approximation errors
Approximation errors measure the difference between an approximate solution and the exact solution of a mathematical model. In AI, understanding and minimizing these errors are critical for ensuring the accuracy of predictions and outputs.
- AuroRA: Breaking Low-Rank Bottleneck of LoRA with Nonlinear Mapping
- EditInfinity: Image Editing with Binary-Quantized Generative Models
- Generalization Bounds for Kolmogorov-Arnold Networks (KANs) and Enhanced KANs with Lower Lipschitz Complexity
- Informed Correctors for Discrete Diffusion Models
- Robust Hyperbolic Learning with Curvature-Aware Optimization
- SCoT: Unifying Consistency Models and Rectified Flows via Straight-Consistent Trajectories
- SUMO: Subspace-Aware Moment-Orthogonalization for Accelerating Memory-Efficient LLM Training
- Temporal-Difference Variational Continual Learning