5 citations · 18 across the 14 of their papers we have counts for
15 papers
Bongard-RWR+: Real-World Representations of Fine-Grained Concepts in Bongard Problems
Szymon Pawlonka, Mikołaj Małkiński, Jacek Mańdziuk
Bongard Problems (BPs) provide a challenging testbed for abstract visual reasoning (AVR), requiring models to identify visual concepts fromjust a few examples and describe them in…
Advancing Generalization Across a Variety of Abstract Visual Reasoning Tasks
Mikołaj Małkiński, Jacek Mańdziuk
The abstract visual reasoning (AVR) domain presents a diverse suite of analogy-based tasks devoted to studying model generalization. Recent years have brought dynamic progress in t…
Modified Adaptive Tree-Structured Parzen Estimator for Hyperparameter Optimization
Szymon Sieradzki, Jacek Mańdziuk
In this paper, we review hyperparameter optimization methods for machine learning models, with a particular focus on the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm.…
Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning
Piotr A. Kowalski, Szymon Kucharczyk, Jacek Mańdziuk
This study investigates the potential of hybrid metaheuristic algorithms to enhance the training of Probabilistic Neural Networks (PNNs) by leveraging the complementary strengths o…
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The p…
Reasoning Limitations of Multimodal Large Language Models. A Case Study of Bongard Problems
Mikołaj Małkiński, Szymon Pawlonka, Jacek Mańdziuk
Abstract visual reasoning (AVR) involves discovering shared concepts across images through analogy, akin to solving IQ test problems. Bongard Problems (BPs) remain a key challenge…
Bootstrap Sampling Rate Greater than 1.0 May Improve Random Forest Performance
Stanisław Kaźmierczak, Jacek Mańdziuk
Random forests (RFs) utilize bootstrap sampling to generate individual training sets for each component tree by sampling with replacement, with the sample size typically equal to t…
Interpretable Multi-task Learning with Shared Variable Embeddings
Maciej Żelaszczyk, Jacek Mańdziuk
This paper proposes a general interpretable predictive system with shared information. The system is able to perform predictions in a multi-task setting where distinct tasks are no…
A Unified View of Abstract Visual Reasoning Problems
Mikołaj Małkiński, Jacek Mańdziuk
The field of Abstract Visual Reasoning (AVR) encompasses a wide range of problems, many of which are inspired by human IQ tests. The variety of AVR tasks has resulted in state-of-t…
A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning
Mikołaj Małkiński, Jacek Mańdziuk
We study generalization and knowledge reuse capabilities of deep neural networks in the domain of abstract visual reasoning (AVR), employing Raven's Progressive Matrices (RPMs), a…
ReModels: Quantile Regression Averaging models
Grzegorz Zakrzewski, Kacper Skonieczka, Mikołaj Małkiński +1
Electricity price forecasts play a crucial role in making key business decisions within the electricity markets. A focal point in this domain are probabilistic predictions, which d…
Text-to-Image Cross-Modal Generation: A Systematic Review
Maciej Żelaszczyk, Jacek Mańdziuk
We review research on generating visual data from text from the angle of "cross-modal generation." This point of view allows us to draw parallels between various methods geared tow…
One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
Mikołaj Małkiński, Jacek Mańdziuk
Abstract Visual Reasoning (AVR) comprises a wide selection of various problems similar to those used in human IQ tests. Recent years have brought dynamic progress in solving partic…
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted,…
AttentionMix: Data augmentation method that relies on BERT attention mechanism
Dominik Lewy, Jacek Mańdziuk
The Mixup method has proven to be a powerful data augmentation technique in Computer Vision, with many successors that perform image mixing in a guided manner. One of the interesti…