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Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …
the distribution of training samples. Research has fragmented into various interconnected …
Im-loss: information maximization loss for spiking neural networks
Abstract Spiking Neural Network (SNN), recognized as a type of biologically plausible
architecture, has recently drawn much research attention. It transmits information by $0/1 …
architecture, has recently drawn much research attention. It transmits information by $0/1 …
How to train your energy-based models
[PDF][PDF] Information-Theoretic Methods in Deep Neural Networks: Recent Advances and Emerging Opportunities.
We present a review on the recent advances and emerging opportunities around the theme
of analyzing deep neural networks (DNNs) with information-theoretic methods. We first …
of analyzing deep neural networks (DNNs) with information-theoretic methods. We first …
Energy-based models for anomaly detection: A manifold diffusion recovery approach
We present a new method of training energy-based models (EBMs) for anomaly detection
that leverages low-dimensional structures within data. The proposed algorithm, Manifold …
that leverages low-dimensional structures within data. The proposed algorithm, Manifold …
A general recipe for likelihood-free Bayesian optimization
The acquisition function, a critical component in Bayesian optimization (BO), can often be
written as the expectation of a utility function under a surrogate model. However, to ensure …
written as the expectation of a utility function under a surrogate model. However, to ensure …