Author

J. Tabor

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Preprint Oct 2026

ClasSAE: Class-Aligned Sparse Autoencoders via Differentiable Feature-Class Affinity

Sparse Autoencoders (SAEs) began as an unsupervised tool for decomposing neural representations into sparse, interpretable features, and are increasingly used not only for passive analysis but also for active interventions such as unlearning, bias mitigation, and concept editing. A central challenge for these editing a...

Jakub Stepien, M. Mazur, J. Tabor et al. · 0 citations

Preprint Aug 2026

Floating Radiance Networks

Experiments across standard reconstruction benchmarks demonstrate competitive rendering quality while using a compact set of primitives and establish FlaRe as a versatile representation that brings high-fidelity neural rendering, ray-tracing, geometric manipulation, and appearance editing into a unified scene model.

K. Byrski, Rafał Tobiasz, Grzegorz Wilczynski et al. · 0 citations

#machine learning Preprint Aug 2026

Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection

Prof-K is introduced, a fast, scalable, and distribution-agnostic algorithm for exact top-k selection that derives high-probability guarantees for correctness and buffer size, together with an approximately optimal sample size that minimizes overhead as a function of N and k.

Tadeusz Dziarmaga, W. Sikora, L. Struski et al. · 2 citations