5 papers · 1 filter
PhAME: Phenotype-Aware Molecular Editing via Latent Diffusion
Åukasz Janisiów, Sebastian MusiaÅ, Bartosz ZieliÅski +2
Small-molecule drug discovery requires simultaneous optimization of numerous properties of candidate molecules. These properties can be investigated through the analysis of high-di…
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
Magdalena Proszewska, Tomasz Danel, Dawid Rymarczyk
Understanding the reasoning behind deep learning model predictions is crucial in cheminformatics and drug discovery, where molecular design determines their properties. However, cu…
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
Marcin Sendera, Åukasz Struski, Kamil KsiÄ Å¼ek +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…
OMENN: One Matrix to Explain Neural Networks
Adam Wróbel, MikoÅaj Janusz, Bartosz ZieliÅski +1
Deep Learning (DL) models are often black boxes, making their decision-making processes difficult to interpret. This lack of transparency has driven advancements in eXplainable Art…
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
Jan Olszewski, Dawid Rymarczyk, Piotr Wójcik +2
Active Visual Exploration (AVE) optimizes the utilization of robotic resources in real-world scenarios by sequentially selecting the most informative observations. However, modern…