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Found 6 papers, 0 papers with code
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
To remedy this issue, we introduce Bayesian convolutional deep sets that construct the random translation equivariant functional representations with stationary prior.
Scalable Hybrid Hidden Markov Model with Gaussian Process Emission for Sequential Time-series Observations
We then propose a scalable learning method to train the HMM-GPSM model using large-scale data having (1) long sequences of state transitions and (2) a large number of time-series observations for each hidden state.
Approximate Inference for Spectral Mixture Kernel
To improve the training, we propose an approximate Bayesian inference for the SM kernel.
Implicit Kernel Attention
For the attention in Transformer and GAT, we derive that the attention is a product of two parts: 1) the RBF kernel to measure the similarity of two instances and 2) the exponential of $L^{2}$ norm to compute the importance of individual instances.
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering
This model can effectively model the sequence of time-series data.
Spectral Mixture Kernel Approximation Using Reparameterized Random Fourier Feature
We propose a method for Spectral Mixture kernel approximation using the Reparameterized Random Fourier Feature (R-RFF) in the sense of both general parameter and natural parameter view.
