13 citations · 14 across the 3 of their papers we have counts for
3 papers
Uniform approximation of common Gaussian process kernels using equispaced Fourier grids
Alex Barnett, Philip Greengard, Manas Rachh
The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponential…
Learning to Grow Pretrained Models for Efficient Transformer Training
Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen +6
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic…
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Han Guo, Philip Greengard, Hongyi Wang +3
The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decompo…