Indications, Approach and Outcomes Over a 15-Year Period. [PDF]

Pediatr Transplant
Lombardo AM, Stout M, Zann A, McLeod D, Alpert S, Jayanthi VR, DaJusta D, Ching CB.   +7 more
europepmc   +1 more source

Advancing cardiovascular disease diagnosis with an interpretable and responsible AI framework. [PDF]

Sci Rep
Hasan KS, Dhrubo IS.
europepmc   +1 more source

Uncertainty-Aware Parking Prediction Using Bayesian Neural Networks. [PDF]

Sensors (Basel)
Nezhadettehad A, Zaslavsky A, Rakib A, Loke SW.   +3 more
europepmc   +1 more source

Beyond Accuracy: Enhancing Parkinson's Diagnosis with Uncertainty Quantification of Machine Learning Models. [PDF]

Artif Intell Healthc (2025)
Azad A, Islam MS, Hoque E, Rahman MS.
europepmc   +1 more source

"Getting to diagnosis was an absolute nightmare": survey insights about the lived experience of spinal CSF leak in Australia and Aotearoa New Zealand. [PDF]

J Neurol
Knight LSW, Smith RL, Britten-Jones AC, John SE, Grayden DB, Bui BV, Ayton LN, Nguyen BN.   +7 more
europepmc   +1 more source

FPGA Architecture Enhancements for Efficient BNN Implementation [PDF]

2018 International Conference on Field-Programmable Technology (FPT), 2018
Binarized neural networks (BNNs) are ultra-reduced precision neural networks, having weights and activations restricted to single-bit values. BNN computations operate on bitwise data, making them particularly amenable to hardware implementation. In this paper, we first analyze BNN implementations on contemporary commercial 20nm FPGAs.
Jin Hee Kim, Jongeun Lee, Jason Helge Anderson   +2 more
openaire   +2 more sources

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LP-BNN: Ultra-low-Latency BNN Inference with Layer Parallelism

2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019
High inference latency seriously limits the deployment of DNNs in real-time domains such as autonomous driving, robotic control, and many others. To address this emerging challenge, researchers have proposed approximate DNNs with reduced precision, e.g., Binarized Neural Networks (BNNs).
Tong Geng, Chunshu Wu, Martin Herbordt   +2 more
exaly   +2 more sources

A high-throughput scalable BNN accelerator with fully pipelined architecture

CCF Transactions on High Performance Computing, 2021
By replacing multiplication with XNOR operation, Binarized Neural Networks (BNN) are hardware-friendly and extremely suitable for FPGA acceleration. Previous researches highlighted the potential exploitation of BNNs performance. However, most of the present researches targeted at minimizing chip areas.
Zhe Han, Jingfei Jiang, Jinwei Xu
exaly   +2 more sources

BNN Training Algorithm with Ternary Gradients and BNN based on MRAM Array

TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), 2023
T Kawahara
exaly   +2 more sources