interpolation
Interpolation in AI signifies the process of estimating unknown values within the range of a discrete set of known points, crucial in scenarios where models predict outputs based on existing data.
- DNAEdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing
- Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing
- Efficient $k$-Sparse Band–Limited Interpolation with Improved Approximation Ratio
- FLOWING: Implicit Neural Flows for Structure-Preserving Morphing
- Grids Often Outperform Implicit Neural Representation at Compressing Dense Signals
- How Benchmark Prediction from Fewer Data Misses the Mark
- Low-Rank Graphon Learning for Networks
- Machine Unlearning under Overparameterization
- Optimal and Provable Calibration in High-Dimensional Binary Classification: Angular Calibration and Platt Scaling
- Point Cloud Synthesis Using Inner Product Transforms
- PolyJuice Makes It Real: Black-Box, Universal Red Teaming for Synthetic Image Detectors
- Rotary Masked Autoencoders are Versatile Learners
- TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting