Luca Ambrogioni

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 43

Top Topics

Variational Inference

Gaussian Process Regression

Probabilistic Modeling

Top Venues

BNAIC/BENELEARN

Publications

Luca Ambrogioni
In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks. Entropy 26 (5) (2024)
Luca Ambrogioni, Louis Rouillard, Demian Wassermann
Robust and highly scalable estimation of directional couplings from time-shifted signals. CoRR (2024)
Luca Ambrogioni
In search of dispersed memories: Generative diffusion models are associative memory networks. CoRR (2023)
Gabriel Raya, Luca Ambrogioni
Spontaneous symmetry breaking in generative diffusion models. NeurIPS (2023)
Luca Ambrogioni
The statistical thermodynamics of generative diffusion models. CoRR (2023)
Gianluigi Silvestri, Daan Roos, Luca Ambrogioni
Deterministic training of generative autoencoders using invertible layers. ICLR (2023)
Sander Dalm, Nasir Ahmad, Luca Ambrogioni, Marcel van Gerven
Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Luca Ambrogioni
Stationarity without mean reversion: Improper Gaussian process regression and improper kernels. CoRR (2023)
Gabriel Raya, Luca Ambrogioni
Spontaneous symmetry breaking in generative diffusion models. CoRR (2023)
Gianluigi Silvestri, Emily Fertig, Dave Moore, Luca Ambrogioni
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling. ICLR (2022)
Gianluigi Silvestri, Daan Roos, Luca Ambrogioni
Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. CoRR (2022)
Julia Berezutskaya, Luca Ambrogioni, Nick F. Ramsey, Marcel A. J. van Gerven
Towards Naturalistic Speech Decoding from Intracranial Brain Data. EMBC (2022)
Gianluigi Silvestri, Emily Fertig, Dave Moore, Luca Ambrogioni
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling. CoRR (2021)
Sander Dalm, Nasir Ahmad, Luca Ambrogioni, Marcel van Gerven
Scaling up learning with GAIT-prop. CoRR (2021)
Katja Seeliger, Luca Ambrogioni, Yagmur Güçlütürk, L. M. van den Bulk, Umut Güçlü, Marcel A. J. van Gerven
End-to-end neural system identification with neural information flow. PLoS Comput. Biol. 17 (2) (2021)
Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven
Automatic structured variational inference. AISTATS (2021)
Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
Automatic variational inference with cascading flows. ICML (2021)
Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven
Automatic variational inference with cascading flows. CoRR (2021)
Luca Ambrogioni
Knowledge is reward: Learning optimal exploration by predictive reward cashing. CoRR (2021)
Nasir Ahmad, Luca Ambrogioni, Marcel A. J. van Gerven
Spike-Timing-Dependent Inference of Synaptic Weights. CoRR (2020)
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-draa, Marcel van Gerven, François Laviolette
The Indian Chefs Process. CoRR (2020)
Luca Ambrogioni, Max Hinne, Marcel van Gerven
Automatic structured variational inference. CoRR (2020)
Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. NeurIPS (2020)
Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. CoRR (2020)
Gabriëlle Ras, Luca Ambrogioni, Pim Haselager, Marcel A. J. van Gerven, Umut Güçlü
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations. CoRR (2020)
Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel van Gerven, François Laviolette
The Indian Chefs Process. UAI (2020)
Gabriëlle Ras, Ron Dotsch, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks. CoRR (2019)
Luca Ambrogioni, Eric Maris
Complex-valued gaussian process regression for time series analysis. Signal Process. 160 (2019)
Gabrielle Ras, Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
Temporal Factorization of 3D Convolutional Kernels. BNAIC/BENELEARN (2019)
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yagmur Güçlütürk, Max Hinne, Eric Maris, Marcel van Gerven
Forward Amortized Inference for Likelihood-Free Variational Marginalization. AISTATS (2019)
Max Hinne, Marcel A. J. van Gerven, Luca Ambrogioni
Causal inference using Bayesian non-parametric quasi-experimental design. CoRR (2019)
Luca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel van Gerven, Eric Maris
SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity. AISTATS (2019)
Gabriëlle Ras, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven
Temporal Factorization of 3D Convolutional Kernels. CoRR (2019)
Luca Ambrogioni, Umut Güçlü, Marcel van Gerven
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport. CoRR (2019)
Lars Bokkers, Luca Ambrogioni, Umut Güçlü
Segmentation of Photovoltaic Panels in Aerial Photography Using Group Equivariant FCNs. BNAIC/BENELEARN (2019)
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yagmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
Forward Amortized Inference for Likelihood-Free Variational Marginalization. CoRR (2018)
Luca Ambrogioni, Eric Maris
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis. AISTATS (2018)
Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Marcel van Gerven
Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference. CoRR (2018)
Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris
Wasserstein Variational Inference. CoRR (2018)
Katja Seeliger, Umut Güçlü, Luca Ambrogioni, Yagmur Güçlütürk, Marcel A. J. van Gerven
Generative adversarial networks for reconstructing natural images from brain activity. NeuroImage 181 (2018)
Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris
Wasserstein Variational Inference. NeurIPS (2018)
Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris
GP CaKe: Effective brain connectivity with causal kernels. NIPS (2017)
Luca Ambrogioni, Marcel A. J. van Gerven, Eric Maris
Dynamic decomposition of spatiotemporal neural signals. PLoS Comput. Biol. 13 (5) (2017)
Max Hinne, Luca Ambrogioni, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven
Structurally-informed Bayesian functional connectivity analysis. NeuroImage 86 (2014)