Search Results for author:
Found 11 papers, 6 papers with code
TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
The best neural architecture for a given machine learning problem depends on many factors: not only the complexity and structure of the dataset, but also on resource constraints including latency, compute, energy consumption, etc.
PyGlove: Symbolic Programming for Automated Machine Learning
As a result, AutoML can be reformulated as an automated process of symbolic manipulation.
Multi-path Neural Networks for On-device Multi-domain Visual Classification
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
Discovering Multi-Hardware Mobile Models via Architecture Search
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored.
Can weight sharing outperform random architecture search? An investigation with TuNAS
Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, we obtain a family of object detection models, MobileDets, that achieve state-of-the-art results across mobile accelerators.
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
Without extra retraining or post-processing steps, we are able to train a single set of shared weights on ImageNet and use these weights to obtain child models whose sizes range from 200 to 1000 MFLOPs.
Ranked #30 on
Neural Architecture Search
on ImageNet
Neural Predictor for Neural Architecture Search
First we train N random architectures to generate N (architecture, validation accuracy) pairs and use them to train a regression model that predicts accuracy based on the architecture.
Scaling Up Neural Architecture Search with Big Single-Stage Models
In this work, we propose BigNAS, an approach that simplifies this workflow and scales up neural architecture search to target a wide range of model sizes simultaneously.
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks.
Ranked #773 on
Image Classification
on ImageNet
Understanding and Simplifying One-Shot Architecture Search
There is growing interest in automating neural network architecture design.
