54 citations · 87 across the 11 of their papers we have counts for
10 papers · 1 filter
On the Limits of Representation-Based Test-Time Adaptation for Multimodal LLMs
Mikołaj Piórczyński, Wojciech Masarczyk, Damian Sójka +2
Vision-language models suffer significant accuracy drops under common corruptions. While test-time adaptation (TTA) successfully mitigates these shifts in CLIP-like contrastive mod…
Unpacking Softmax: How Temperature Drives Representation Collapse, Compression, and Generalization
Wojciech Masarczyk, Mateusz Ostaszewski, Tin Sum Cheng +3
The softmax function is a fundamental building block of deep neural networks, commonly used to define output distributions in classification tasks or attention weights in transform…
On consequences of finetuning on data with highly discriminative features
Wojciech Masarczyk, Tomasz Trzciński, Mateusz Ostaszewski
In the era of transfer learning, training neural networks from scratch is becoming obsolete. Transfer learning leverages prior knowledge for new tasks, conserving computational res…
The Tunnel Effect: Building Data Representations in Deep Neural Networks
Wojciech Masarczyk, Mateusz Ostaszewski, Ehsan Imani +3
Deep neural networks are widely known for their remarkable effectiveness across various tasks, with the consensus that deeper networks implicitly learn more complex data representa…
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks
Bartosz Grabowski, Przemysław Głomb, Wojciech Masarczyk +4
Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-ai…
Reinforcement learning with experience replay and adaptation of action dispersion
Paweł Wawrzyński, Wojciech Masarczyk, Mateusz Ostaszewski
Effective reinforcement learning requires a proper balance of exploration and exploitation defined by the dispersion of action distribution. However, this balance depends on the ta…
Logarithmic Continual Learning
Wojciech Masarczyk, Paweł Wawrzyński, Daniel Marczak +2
We introduce a neural network architecture that logarithmically reduces the number of self-rehearsal steps in the generative rehearsal of continually learned models. In continual l…
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
Kamil Deja, Paweł Wawrzyński, Wojciech Masarczyk +2
We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space. Deep generative models suffer from catastrophi…
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
Kamil Deja, Paweł Wawrzyński, Daniel Marczak +2
We introduce a binary latent space autoencoder architecture to rehearse training samples for the continual learning of neural networks. The ability to extend the knowledge of a mod…
Reducing catastrophic forgetting with learning on synthetic data
Wojciech Masarczyk, Ivona Tautkute
Catastrophic forgetting is a problem caused by neural networks' inability to learn data in sequence. After learning two tasks in sequence, performance on the first one drops signif…