PGLD optimizes synthesis-aware stochastic DNA libraries via policy gradients to bypass synthesis cost limits, enabling million-sequence libraries for antibody exploration at low cost.
PaGeR adapts perspective 3D foundation models to panoramas to predict depth, normals, and sky masks in one pass, achieving state-of-the-art 360-degree geometry estimation.
Multi-variable conformal prediction extends calibration to vector-valued scores with multiple variables, removing data splitting while preserving coverage and yielding smaller, more stable prediction sets.