55 citations · 95 across the 6 of their papers we have counts for
3 papers · 1 filter
Learning to Extend Program Graphs to Work-in-Progress Code
Xuechen Li, Chris J. Maddison, Daniel Tarlow
Source code spends most of its time in a broken or incomplete state during software development. This presents a challenge to machine learning for code, since high-performing model…
Efficient and Accurate Gradients for Neural SDEs
Patrick Kidger, James Foster, Xuechen Li +1
Neural SDEs combine many of the best qualities of both RNNs and SDEs: memory efficient training, high-capacity function approximation, and strong priors on model space. This makes…
Neural SDEs as Infinite-Dimensional GANs
Patrick Kidger, James Foster, Xuechen Li +2
Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics. However, a fundamental limitation has been that such models have typically bee…