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Found 8 papers, 1 papers with code
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means
We also introduce a variant, IDKM with Jacobian-Free-Backpropagation (IDKM-JFB), for which the time complexity of the gradient calculation is independent of $t$ as well.
Learning Interpretable Models for Coupled Networks Under Domain Constraints
This not only leads to a cleaner way of applying network constraints but also provides a more scalable solution when network connectivity is sparse.
Expertise and confidence explain how social influence evolves along intellective tasks
Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals.
DANR: Discrepancy-aware Network Regularization
Network regularization is an effective tool for incorporating structural prior knowledge to learn coherent models over networks, and has yielded provably accurate estimates in applications ranging from spatial economics to neuroimaging studies.
Estimating localized complexity of white-matter wiring with GANs
Our proposed method builds on a Bayesian estimate of heteroscedastic aleatoric uncertainty of a region of white matter by inpainting it from its context.
Spatial Coherence of Oriented White Matter Microstructure: Applications to White Matter Regions Associated with Genetic Similarity
Furthermore, we show that the orientation similarity within these regions generalizes to a subset of 47 pairs of non-twin siblings, and show that these siblings are on average as similar as dizygotic twins.
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data
Data mining practitioners are facing challenges from data with network structure.
Behavior Query Discovery in System-Generated Temporal Graphs
In this work, we investigate how to query temporal graphs and treat query formulation as a discriminative temporal graph pattern mining problem.
