4 citations · 10 across the 16 of their papers we have counts for
7 papers · 1 filter
ClasSAE: Class-Aligned Sparse Autoencoders via Differentiable Feature-Class Affinity
Jakub Stępień, Marcin Mazur, Jacek Tabor +1
Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingly used not only for passive…
Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection
Tadeusz Dziarmaga, Witold Sikora, Łukasz Struski +2
Top-k selection is a fundamental computational primitive with applications spanning databases, information retrieval, signal processing, and modern machine learning workloads, incl…
Fast LapSum: Exact Differentiable Top- at Million Scale
Łukasz Struski, Joanna Wojciechowicz, Jakub Antczak +3
Selecting the top- elements is a fundamental operation for inducing sparsity in large-scale models and optimization problems, enabling robust expert activation, token routing or…
TOM-GS: Editable Video Representation via Temporal Opacity Modulation of Static 3D Gaussians
Marek Lisowski, Łukasz Smoliński, Kornel Howil +3
While Implicit Neural Representations (INRs) and dynamic 3D Gaussian Splatting (3DGS) achieve impressive results in video processing, they often fall short of producing representat…
SoftSAE: Dynamic Top-K Selection for Adaptive Sparse Autoencoders
Jakub Stępień, Marcin Mazur, Jacek Tabor +1
Sparse Autoencoders (SAEs) have become an important tool in mechanistic interpretability, helping to analyze internal representations in both Large Language Models (LLMs) and Visio…
QuantumGS: Quantum Encoding Framework for Gaussian Splatting
Grzegorz Wilczyński, Rafał Tobiasz, Paweł Gora +2
Recent advances in neural rendering, particularly 3D Gaussian Splatting (3DGS), have enabled real-time rendering of complex scenes. However, standard 3DGS relies on spherical harmo…
ReLAPSe: Reinforcement-Learning-trained Adversarial Prompt Search for Erased concepts in unlearned diffusion models
Ignacy Kolton, Kacper Marzol, Paweł Batorski +3
Machine unlearning is a key defense mechanism for removing unauthorized concepts from text-to-image diffusion models, yet recent evidence shows that latent visual information often…