129 citations · 798 across the 46 of their papers we have counts for
7 papers · 1 filter
Sampled Transformer for Point Sets
Shidi Li, Christian Walder, Alexander Soen +2
The sparse transformer can reduce the computational complexity of the self-attention layers to , whilst still being a universal approximator of continuous sequence-to-sequenc…
Interval-censored Hawkes processes
Marian-Andrei Rizoiu, Alexander Soen, Shidi Li +4
Interval-censored data solely records the aggregated counts of events during specific time intervals - such as the number of patients admitted to the hospital or the volume of vehi…
UNIPoint: Universally Approximating Point Processes Intensities
Alexander Soen, Alexander Mathews, Daniel Grixti-Cheng +1
Point processes are a useful mathematical tool for describing events over time, and so there are many recent approaches for representing and learning them. One notable open questio…
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang, Christian J. Walder, Edwin V. Bonilla +2
Approximate inference techniques are the cornerstone of probabilistic methods based on Gaussian process priors. Despite this, most work approximately optimizes standard divergence…
Efficient Non-parametric Bayesian Hawkes Processes
Rui Zhang, Christian Walder, Marian-Andrei Rizoiu +1
In this paper, we develop an efficient nonparametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it pr…
Revisiting revisits in trajectory recommendation
Aditya Krishna Menon, Dawei Chen, Lexing Xie +1
Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit. It is strongly desirable for the recommended sequence to avoid loops…
Learning Points and Routes to Recommend Trajectories
Dawei Chen, Cheng Soon Ong, Lexing Xie
The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation…