The future of deep learning has unfolded in unexpected ways, surpassing expert predictions and transforming industries from healthcare to transportation. Its ability to learn from vast data and make ...

Keywords: generative models, auto-encoders, graph divergence, manifolds, geometry Citation: Shukla A, Dadhich R, Singh R, Rayas A, Saidi P, Dasarathy G, Berisha V and Turaga P (2024) Orthogonality and ...

Graph attention, used in GAT and GaAN, assigns weights to nodes based on their importance. ENADPool is a cluster-based hierarchical pooling method that assigns nodes to unique clusters, calculates ...

The Scene Graph Generation TRansformer (SGTR) is introduced (Figure 1) as an end-to-end model with three main modules: entity node generator, predicate node generator, and a differentiable graph ...

Deep learning solutions have recently demonstrated remarkable performance in phase unwrapping by approaching the problem as a semantic segmentation task. However, these solutions lack explainability ...

SDCD: Stable Differentiable Causal Discovery SDCD is a method for inferring causal graphs from labeled interventional data. You can read the associated preprint, "Stable Differentiable Causal ...

dasp-pytorch is a Python library for constructing differentiable audio signal processors using PyTorch. These differentiable processors can be used standalone or within the computation graph of neural ...