46 citations · 66 across the 18 of their papers we have counts for
7 papers · 1 filter
On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation
Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE). In existing samplers, the coefficients of the ODE solvers are p…
Variance Constrained Autoencoding
D. T. Braithwaite, M. O'Connor, W. B. Kleijn
Recent state-of-the-art autoencoder based generative models have an encoder-decoder structure and learn a latent representation with a pre-defined distribution that can be sampled…
Approximated Orthonormal Normalisation in Training Neural Networks
Guoqiang Zhang, Kenta Niwa, W. B. Kleijn
Generalisation of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new techniqu…
The HSIC Bottleneck: Deep Learning without Back-Propagation
Wan-Duo Kurt Ma, J. P. Lewis, W. Bastiaan Kleijn
We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to the conventional cross-entropy…
Rapidly Adapting Moment Estimation
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the indiv…
Kernel Density Estimation-Based Markov Models with Hidden State
Gustav Eje Henter, Arne Leijon, W. Bastiaan Kleijn
We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities…