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Found 5 papers, 2 papers with code
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
In this work, we propose $\verb|fit-a-nef|$, a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets, resulting in a significant speed-up.
Data Augmentations in Deep Weight Spaces
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various fields, from analyzing and editing neural fields and implicit neural representations, to network pruning and quantization.
Neural Modulation Fields for Conditional Cone Beam Neural Tomography
Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction.
Learning Lie Group Symmetry Transformations with Neural Networks
The problem of detecting and quantifying the presence of symmetries in datasets is useful for model selection, generative modeling, and data analysis, amongst others.
Learning reversible symplectic dynamics
Time-reversal symmetry arises naturally as a structural property in many dynamical systems of interest.
