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Transfer learning slashes the computational cost of fitting neural fields to scientific data, making high-dimensional simulations far more tractable.
Achieve photorealistic 3D reconstruction from extremely dark images with a new Gaussian Splatting pipeline that leverages luminance guidance.
Reconstructing 3D scenes from images obscured by smoke and extreme darkness is now significantly more achievable, thanks to insights gleaned from the NTIRE 2026 challenge.
Smooth activation functions unlock smoothness adaptivity in constant-depth neural networks, achieving minimax-optimal statistical rates without needing depth increases like ReLU networks.
Achieve up to 3.57x speedup in mixed-precision QNN inference by using a runtime-reconfigurable bitwise systolic array, enabling dynamic precision adjustments for optimal resource-accuracy trade-offs on hardware accelerators.