
Scaling up learning with GAITprop
Backpropagation of error (BP) is a widely used and highly successful lea...
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Automatic variational inference with cascading flows
The automation of probabilistic reasoning is one of the primary aims of ...
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Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
In this paper we introduce the temporally factorized 3D convolution (3TC...
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GAITprop: A biologically plausible learning rule derived from backpropagation of error
Traditional backpropagation of error, though a highly successful algorit...
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SpikeTimingDependent Inference of Synaptic Weights
A potential solution to the weight transport problem, which questions th...
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Automatic structured variational inference
The aim of probabilistic programming is to automatize every aspect of pr...
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The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...
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Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
Perceived personality traits attributed to an individual do not have to ...
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Temporal Factorization of 3D Convolutional Kernels
3D convolutional neural networks are difficult to train because they are...
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Causal inference using Bayesian nonparametric quasiexperimental design
The de facto standard for causal inference is the randomized controlled ...
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kGANs: Ensemble of Generative Models with SemiDiscrete Optimal Transport
Generative adversarial networks (GANs) are the state of the art in gener...
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Perturbative estimation of stochastic gradients
In this paper we introduce a family of stochastic gradient estimation te...
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Wasserstein Variational Gradient Descent: From SemiDiscrete Optimal Transport to Ensemble Variational Inference
Particlebased variational inference offers a flexible way of approximat...
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Forward Amortized Inference for LikelihoodFree Variational Marginalization
In this paper, we introduce a new form of amortized variational inferenc...
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Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of a...
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The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables
This paper introduces the kernel mixture network, a new method for nonpa...
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GP CaKe: Effective brain connectivity with causal kernels
A fundamental goal in network neuroscience is to understand how activity...
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Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis
Computing accurate estimates of the Fourier transform of analog signals ...
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Estimating Nonlinear Dynamics with the ConvNet Smoother
Estimating the state of a dynamical system from a series of noisecorrup...
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Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes
The analysis of nonstationary time series is of great importance in many...
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Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dyn...
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Luca Ambrogioni
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