Search Results for author:
Found 12 papers, 2 papers with code
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning
We allow the effective combination of design experience from different sources, so as to create an effective search space containing a variety of TSF models to support different TSF tasks.
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy
Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource constrained environments.
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model
They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results.
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework
Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and domain knowledge, which is not friendly to common users.
Search For Deep Graph Neural Networks
Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem.
FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures.
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem.
Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network
In our work, we present Auto-CASH, a pre-trained model based on meta-learning, to solve the CASH problem more efficiently.
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
To reduce searching cost, most NAS algorithms use fixed outer network level structure, and search the repeatable cell structure only.
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
However, most machine learning algorithms are sensitive to the hyper-parameters.
ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning
Motivated by this, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations.
Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem
In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems.
