๐โ: computable semantics for differentiable programming with higher-order functions and datatypes
๐โ: computable semantics for differentiable programming with higher-order functions and datatypes
Deep learning is moving towards increasingly sophisticated optimization objectives that employ higher-order functions, such as integration, continuous optimization, and root-finding. Since differentiable programming frameworks such as PyTorch and TensorFlow do not have first-class representations of these functions, developers must reason about the semantics of such objectives and manually translate them โฆ