genwro.AI

We are a research group specializing in artificial intelligence, with a particular focus on machine learning and generative models

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About us

Through constant innovation and continuous pursuit of excellence, we strive to redefine the boundaries of what is possible in the world of artificial intelligence. Our pioneering research drives the development of cutting-edge algorithms and techniques, enabling the creation of more reliable and intelligent machine learning systems.

Collaborating with research centers

We cooperate with various research centers, including University of Cambridge, Imperial College London, Tooploox, Warsaw University of Technology, ETH Zurich, UCD Dublin, GMUM, Jagiellonian University in Kraków.

Publishing in top conferences

We consistently contribute research showcased at premier AI conferences like NeurIPS, AAAI, ICML, ICLR.

Working with various technologies

We are a team of ML researchers focused on: Generative models, 3D representation learning, Few-shot learning, Uncertainty estimation and out-of-distribution detection, omputer vision (segmentation, image restoration, image and video generation)

Published publications

Total citations

Projects

milion project co-financing

Team

Maciej Zięba

Maciej Zięba

DSc, PhD, genwro.AI group leader
Michał Stypułkowski

Michał Stypułkowski

PhD Candidate
Patryk Wielopolski

Patryk Wielopolski

PhD Candidate
Przemysław Dolata

Przemysław Dolata

PhD Candidate
Konrad Karanowski

Konrad Karanowski

Msc Candidate
Weronika Jakubowska

Weronika Jakubowska

PhD Candidate
Dawid Migacz

Dawid Migacz

PhD Candidate
Oleksii Furman

Oleksii Furman

Msc Candidate
Katarzyna Jabłońska

Katarzyna Jabłońska

PhD Candidate
Łukasz Lenkiewicz

Łukasz Lenkiewicz

MSc student
Wojciech Kozłowski

Wojciech Kozłowski

PhD Candidate

Portfolio

Explore a selection of our research papers published at leading machine learning conferences and journals.

  • All
  • 2024
  • 2023
  • 2022
  • 2021
FlowHMM: Flow-based continuous hidden Markov models

FlowHMM

NeurIPS 2022

PluGeN: Multi-Label Conditional Generation From Pre-Trained Models

Plugen

AAAI 2022

Non-Gaussian Gaussian Processes
                for Few-Shot Regression

NGGP

NeurIPS 2021

Diffused Heads: Diffusion Models Beat GANs
                on Talking-Face Generation

Diffused Heads

WACV 2024

HyperShot: Few-Shot Learning by Kernel HyperNetworks

HyperShot

WACV 2023

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression

TreeFlow

ECAI 2023