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Found 4 papers, 1 papers with code
Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients
We interpret these neural networks by finding an intersection between the equivalence class and human-readable equations defined by a symbolic search space.
Enhancing Image Layout Control with Loss-Guided Diffusion Models
Diffusion models are a powerful class of generative models capable of producing high-quality images from pure noise using a simple text prompt.
Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries.
Extremal learning: extremizing the output of a neural network in regression problems
Finding the extremizing input of an approximated model is formulated as the training of an additional neural network with a loss function that minimizes when the extremizing input is achieved.
