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Found 5 papers, 2 papers with code
Single-level Adversarial Data Synthesis based on Neural Tangent Kernels
In this paper, we propose a new generative model called the generative adversarial NTK (GA-NTK) that has a single-level objective.
Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structures
We study the problem of detecting critical structures using a graph embedding model.
Ego-CNN: An Ego Network-based Representation of Graphs Detecting Critical Structures
While existing graph embedding models can generate useful embedding vectors that perform well on graph-related tasks, what valuable information can be jointly learned by a graph embedding model is less discussed.
SIC-GAN: A Self-Improving Collaborative GAN for Decoding Sketch RNNs
We propose the Self-Improving Collaborative GAN (SIC-GAN), where there are two generators (variational RNNs) collaborating with each other to output a sequence and aiming to trick the discriminator into believing the sequence is of good quality.
Learning User Perceived Clusters with Feature-Level Supervision
We show that this kind of supervision can be easily obtained in the form of perception vectors in many applications.
