Search Results for author:
Found 9 papers, 7 papers with code
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
If the nonlinear partition function and the local model fits well with ground-truth black-box function, then good partitions and candidates can be reached with much fewer samples.
Few-shot Neural Architecture Search
supernet, to approximate the performance of every architecture in the search space via weight-sharing.
Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider
Function as a Service (FaaS) has been gaining popularity as a way to deploy computations to serverless backends in the cloud.
Distributed, Parallel, and Cluster Computing
Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal.
Sample-Efficient Neural Architecture Search by Learning Action Space
To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.
Ranked #7 on
Neural Architecture Search
on CIFAR-10 Image Classification
(Params metric)
Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search
To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.
Ranked #15 on
Image Classification
on CIFAR-10
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.
A-Tree: A Bounded Approximate Index Structure
At the core of our index is a tunable error parameter that allows a DBA to balance lookup performance and space consumption.
Databases
