4 citations · 10 across the 16 of their papers we have counts for
19 papers
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…
MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging
Kacper Marzol, Ignacy Kolton, Weronika Smolak-Dyżewska +6
Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstructi…
UnGuide: Learning to Forget with LoRA-Guided Diffusion Models
Agnieszka Polowczyk, Alicja Polowczyk, Dawid Malarz +4
Recent advances in large-scale text-to-image diffusion models have heightened concerns about their potential misuse, especially in generating harmful or misleading content. This un…
CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian Splatting
Kornel Howil, Joanna Waczyńska, Piotr Borycki +3
Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content.…
CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression
Krzysztof Byrski, Jacek Tabor, Przemysław Spurek +1
In practical applications of regression analysis, it is not uncommon to encounter a multitude of values for each attribute. In such a situation, the univariate distribution, which…
MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization
Rafał Tobiasz, Grzegorz Wilczyński, Marcin Mazur +3
Gaussian Splatting (GS) is a recent and pivotal technique in 3D computer graphics. GS-based algorithms almost always bypass classical methods such as ray tracing, which offer numer…
RaySplats: Ray Tracing based Gaussian Splatting
Krzysztof Byrski, Marcin Mazur, Jacek Tabor +4
3D Gaussian Splatting (3DGS) is a process that enables the direct creation of 3D objects from 2D images. This representation offers numerous advantages, including rapid training an…
VeGaS: Video Gaussian Splatting
Weronika Smolak-Dyżewska, Dawid Malarz, Kornel Howil +3
Implicit Neural Representations (INRs) employ neural networks to approximate discrete data as continuous functions. In the context of video data, such models can be utilized to tra…
GASP: Gaussian Splatting for Physic-Based Simulations
Piotr Borycki, Weronika Smolak, Joanna Waczyńska +3
Physics simulation is paramount for modeling and utilizing 3D scenes in various real-world applications. However, integrating with state-of-the-art 3D scene rendering techniques su…
PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification
Magdalena Trędowicz, Marcin Mazur, Szymon Janusz +3
Video processing is generally divided into two main categories: processing of the entire video, which typically yields optimal classification outcomes, and real-time processing, wh…
NegGS: Negative Gaussian Splatting
Artur Kasymov, Bartosz Czekaj, Marcin Mazur +2
One of the key advantages of 3D rendering is its ability to simulate intricate scenes accurately. One of the most widely used methods for this purpose is Gaussian Splatting, a nove…
HyperPlanes: Hypernetwork Approach to Rapid NeRF Adaptation
Paweł Batorski, Dawid Malarz, Marcin Przewięźlikowski +3
Neural radiance fields (NeRFs) are a widely accepted standard for synthesizing new 3D object views from a small number of base images. However, NeRFs have limited generalization pr…
MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation
Dominik Zimny, Artur Kasymov, Adam Kania +4
NeRF is a popular model that efficiently represents 3D objects from 2D images. However, vanilla NeRF has some important limitations. NeRF must be trained on each object separately.…