Search Results for author:
Found 7 papers, 4 papers with code
Carbon-Efficient Neural Architecture Search
This work presents a novel approach to neural architecture search (NAS) that aims to reduce energy costs and increase carbon efficiency during the model design process.
Multi-objective Optimization by Learning Space Partitions
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Multi-objective Optimization by Learning Space Partition
In this paper, we propose LaMOO, a novel multi-objective optimizer that learns a model from observed samples to partition the search space and then focus on promising regions that are likely to contain a subset of the Pareto frontier.
Few-shot Neural Architecture Search
supernet, to approximate the performance of every architecture in the search space via weight-sharing.
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)
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.
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance.
