dCGPANN encodes neural nets so evolutionary operators can rewire, prune, adapt activations and add skips while gradient descent tunes parameters, yielding smaller networks with lower regression error in fixed time.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2019 3verdicts
UNVERDICTED 3representative citing papers
A framework learns collection embeddings from runway images and applies RNN/LSTM to predict next-season designs at 78.42% average AUC over 32 years of data.
The paper proposes the Aesthetic Multi-Attribute Network (AMAN) that jointly predicts captions and scores for five aesthetic attributes using a new weakly-labeled dataset created via knowledge transfer.
citing papers explorer
-
Neural Network Architecture Search with Differentiable Cartesian Genetic Programming for Regression
dCGPANN encodes neural nets so evolutionary operators can rewire, prune, adapt activations and add skips while gradient descent tunes parameters, yielding smaller networks with lower regression error in fixed time.
-
Predicting Next-Season Designs on High Fashion Runway
A framework learns collection embeddings from runway images and applies RNN/LSTM to predict next-season designs at 78.42% average AUC over 32 years of data.
-
Aesthetic Attributes Assessment of Images
The paper proposes the Aesthetic Multi-Attribute Network (AMAN) that jointly predicts captions and scores for five aesthetic attributes using a new weakly-labeled dataset created via knowledge transfer.