54 citations · 164 across the 23 of their papers we have counts for
12 papers · 1 filter
TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression
Patryk Wielopolski, Maciej Zięba
The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables…
Continual learning on 3D point clouds with random compressed rehearsal
Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski +2
Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype…
HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks
M. Przewięźlikowski, P. Przybysz, J. Tabor +2
The aim of Few-Shot learning methods is to train models which can easily adapt to previously unseen tasks, based on small amounts of data. One of the most popular and elegant Few-S…
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…
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka +4
Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling. However, existing methods often lack the…
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Maciej Zięba, Marcin Przewięźlikowski, Marek Śmieja +3
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenar…
Conditional Invertible Flow for Point Cloud Generation
Michał Stypułkowski, Maciej Zamorski, Maciej Zięba +1
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a pro…
Generative Adversarial Networks: recent developments
Maciej Zamorski, Adrian Zdobylak, Maciej Zięba +1
In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanat…
Semi-supervised learning with Bidirectional GANs
Maciej Zamorski, Maciej Zięba
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss funct…
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Maciej Zamorski, Maciej Zięba, Piotr Klukowski +4
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtai…
Training Triplet Networks with GAN
Maciej Zieba, Lei Wang
Triplet networks are widely used models that are characterized by good performance in classification and retrieval tasks. In this work we propose to train a triplet network by putt…