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🧠 NeurIPS2026 · 3 paper notes

Building Transformation Layers for Riemannian Neural Networks: The paper reinterprets fully connected layers as signed point-to-hyperplane responses, constructs manifold-valued layers using multiple tangent spaces and a tractable pseudo-distance, and extends them to convolution through product manifolds, validating representational flexibility across ten geometric instantiations while performance and cost remain geometry- and data-dependent.
D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Amplitude and Pixel Spaces: D-GAP uses task-loss gradients with respect to Fourier amplitudes to determine frequency-wise cross-domain mixing strengths, then fuses the result with pixel mixing, improving over the respective best generic methods by an average of 5.3 percentage points on four real-world datasets; however, not using target labels does not mean not accessing target data during training.
Position: Let’s Strengthen Verifiability If We Can’t Enforce Reproducibility: Drawing on a code-availability survey of five leading ML/CV conferences from 2021–2025, this paper proposes integrating experiment-log checks and metric recomputation from prediction files into submission, review, and publication when full reproduction cannot be enforced; it is a proposal for partial consistency verification, not a demonstrated fraud-detection system.