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Found 9 papers, 3 papers with code
Emergent Dynamics in Neural Cellular Automata
Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA).
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.
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.
DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata
Current Dynamic Texture Synthesis (DyTS) models can synthesize realistic videos.
CLIPasso: Semantically-Aware Object Sketching
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings.
Optimizing Latent Space Directions For GAN-based Local Image Editing
Generative Adversarial Network (GAN) based localized image editing can suffer from ambiguity between semantic attributes.
[Re] Warm-Starting Neural Network Training
We reproduce the results of the paper ”On Warm-Starting Neural Network Training.” In many real-world applications, the training data is not readily available and is accumulated over time.
ChOracle: A Unified Statistical Framework for Churn Prediction
In this approach which is more realistic in real world online services, at each time-step the model predicts the user return time instead of predicting a churn label.
Back to square one: probabilistic trajectory forecasting without bells and whistles
We introduce a spatio-temporal convolutional neural network model for trajectory forecasting from visual sources.
