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05/31/2023
Spontaneous symmetry breaking in generative diffusion models
Generative diffusion models have recently emerged as a leading approach ...
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05/19/2022
Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers
In this work, we provide an exact likelihood alternative to the variatio...
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10/12/2021
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
Normalizing flows have shown great success as general-purpose density es...
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09/17/2021
Knowledge is reward: Learning optimal exploration by predictive reward cashing
There is a strong link between the general concept of intelligence and t...
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02/23/2021
Scaling up learning with GAIT-prop
Backpropagation of error (BP) is a widely used and highly successful lea...
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02/09/2021
Automatic variational inference with cascading flows
The automation of probabilistic reasoning is one of the primary aims of ...
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06/29/2020
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
In this paper we introduce the temporally factorized 3D convolution (3TC...
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06/11/2020
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Traditional backpropagation of error, though a highly successful algorit...
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03/09/2020
Spike-Timing-Dependent Inference of Synaptic Weights
A potential solution to the weight transport problem, which questions th...
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02/03/2020
Automatic structured variational inference
The aim of probabilistic programming is to automatize every aspect of pr...
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01/29/2020
The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonpara...
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12/20/2019
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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12/09/2019
Temporal Factorization of 3D Convolutional Kernels
3D convolutional neural networks are difficult to train because they are...
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11/15/2019
Causal inference using Bayesian non-parametric quasi-experimental design
The de facto standard for causal inference is the randomized controlled ...
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07/09/2019
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
Generative adversarial networks (GANs) are the state of the art in gener...
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03/31/2019
Perturbative estimation of stochastic gradients
In this paper we introduce a family of stochastic gradient estimation te...
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11/07/2018
Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference
Particle-based variational inference offers a flexible way of approximat...
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05/29/2018
Forward Amortized Inference for Likelihood-Free Variational Marginalization
In this paper, we introduce a new form of amortized variational inferenc...
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05/29/2018
Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of a...
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05/19/2017
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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05/16/2017
GP CaKe: Effective brain connectivity with causal kernels
A fundamental goal in network neuroscience is to understand how activity...
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04/10/2017
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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02/17/2017
Estimating Nonlinear Dynamics with the ConvNet Smoother
Estimating the state of a dynamical system from a series of noise-corrup...
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10/31/2016
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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05/09/2016