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Found 10 papers, 6 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.
Uncovering mesa-optimization algorithms in Transformers
Transformers have become the dominant model in deep learning, but the reason for their superior performance is poorly understood.
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.
$μ$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.
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).
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight
Learning uses both simulation and real environments without requiring autonomous flight in the physical environment during training, and combines supervised learning for predicting positions to visit and reinforcement learning for continuous control.
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
We propose to decompose instruction execution to goal prediction and action generation.
