Search Results for author:
Found 7 papers, 3 papers with code
Towards Data-Free Domain Generalization
In particular, we address the question: How can knowledge contained in models trained on different source domains be merged into a single model that generalizes well to unseen target domains, in the absence of source and target domain data?
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts.
ARCADe: A Rapid Continual Anomaly Detector
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored.
Few-Shot One-Class Classification via Meta-Learning
Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples.
Predicting the Co-Evolution of Event and Knowledge Graphs
By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well.
Learning with Memory Embeddings
We introduce a number of hypotheses on human memory that can be derived from the developed mathematical models.
Type-Constrained Representation Learning in Knowledge Graphs
Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning.
