New arXiv work shows rectified flow models can achieve ~Õ(ε⁻²) sample complexity, improving on prior ε⁻⁴ bounds for flow matching and matching the optimal rate for mean estimation. The gains come from rectified flows’…

#AI #MachineLearning #GenerativeModels #ArXiv
https://arxiv.org/abs/2601.20250

Appleが発表した「Normalizing Trajectory Models (NTM)」：確率的軌跡モデリングの限界を突破する高効率生成AIアーキテクチャ

Appleが発表したNormalizing Trajectory Models (NTM)の技術解説。正確な尤度を維持しつつ数ステップでの高速生成を実現。

#GenerativeModels #NormalizingFlows #DiffusionModels #ComputerVision #MachineLearning

New arXiv notes frame control as dynamically constrained sampling, extending flow matching and diffusion methods to trajectory planning and steering systems toward target states. Aimed at control theory and robotics…

#Robotics #ControlSystems #GenerativeModels #AI
https://arxiv.org/abs/2609.33164

His talk "Fail Better? The Computational Study of Character Types in Narratives" led into a lively discussion on the benefits and pitfalls of automating the #CLS annotation process with #GenerativeModels, and on how to #validate that the output we receive is actually what we were asking for.

Explaining grounding #Grounding #GenerativeModels #ArtificialIntelligence #MachineLearning #DataScience

Explaining grounding #Grounding #GenerativeModels #ArtificialIntelligence #MachineLearning #DataScience