8 citations · 19 across the 7 of their papers we have counts for
7 papers
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
Marcin Sendera, Łukasz Struski, Kamil Książek +3
While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pre…
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
Artur Kasymov, Marcin Sendera, Michał Stypułkowski +2
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the…
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Marcin Sendera, Marcin Przewięźlikowski, Konrad Karanowski +3
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task. The main challenge in this area is the one-shot setting where only one eleme…
Non-Gaussian Gaussian Processes for Few-Shot Regression
Marcin Sendera, Jacek Tabor, Aleksandra Nowak +5
Gaussian Processes (GPs) have been widely used in machine learning to model distributions over functions, with applications including multi-modal regression, time-series prediction…
Flow-based SVDD for anomaly detection
Marcin Sendera, Marek Śmieja, Łukasz Maziarka +3
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools. Contrary to other appro…
OneFlow: One-class flow for anomaly detection based on a minimal volume region
Łukasz Maziarka, Marek Śmieja, Marcin Sendera +3
We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is co…
Data adaptation in HANDY economy-ideology model
Marcin Sendera
The concept of mathematical modeling is widespread across almost all of the fields of contemporary science and engineering. Because of the existing necessity of predictions the beh…