25 citations · 47 across the 11 of their papers we have counts for
12 papers
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…
Learning to Move Objects with Fluid Streams in a Differentiable Simulation
Karlis Freivalds, Laura Leja, Oskars Teikmanis
We introduce a method for manipulating objects in three-dimensional space using controlled fluid streams. To achieve this, we train a neural network controller in a differentiable…
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…
Denoising Diffusion for Sampling SAT Solutions
Karlis Freivalds, Sergejs Kozlovics
Generating diverse solutions to the Boolean Satisfiability Problem (SAT) is a hard computational problem with practical applications for testing and functional verification of soft…
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…
Graph Compact Orthogonal Layout Algorithm
Karlis Freivalds, Jans Glagolevs
There exist many orthogonal graph drawing algorithms that minimize edge crossings or edge bends, however they produce unsatisfactory drawings in many practical cases. In this paper…
A Statistical Method for Object Counting
Jans Glagolevs, Karlis Freivalds
In this paper we present a new object counting method that is intended for counting similarly sized and mostly round objects. Unlike many other algorithms of the same purpose, the…
Improving the Neural GPU Architecture for Algorithm Learning
Karlis Freivalds, Renars Liepins
Algorithm learning is a core problem in artificial intelligence with significant implications on automation level that can be achieved by machines. Recently deep learning methods a…