end-to-end-learning

DiffSlack proposes a differentiable projection layer that enforces nonlinear inequality constraints in neural networks by reformulating them as equalities with learnable slack variables, achieving improved feasibility and planning success in vehicle path planning.

This paper proposes an end-to-end Conformer-based neural decoder for intracortical speech decoding from a participant with ALS, achieving a 23.80% character error rate without any external language model. It demonstrates that meaningful character-level decoding is possible in a fully end-to-end framework.

The authors introduce Sub-JEPA, a method using Subspace Gaussian Regularization to improve the stability of end-to-end world models like LeWM, showing consistent performance gains on continuous-control benchmarks.

MoCapAnything V2 introduces a fully end-to-end framework for arbitrary-skeleton motion capture from monocular video, jointly optimizing video-to-pose and pose-to-rotation predictions to resolve rotation ambiguity.