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Found 7 papers, 2 papers with code
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs.
Ranked #2 on
Node Classification
on Cornell
Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network?
With the rapid development of GPU (Graphics Processing Unit) technologies and neural networks, we can explore more appropriate data structures and algorithms.
MixBCT: Towards Self-Adapting Backward-Compatible Training
As a solution, backward-compatible training can be employed to avoid the necessity of updating old retrieval datasets.
CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning
Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students.
Key frames assisted hybrid encoding for photorealistic compressive video sensing
Snapshot compressive imaging (SCI) encodes high-speed scene video into a snapshot measurement and then computationally makes reconstructions, allowing for efficient high-dimensional data acquisition.
Visualizing the Finer Cluster Structure of Large-Scale and High-Dimensional Data
Dimension reduction and visualization of high-dimensional data have become very important research topics because of the rapid growth of large databases in data science.
Adaptive Generation of Phantom Limbs Using Visible Hierarchical Autoencoders
This paper proposed a hierarchical visible autoencoder in the adaptive phantom limbs generation according to the kinetic behavior of functional body-parts, which are measured by heterogeneous kinetic sensors.
