Introduces a terrain-specific benchmark showing cross-domain gaps in INR methods and demonstrates that HUVR+SIREN achieves superior height and derivative fidelity in a compact quantized format.
Spatial functa: Scaling functa to imagenet classifi- cation and generation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
Picasso is an inference-time pose corrector that uses physics-constrained rejection sampling and a contact scene graph to make multi-object scene reconstructions physically plausible and often more accurate.
DNG-Encoder represents NN weights as dynamic graphs to preserve sequential inference and powers INR2JLS, which raises INR classification accuracy by ~10% on CIFAR-100-INR.
citing papers explorer
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Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data
Introduces a terrain-specific benchmark showing cross-domain gaps in INR methods and demonstrates that HUVR+SIREN achieves superior height and derivative fidelity in a compact quantized format.
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Picasso: Holistic Scene Reconstruction with Physics-Constrained Sampling
Picasso is an inference-time pose corrector that uses physics-constrained rejection sampling and a contact scene graph to make multi-object scene reconstructions physically plausible and often more accurate.
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Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
DNG-Encoder represents NN weights as dynamic graphs to preserve sequential inference and powers INR2JLS, which raises INR classification accuracy by ~10% on CIFAR-100-INR.