activity
20172021
most citedLearning by Association - A versatile semi-supervised training method for neural networks

54 citations · 111 across the 5 of their papers we have counts for

collaborators

7 papers

cs.NE2021

Differentiable Programming of Reaction-Diffusion Patterns

Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson

Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature. Current RD system design practices boil down to trial-and…

cs.AI20216 cited

Texture Generation with Neural Cellular Automata

Alexander Mordvintsev, Eyvind Niklasson, Ettore Randazzo

Neural Cellular Automata (NCA) have shown a remarkable ability to learn the required rules to "grow" images, classify morphologies, segment images, as well as to do general computa…

cs.CV20209 cited

Image segmentation via Cellular Automata

Mark Sandler, Andrey Zhmoginov, Liangcheng Luo +3

In this paper, we propose a new approach for building cellular automata to solve real-world segmentation problems. We design and train a cellular automaton that can successfully se…

cs.LG20203 cited

MPLP: Learning a Message Passing Learning Protocol

Ettore Randazzo, Eyvind Niklasson, Alexander Mordvintsev

We present a novel method for learning the weights of an artificial neural network - a Message Passing Learning Protocol (MPLP). In MPLP, we abstract every operations occurring in…

cs.LG2018

GPGPU Linear Complexity t-SNE Optimization

Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev +5

The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high…

cs.CV201739 cited

Associative Domain Adaptation

Philip Haeusser, Thomas Frerix, Alexander Mordvintsev +1

We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain…