Transformers approximate α-Hölder functions via softmax partition of unity with two encoder blocks, achieving near minimax-optimal generalization rates.
SNAP uses a pose-conditioned local decoder and latent-space reconstruction objective for self-supervised geometric representation learning via novel view synthesis, yielding transferable multi-view features competitive with supervised methods.
SOLAR aligns soft-token representations across languages during supervised fine-tuning to improve multilingual reasoning consistency, boosting accuracy up to 17.7 points with largest gains on low-resource languages.
A framework predicts reconstruction error of compressive signal parameterizations via scaled differences between model predictions at different compression levels without ground truth. It yields non-asymptotic, signal-specific bounds that closely track global errors and local error heatmaps across i
Actor-accelerated PDA learns a policy network to approximate PDA optimization subproblems, speeding up continuous-action reinforcement learning while preserving convergence guarantees and outperforming PPO.