1 citations · 1 across the 4 of their papers we have counts for
5 papers
Gated Graph Neural Networks for Learning Hidden Independent Cascade Dynamics
Anubha Goel, Illia Oleksiienko, Mateusz Wilinski +2
Information and infectious diseases spread through social networks, but the spreading probabilities driving them are hard to estimate without per-node activation times. Application…
Explicit Dropout: Deterministic Regularization for Transformer Architectures
Vidhi Agrawal, Illia Oleksiienko, Alexandros Iosifidis
Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives.…
InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation
Sanyam Jain, Khuram Naveed, Illia Oleksiienko +2
This work introduces InJecteD, a framework for interpreting Denoising Diffusion Probabilistic Models (DDPMs) by analyzing sample trajectories during the denoising process of 2D poi…
Variational Graph Convolutional Neural Networks
Illia Oleksiienko, Juho Kanniainen, Alexandros Iosifidis
Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in…
Continual Low-Rank Scaled Dot-product Attention
Ginés Carreto Picón, Illia Oleksiienko, Lukas Hedegaard +2
Transformers are widely used for their ability to capture data relations in sequence processing, with great success for a wide range of static tasks. However, the computational and…