Luca Ambrogioni

Orcid: 0000-0003-3673-0825

According to our database1, Luca Ambrogioni authored at least 36 papers between 2014 and 2024.

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Bibliography

2024
In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks.
Entropy, May, 2024

Robust and highly scalable estimation of directional couplings from time-shifted signals.
CoRR, 2024

2023
Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks.
Trans. Mach. Learn. Res., 2023

The statistical thermodynamics of generative diffusion models.
CoRR, 2023

Stationarity without mean reversion: Improper Gaussian process regression and improper kernels.
CoRR, 2023

Spontaneous symmetry breaking in generative diffusion models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deterministic training of generative autoencoders using invertible layers.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers.
CoRR, 2022

Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Towards Naturalistic Speech Decoding from Intracranial Brain Data.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

2021
End-to-end neural system identification with neural information flow.
PLoS Comput. Biol., 2021

Knowledge is reward: Learning optimal exploration by predictive reward cashing.
CoRR, 2021

Scaling up learning with GAIT-prop.
CoRR, 2021

Automatic variational inference with cascading flows.
Proceedings of the 38th International Conference on Machine Learning, 2021

Automatic structured variational inference.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations.
CoRR, 2020

Spike-Timing-Dependent Inference of Synaptic Weights.
CoRR, 2020

Automatic structured variational inference.
CoRR, 2020

The Indian Chefs Process.
CoRR, 2020

The Indian Chefs Process.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Complex-valued gaussian process regression for time series analysis.
Signal Process., 2019

Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks.
CoRR, 2019

Causal inference using Bayesian non-parametric quasi-experimental design.
CoRR, 2019

k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport.
CoRR, 2019

Temporal Factorization of 3D Convolutional Kernels.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

Segmentation of Photovoltaic Panels in Aerial Photography Using Group Equivariant FCNs.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

Forward Amortized Inference for Likelihood-Free Variational Marginalization.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Generative adversarial networks for reconstructing natural images from brain activity.
NeuroImage, 2018

Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference.
CoRR, 2018

Wasserstein Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Dynamic decomposition of spatiotemporal neural signals.
PLoS Comput. Biol., 2017

GP CaKe: Effective brain connectivity with causal kernels.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2014
Structurally-informed Bayesian functional connectivity analysis.
NeuroImage, 2014


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