Manifold Approximation Leads to Robust Kernel Alignment accepted at TMLR
leland_mcinnes · x · 2026-10-29
The authors announce their paper 'Manifold Approximation Leads to Robust Kernel Alignment' has been accepted at TMLR. Kernel Alignment compares two different representations, shapes, or geometries; this work makes it more robust via manifold approximation.
More from Research
- Hypothesis: DeepSeek seeds on GPT data, and internal RL drives its more-extreme-than-GPT personality — teortaxesTex · 2026-10-29
- BlackboxNLP kicks off at EMNLP2026 Budapest, opening keynote on how LLMs read — gsarti_ · 2026-10-29
- QUAN paper: real-valued quaternion convolution nets SOTA in oriented detection — Bryce Grant · 2026-10-29
- Tim Vieira Explains Conditional Poisson Sampling with Verified Identities and Interactive Visuals — xtimv · 2026-10-29
- Reward-model ensembles from different pretraining seeds resist reward hacking better — vxnuaj · 2026-10-29
- Google DeepMind engineer shows five CUDA optimizations that speed up 3D Gaussian Splatting by 10.5x — janusch_patas · 2026-10-29