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.
In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, with taking the multiple dynamic workloads into consideration.
Recently, some Neural Architecture Search (NAS) techniques are proposed for the automatic design of Graph Convolutional Network (GCN) architectures.
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem.
To solve this problem, in this paper, we propose a general framework, named EM-RBR(embedding and rule-based reasoning), capable of combining the advantages of reasoning based on rules and the state-of-the-art models of embedding.
In our work, we present Auto-CASH, a pre-trained model based on meta-learning, to solve the CASH problem more efficiently.
To reduce searching cost, most NAS algorithms use fixed outer network level structure, and search the repeatable cell structure only.
In this demo, we present ConsciousControlFlow(CCF), a prototype system to demonstrate conscious Artificial Intelligence (AI).
However, most machine learning algorithms are sensitive to the hyper-parameters.
Motivated by this, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations.
In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems.
In industrial data analytics, one of the fundamental problems is to utilize the temporal correlation of the industrial data to make timely predictions in the production process, such as fault prediction and yield prediction.
In this paper, we focus on online methods for AR-model-based time series prediction with missing values.
Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications.
Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets.
Data quality issues have attracted widespread attention due to the negative impacts of dirty data on data mining and machine learning results.
In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task.