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Found 7 papers, 4 papers with code
Few-Shot Image Classification Along Sparse Graphs
Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data.
Pooling by Sliced-Wasserstein Embedding
Learning representations from sets has become increasingly important with many applications in point cloud processing, graph learning, image/video recognition, and object detection.
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings
An increasing number of machine learning tasks deal with learning representations from set-structured data.
Wasserstein Embedding for Graph Learning
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks.
Ranked #3 on
Graph Classification
on RE-M5K
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules.
Sliced Wasserstein Distance for Learning Gaussian Mixture Models
In contrast to the KL-divergence, the energy landscape for the sliced-Wasserstein distance is more well-behaved and therefore more suitable for a stochastic gradient descent scheme to obtain the optimal GMM parameters.
