'Neural optical flow': parameterizing classic CV circuits for faster network convergence
mike64_t · x · 2026-10-26
Developer mike64t describes a technique he calls "neural optical flow": essentially parameterizing circuits known from traditional computer vision (like optical flow) and bringing them into neural networks. He's unsure if prior literature exists and jokingly invites being "Schmidhuber'd."
- He frames this as an unusual invocation of inductive bias: these expressions are believed to work, so faster convergence from getting the bias "more right" isn't surprising
- He denied the technique is about spatial consistency constraints like rigid bodies moving at the same speed
More from Research
- Sakana AI hosts Tokyo RSI symposium: AI Scientist in Nature, Schmidhuber keynotes — RobertTLange · 2026-10-26
- HuggingFace's Carbon-X Spots Pathogenic Splice Variant Where Gemini 3.8 Flash Misreads the Gene — LoubnaBenAllal1 · 2026-10-26
- ETH Zurich paper: dynamic evaluation allocation matches 10x-budget model rankings — zouharvi · 2026-10-26
- DuoLM: Fast LLM-Based Personalized Recommendation via Compact Encoder-Ranker — GoogleDeepMind · 2026-10-26
- DISC Learns to Combine Time-Series Dataset Similarity Metrics for Transfer Prediction — Yen-Ku Liu · 2026-10-26
- Adobe's SliderEdit++ Improves Slider-Based Image Editing by 18.5% with New SlideBench — Arman Zarei · 2026-10-26