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Found 9 papers, 2 papers with code
Bootstrap The Original Latent: Learning a Private Model from a Black-box Model
In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users to better train their private models via the guidance of the back-propagated results of a Black-box foundation/source model.
Testability-Aware Low Power Controller Design with Evolutionary Learning
The proposed GA-guided XORNets also allows reducing the number of control bits, and the total testing time decreases by 20. 78% on average and up to 47. 09% compared to the existing design without sacrificing test coverage.
Image Magnification Network for Vessel Segmentation in OCTA Images
Optical coherence tomography angiography (OCTA) is a novel non-invasive imaging modality that allows micron-level resolution to visualize the retinal microvasculature.
UCNN: A Convolutional Strategy on Unstructured Mesh
In order to overcome the limitations of FNN and CNN, the unstructured convolutional neural network (UCNN) is proposed, which aggregates and effectively exploits the features of neighbour nodes through the weight function.
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling.
Machine Learning of Partial Differential Equations from Noise Data
Machine learning of partial differential equations from data is a potential breakthrough to solve the lack of physical equations in complex dynamic systems, but because numerical differentiation is ill-posed to noise data, noise has become the biggest obstacle in the application of partial differential equation identification method.
Layer Pruning for Accelerating Very Deep Neural Networks
In this paper, we propose an adaptive pruning method.
Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation
Our method is built as an end-to-end framework for segmentation and classification.
FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic Segmentation of Brain Anatomy
Developed from the seminal FCN with an end-to-end learning-based approach and constructed by newly designed dense blocks including a dense fully-connected layer, the proposed FD-FCN is different from other FCN-based methods and leads to an outperformance in the perspective of both efficiency and accuracy.
