Search Results for author:
Found 18 papers, 11 papers with code
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.)
HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs
Based on the generative SSL task, we propose a hypergraph SSL method, HypeBoy.
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
Second, how does A-X dependence affect GNNs?
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities
Self-supervised learning (SSL) is a growing torrent that has recently transformed machine learning and its many real world applications, by learning on massive amounts of unlabeled data via self-generated supervisory signals.
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
DSV captures the alignment between an augmentation function and the anomaly-generating mechanism with surrogate losses, which approximate the discordance and separability of test data, respectively.
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection
The first is a new unsupervised validation loss that quantifies the alignment between the augmented training data and the (unlabeled) test data.
Classification of Edge-dependent Labels of Nodes in Hypergraphs
Interestingly, many real-world systems modeled as hypergraphs contain edge-dependent node labels, i. e., node labels that vary depending on hyperedges.
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
AERO-GNN provably mitigates the proposed problems of deep graph attention, which is further empirically demonstrated with (a) its adaptive and less smooth attention functions and (b) higher performance at deep layers (up to 64).
NetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks?
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself.
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling.
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Given a graph with partial observations of node features, how can we estimate the missing features accurately?
Transition Matrix Representation of Trees with Transposed Convolutions
How can we effectively find the best structures in tree models?
Model-Agnostic Augmentation for Accurate Graph Classification
Then, we propose NodeSam (Node Split and Merge) and SubMix (Subgraph Mix), two model-agnostic approaches for graph augmentation that satisfy all desired properties with different motivations.
Generalizing Tree Models for Improving Prediction Accuracy
In this work, we propose Decision Transformer Network (DTN), our highly accurate and interpretable tree model based on our generalized framework of tree models, decision transformers.
Signed Graph Diffusion Network
In this paper, we propose Signed Graph Diffusion Network (SGDNet), a novel graph neural network that achieves end-to-end node representation learning for link sign prediction in signed social graphs.
Knowledge Extraction with No Observable Data
Knowledge distillation is to transfer the knowledge of a large neural network into a smaller one and has been shown to be effective especially when the amount of training data is limited or the size of the student model is very small.
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables.
