GazePrior learns a 3D prior over eyes to synthesize realistic ground-truth data for training eye trackers on new devices without new real data collection.
Fourier features let networks learn high frequency functions in low dimensional domains.Advances in neural information processing systems, 33:7537–7547
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
Neural networks parameterize finite-rank generators for ODEs on the orthogonal Lie group, allowing optimization of orthonormal bases in function space with a universality result that rank-2 generators suffice for density.
Spectral Energy Centroid is a new metric that quantifies signal frequency and INR spectral bias, supporting better hyperparameter selection and cross-architecture analysis.
PEACH uses a novel spatio-temporal point cloud sequence encoder plus auxiliary supervision to enable zero-shot adaptation of graph network simulators to unseen physical properties, outperforming mesh-based baselines in simulation accuracy while being more deployable for real scenes.
TransmissiveGS disentangles reflections from transmissions in Gaussian Splatting via dual-Gaussian modeling, residual cues, and a reflection light field for improved transmissive scene reconstruction.
PEPS decomposes positional encodings into projected points with unique frequency-dependent motions to support more efficient learned grid-based encodings in INRs, outperforming prior methods on image, texture, and SDF tasks with often 25% fewer parameters.
WinDiNet repurposes a 2B-parameter video diffusion model as a differentiable surrogate that generates 112-frame urban wind flow rollouts in under one second and enables direct gradient optimization of building positions.
Pretrained scalp-EEG foundation models can be transferred to ECoG via adapters and fine-tuning to match or exceed subject-specific baselines on regression tasks while requiring far less per-patient data.
citing papers explorer
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GazePrior: Zero-Shot AR/VR Eye Tracking via Learned 3D Gaze Reconstruction
GazePrior learns a 3D prior over eyes to synthesize realistic ground-truth data for training eye trackers on new devices without new real data collection.
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Learning Orthonormal Bases for Function Spaces
Neural networks parameterize finite-rank generators for ODEs on the orthogonal Lie group, allowing optimization of orthonormal bases in function space with a universality result that rank-2 generators suffice for density.
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Spectral Energy Centroid: a Metric for Improving Performance and Analyzing Spectral Bias in Implicit Neural Representations
Spectral Energy Centroid is a new metric that quantifies signal frequency and INR spectral bias, supporting better hyperparameter selection and cross-architecture analysis.
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Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
PEACH uses a novel spatio-temporal point cloud sequence encoder plus auxiliary supervision to enable zero-shot adaptation of graph network simulators to unseen physical properties, outperforming mesh-based baselines in simulation accuracy while being more deployable for real scenes.
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TransmissiveGS: Residual-Guided Disentangled Gaussian Splatting for Transmissive Scene Reconstruction and Rendering
TransmissiveGS disentangles reflections from transmissions in Gaussian Splatting via dual-Gaussian modeling, residual cues, and a reflection light field for improved transmissive scene reconstruction.
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PEPS: Positional Encoding Projected Sampling -- Extended
PEPS decomposes positional encodings into projected points with unique frequency-dependent motions to support more efficient learned grid-based encodings in INRs, outperforming prior methods on image, texture, and SDF tasks with often 25% fewer parameters.
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Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
WinDiNet repurposes a 2B-parameter video diffusion model as a differentiable surrogate that generates 112-frame urban wind flow rollouts in under one second and enables direct gradient optimization of building positions.
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CORTEG: Foundation Models Enable Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings
Pretrained scalp-EEG foundation models can be transferred to ECoG via adapters and fine-tuning to match or exceed subject-specific baselines on regression tasks while requiring far less per-patient data.