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Found 18 papers, 1 papers with code
An Association Test Based on Kernel-Based Neural Networks for Complex Genetic Association Analysis
The advent of artificial intelligence, especially the progress of deep neural networks, is expected to revolutionize genetic research and offer unprecedented potential to decode the complex relationships between genetic variants and disease phenotypes, which could mark a significant step toward improving our understanding of the disease etiology.
A Kernel-Based Neural Network Test for High-dimensional Sequencing Data Analysis
While DNN plays a central role in modern AI technology, it has been rarely used in sequencing data analysis due to challenges brought by high-dimensional sequencing data (e. g., overfitting).
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning
In the conducted experiments, the PoFLSC consensus supported the subchain manager to be aware of reservation priority and the core partition of contributors to establish and maintain a competitive subchain.
A Sieve Quasi-likelihood Ratio Test for Neural Networks with Applications to Genetic Association Studies
Neural networks (NN) play a central role in modern Artificial intelligence (AI) technology and has been successfully used in areas such as natural language processing and image recognition.
A Neural Network Based Method with Transfer Learning for Genetic Data Analysis
Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing.
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation
Accurately segmenting temporal frames of cine magnetic resonance imaging (MRI) is a crucial step in various real-time MRI guided cardiac interventions.
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle.
Quantization of Deep Neural Networks for Accurate Edge Computing
Deep neural networks (DNNs) have demonstrated their great potential in recent years, exceeding the per-formance of human experts in a wide range of applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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FGNAS: FPGA-Aware Graph Neural Architecture Search
The success of gragh neural networks (GNNs) in the past years has aroused grow-ing interest and effort in designing best models to handle graph-structured data.
Robust and flexible learning of a high-dimensional classification rule using auxiliary outcomes
The proposed method includes an MTL step using all outcomes to gain efficiency, and a subsequent calibration step using only the outcome of interest to correct both types of biases.
Expectile Neural Networks for Genetic Data Analysis of Complex Diseases
Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes and can be used to discover genetic variants predisposing to sub-populations (e. g., high-risk groups).
NASS: Optimizing Secure Inference via Neural Architecture Search
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests.
On Neural Architecture Search for Resource-Constrained Hardware Platforms
With 30, 000 LUTs, a light-weight design is found to achieve 82. 98\% accuracy and 1293 images/second throughput, compared to which, under the same constraints, the traditional method even fails to find a valid solution.
Grand Challenge of 106-Point Facial Landmark Localization
However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
Unlike existing litera- ture on quantization which primarily targets memory and computation complexity reduction, we apply quan- tization as a method to reduce over tting in FCNs for better accuracy.
Generalized Similarity U: A Non-parametric Test of Association Based on Similarity
Based on theoretical analysis, we proposed to use Laplacian kernel based similarity for GSU to boost power and enhance robustness.
Robust Large Gap Quantum Spin Hall Insulators in Methyl-functionalized III-Bi Buckled Honeycombs
A large bulk band gap is critical for the applications of quantum spin hall (QSH) insulators in spintronics at room temperature.
Materials Science
Trees Assembling Mann Whitney Approach for Detecting Genome-wide Joint Association among Low Marginal Effect loci
Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood ratio based Mann whitney approach (LRMW) when the underlying complex disease involves multiple LME loci and their interactions.
