25 citations · 47 across the 11 of their papers we have counts for
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Interpretable Policy Distillation for Power Grid Topology Control
Aleksandra Dmitruka, Karlis Freivalds
Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardwar…
Discrete Denoising Diffusion Approach to Integer Factorization
Karlis Freivalds, Emils Ozolins, Guntis Barzdins
Integer factorization is a famous computational problem unknown whether being solvable in the polynomial time. With the rise of deep neural networks, it is interesting whether they…
Unsupervised Training for Neural TSP Solver
Elīza Gaile, Andis Draguns, Emīls Ozoliņš +1
There has been a growing number of machine learning methods for approximately solving the travelling salesman problem. However, these methods often require solved instances for tra…
Gates Are Not What You Need in RNNs
Ronalds Zakovskis, Andis Draguns, Eliza Gaile +2
Recurrent neural networks have flourished in many areas. Consequently, we can see new RNN cells being developed continuously, usually by creating or using gates in a new, original…
Goal-Aware Neural SAT Solver
Emils Ozolins, Karlis Freivalds, Andis Draguns +3
Modern neural networks obtain information about the problem and calculate the output solely from the input values. We argue that it is not always optimal, and the network's perform…
Matrix Shuffle-Exchange Networks for Hard 2D Tasks
Emīls Ozoliņš, Kārlis Freivalds, Agris Šostaks
Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that preven…
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
Andis Draguns, Emīls Ozoliņš, Agris Šostaks +2
Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences. The recently introduced neural Shuff…