Conditional Morphogenesis: Emergent Generation of Structural Digits via Neural Cellular Automata

A recent study has introduced a method for fully decentralized certified unlearning in machine learning, focusing on the removal of specific data influences from trained models without a central coordinator. This approach, termed RR-DU, employs a random-walk procedure to enhance privacy and mitigate data poisoning risks, providing convergence guarantees in convex scenarios and stationarity in nonconvex cases.

A recent study has focused on the influential factors extracted by variational autoencoders (VAEs), highlighting the challenge of supervising learned representations without manual intervention. The research emphasizes the role of mutual information between inputs and learned factors as a key indicator for identifying influential factors, revealing that some factors may be non-influential and can be disregarded in data reconstruction.

A new reduced-space formulation for optimizing trained neural networks has been proposed, which evaluates the network's outputs and derivatives on a GPU. This method treats the neural network as a 'gray box,' leading to faster solves and fewer iterations compared to traditional full-space formulations. The approach has been demonstrated on two optimization problems, including adversarial generation for a classifier trained on MNIST images.

A recent study has introduced a pruned version of Capsule Networks (CapsNets), demonstrating that it can operate up to 9.90 times faster than traditional architectures by eliminating 95% of Primary Capsules while maintaining accuracy across various datasets, including MNIST and CIFAR-10.

Recent research has introduced geometry-aware noise injection techniques that enhance the training of machine learning models by considering the underlying structure of data. This approach involves projecting Gaussian noise onto the tangent space of a manifold and mapping it via geodesic curves, leading to improved model generalization and robustness.

A novel training objective called latent nonlinear denoising score matching (LNDSM) has been introduced, enhancing score-based generative models by integrating nonlinear dynamics with a VAE-based framework. This method reformulates the cross-entropy term using an approximate Gaussian transition, improving numerical stability and achieving superior sample quality on the MNIST dataset.