activity
20242026
most citedICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

2 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Evaluating SageMath-Augmented LLM Agents for Computational and Experimental Mathematics

Pavel Snopov, German Magai

Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows und…

cs.LG2026

Reparameterization through Coverings and Topological Weight Priors

Maxim Beketov, Pavel Snopov

We generalise the reparameterization trick (RT) applied in variational autoencoders (VAEs) letting these have latent spaces of non-trivial topology - i.e. that of base manifolds co…

cs.LG2025★ 1 cited

Topology-Aware Activation Functions in Neural Networks

Pavel Snopov, Oleg R. Musin

This study explores novel activation functions that enhance the ability of neural networks to manipulate data topology during training. Building on the limitations of traditional a…

cs.LG2024

Vulnerability Detection via Topological Analysis of Attention Maps

Pavel Snopov, Andrey Nikolaevich Golubinskiy

Recently, deep learning (DL) approaches to vulnerability detection have gained significant traction. These methods demonstrate promising results, often surpassing traditional stati…

cs.LG2024★ 2 cited

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…

math.DG2024

Porosity and topological properties of triply periodic minimal surfaces

Sergei Ermolenko, Pavel Snopov

Triple periodic minimal surfaces (TPMS) have garnered significant interest due to their structural efficiency and controllable geometry, making them suitable for a wide range of ap…

cs.LG2024

TopoBench: A Framework for Benchmarking Topological Deep Learning

Lev Telyatnikov, Guillermo Bernardez, Marco Montagna +34

This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL). TopoBench decomposes TDL int…