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Found 18 papers, 11 papers with code
Mesh Neural Cellular Automata
We propose Mesh Neural Cellular Automata (MeshNCA), a method for directly synthesizing dynamic textures on 3D meshes without requiring any UV maps.
Biomaker CA: a Biome Maker project using Cellular Automata
Finally, we show how to perform interactive evolution, where the user decides how to evolve a plant model interactively and then deploys it in a larger environment.
Growing Steerable Neural Cellular Automata
Neural Cellular Automata (NCA) models have shown remarkable capacity for pattern formation and complex global behaviors stemming from local coordination.
Differentiable Programming of Chemical Reaction Networks
We present a differentiable formulation of abstract chemical reaction networks (CRNs) that can be trained to solve a variety of computational tasks.
Transformers learn in-context by gradient descent
We start by providing a simple weight construction that shows the equivalence of data transformations induced by 1) a single linear self-attention layer and by 2) gradient-descent (GD) on a regression loss.
Growing Isotropic Neural Cellular Automata
Modeling the ability of multicellular organisms to build and maintain their bodies through local interactions between individual cells (morphogenesis) is a long-standing challenge of developmental biology.
$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
We study the problem of example-based procedural texture synthesis using highly compact models.
Differentiable Programming of Reaction-Diffusion Patterns
Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature.
Texture Generation with Neural Cellular Automata
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 computation such as path-finding.
Image segmentation via Cellular Automata
The update rule is applied repeatedly in parallel to a large random subset of cells and after convergence is used to produce segmentation masks that are then back-propagated to learn the optimal update rules using standard gradient descent methods.
MPLP: Learning a Message Passing Learning Protocol
We present a novel method for learning the weights of an artificial neural network - a Message Passing Learning Protocol (MPLP).
Feature Visualization
There is a growing sense that neural networks need to be interpretable to humans.
Differentiable Image Parameterizations
Typically, we parameterize the input image as the RGB values of each pixel, but that isn’t the only way.
GPGPU Linear Complexity t-SNE Optimization
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-dimensional data.
The Building Blocks of Interpretability
In this article, we treat existing interpretability methods as fundamental and composable building blocks for rich user interfaces.
Associative Domain Adaptation
Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain invariant embeddings, while minimizing the classification error on the labeled source domain.
Ranked #6 on
Domain Adaptation
on SYNSIG-to-GTSRB
Learning by Association -- A Versatile Semi-Supervised Training Method for Neural Networks
We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data.
Learning by Association - A versatile semi-supervised training method for neural networks
We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data.
