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Found 7 papers, 2 papers with code
NoiseNCA: Noisy Seed Improves Spatio-Temporal Continuity of Neural Cellular Automata
We demonstrate the effectiveness of our approach in preserving the consistency of NCA dynamics across a wide range of spatio-temporal granularities.
Emergent Dynamics in Neural Cellular Automata
Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA).
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.
Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
Feature alignment is the primary means of fusing multimodal data.
Ranked #1 on
Arrhythmia Detection
on MIT-BIH Arrhythmia Database
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos.
Spatiotemporal Attacks for Embodied Agents
Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness.
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
In this paper, we first draw the close connection between adversarial robustness and neuron sensitivities, as sensitive neurons make the most non-trivial contributions to model predictions in the adversarial setting.
