Search Results for author:
Found 9 papers, 3 papers with code
DAS: Neural Architecture Search via Distinguishing Activation Score
NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer.
Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor
To solve this issue, we propose a Cross-Domain Predictor (CDP), which is trained based on the existing NAS benchmark datasets (e. g., NAS-Bench-101), but can be used to find high-performance architectures in large-scale search spaces.
Architecture Augmentation for Performance Predictor Based on Graph Isomorphism
Performance predictors can greatly alleviate the prohibitive cost of NAS by directly predicting the performance of DNNs.
Evolving Deep Neural Networks for Collaborative Filtering
As a consequence, it requires the designers to develop expertise in both CF and DNNs, which limits the application of deep learning methods in CF and the accuracy of recommended results.
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
The paper conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform.
Homogeneous Architecture Augmentation for Neural Predictor
Specifically, a homogeneous architecture augmentation algorithm is proposed in HAAP to generate sufficient training data taking the use of homogeneous representation.
Transgender Community Sentiment Analysis from Social Media Data: A Natural Language Processing Approach
Transgender community is experiencing a huge disparity in mental health conditions compared with the general population.
A Survey on Evolutionary Neural Architecture Search
Deep Neural Networks (DNNs) have achieved great success in many applications.
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks.
