Search Results for author:
Found 7 papers, 4 papers with code
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation.
iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation.
Ranked #22 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search.
Extension of causal decomposition in the mutual complex dynamic process
Causal decomposition depicts a cause-effect relationship that is not based on the concept of prediction, but based on the phase dependence of time series.
Efficient Novelty-Driven Neural Architecture Search
The best architecture obtained by our algorithm with the same search space achieves the state-of-the-art test error rate of 2. 51\% on CIFAR-10 with only 7. 5 hours search time in a single GPU, and a validation perplexity of 60. 02 and a test perplexity of 57. 36 on PTB.
High Dimensional Bayesian Optimization via Supervised Dimension Reduction
Furthermore, a kernel trick is developed to reduce computational complexity and learn nonlinear subset of the unknowing function when applying SIR to extremely high dimensional BO.
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization
In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model.
