1 citations · 1 across the 11 of their papers we have counts for
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Graph Random Features for Scalable Gaussian Processes
Matthew Zhang, Jihao Andreas Lin, Krzysztof Choromanski +3
We study the application of graph random features (GRFs) - a recently introduced stochastic estimator of graph node kernels - to scalable Gaussian processes on discrete input space…
Rotary Position Encodings for Graphs
Isaac Reid, Arijit Sehanobish, Cederik Höfs +7
We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large language models (LLMs) and vision transform…
Tighter sparse variational Gaussian processes
Thang D. Bui, Matthew Ashman, Richard E. Turner
Sparse variational Gaussian process (GP) approximations based on inducing points have become the de facto standard for scaling GPs to large datasets, owing to their theoretical ele…