Structurally-informed Bayesian functional connectivity analysis. - Max Hinne; Luca Ambrogioni; Ronald J. Janssen; Tom Heskes; Marcel A. J. van Gerven (NeuroImage, 2014)

GP CaKe: Effective brain connectivity with causal kernels. - Luca Ambrogioni; Max Hinne; Marcel van Gerven; Eric Maris (NIPS, 2017)

Dynamic decomposition of spatiotemporal neural signals. - Luca Ambrogioni; Marcel A. J. van Gerven; Eric Maris (PLoS Comput. Biol., 2017)

Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis. - Luca Ambrogioni; Eric Maris (AISTATS, 2018)

Wasserstein Variational Inference. - Luca Ambrogioni; Umut Güçlü; Yagmur Güçlütürk; Max Hinne; Marcel A. J. van Gerven; Eric Maris (NeurIPS, 2018)

Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference. - Luca Ambrogioni; Umut Güçlü; Yagmur Güçlütürk; Marcel van Gerven (CoRR, 2018)

Generative adversarial networks for reconstructing natural images from brain activity. - Katja Seeliger; Umut Güçlü; Luca Ambrogioni; Yagmur Güçlütürk; Marcel A. J. van Gerven (NeuroImage, 2018)

SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity. - Luca Ambrogioni; Patrick Ebel 002; Max Hinne; Umut Güçlü; Marcel van Gerven; Eric Maris (AISTATS, 2019)

Forward Amortized Inference for Likelihood-Free Variational Marginalization. - Luca Ambrogioni; Umut Güçlü; Julia Berezutskaya; Eva W. P. van den Borne; Yagmur Güçlütürk; Max Hinne; Eric Maris; Marcel van Gerven (AISTATS, 2019)

Segmentation of Photovoltaic Panels in Aerial Photography Using Group Equivariant FCNs. - Lars Bokkers; Luca Ambrogioni; Umut Güçlü (BNAIC/BENELEARN, 2019)

Temporal Factorization of 3D Convolutional Kernels. - Gabrielle Ras; Luca Ambrogioni; Umut Güçlü; Marcel van Gerven (BNAIC/BENELEARN, 2019)

k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport. - Luca Ambrogioni; Umut Güçlü; Marcel van Gerven (CoRR, 2019)

Causal inference using Bayesian non-parametric quasi-experimental design. - Max Hinne; Marcel A. J. van Gerven; Luca Ambrogioni (CoRR, 2019)

Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks. - Gabriëlle Ras; Ron Dotsch; Luca Ambrogioni; Umut Güçlü; Marcel A. J. van Gerven (CoRR, 2019)

Complex-valued gaussian process regression for time series analysis. - Luca Ambrogioni; Eric Maris (Signal Process., 2019)

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. - Nasir Ahmad; Marcel A. J. van Gerven; Luca Ambrogioni (NeurIPS, 2020)

The Indian Chefs Process. - Patrick Dallaire; Luca Ambrogioni; Ludovic Trottier; Umut Güçlü; Max Hinne; Philippe Giguère; Marcel van Gerven; François Laviolette (UAI, 2020)

The Indian Chefs Process. - Patrick Dallaire; Luca Ambrogioni; Ludovic Trottier; Umut Güçlü; Max Hinne; Philippe Giguère; Brahim Chaib-draa; Marcel van Gerven; François Laviolette (CoRR, 2020)

Automatic structured variational inference. - Luca Ambrogioni; Max Hinne; Marcel van Gerven (CoRR, 2020)

Spike-Timing-Dependent Inference of Synaptic Weights. - Nasir Ahmad; Luca Ambrogioni; Marcel A. J. van Gerven (CoRR, 2020)

Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations. - Gabriëlle Ras; Luca Ambrogioni; Pim Haselager; Marcel A. J. van Gerven; Umut Güçlü (CoRR, 2020)

Automatic structured variational inference. - Luca Ambrogioni; Kate Lin; Emily Fertig; Sharad Vikram; Max Hinne; Dave Moore; Marcel van Gerven (AISTATS, 2021)

Automatic variational inference with cascading flows. - Luca Ambrogioni; Gianluigi Silvestri; Marcel van Gerven (ICML, 2021)

Scaling up learning with GAIT-prop. - Sander Dalm; Nasir Ahmad; Luca Ambrogioni; Marcel van Gerven (CoRR, 2021)

Knowledge is reward: Learning optimal exploration by predictive reward cashing. - Luca Ambrogioni (CoRR, 2021)

End-to-end neural system identification with neural information flow. - Katja Seeliger; Luca Ambrogioni; Yagmur Güçlütürk; L. M. van den Bulk; Umut Güçlü; Marcel A. J. van Gerven (PLoS Comput. Biol., 2021)

Towards Naturalistic Speech Decoding from Intracranial Brain Data. - Julia Berezutskaya; Luca Ambrogioni; Nick F. Ramsey; Marcel A. J. van Gerven (EMBC, 2022)

Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling. - Gianluigi Silvestri; Emily Fertig; Dave Moore; Luca Ambrogioni (ICLR, 2022)

Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. - Gianluigi Silvestri; Daan Roos; Luca Ambrogioni (CoRR, 2022)

Deterministic training of generative autoencoders using invertible layers. - Gianluigi Silvestri; Daan Roos; Luca Ambrogioni (ICLR, 2023)

Spontaneous symmetry breaking in generative diffusion models. - Gabriel Raya; Luca Ambrogioni (NeurIPS, 2023)

Stationarity without mean reversion: Improper Gaussian process regression and improper kernels. - Luca Ambrogioni (CoRR, 2023)

The statistical thermodynamics of generative diffusion models. - Luca Ambrogioni (CoRR, 2023)

Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks. - Sander Dalm; Nasir Ahmad; Luca Ambrogioni; Marcel van Gerven (Trans. Mach. Learn. Res., 2023)

Stationarity without mean reversion in improper Gaussian processes. - Luca Ambrogioni (ICML, 2024)

Robust and highly scalable estimation of directional couplings from time-shifted signals. - Luca Ambrogioni; Louis Rouillard; Demian Wassermann (CoRR, 2024)

Losing dimensions: Geometric memorization in generative diffusion. - Beatrice Achilli; Enrico Ventura; Gianluigi Silvestri; Bao Pham; Gabriel Raya; Dmitry Krotov; Carlo Lucibello; Luca Ambrogioni (CoRR, 2024)

In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks. - Luca Ambrogioni (Entropy, 2024)

Dynamic Negative Guidance of Diffusion Models. - Felix Koulischer; Johannes Deleu; Gabriel Raya; Thomas Demeester; Luca Ambrogioni (ICLR, 2025)

Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion. - Enrico Ventura; Beatrice Achilli; Gianluigi Silvestri; Carlo Lucibello; Luca Ambrogioni (ICLR, 2025)

Training Consistency Models with Variational Noise Coupling. - Gianluigi Silvestri; Luca Ambrogioni; Chieh-Hsin Lai; Yuhta Takida; Yuki Mitsufuji (CoRR, 2025)

Entropic Time Schedulers for Generative Diffusion Models. - Dejan Stancevic; Luca Ambrogioni (CoRR, 2025)

Memorization to Generalization: Emergence of Diffusion Models from Associative Memory. - Bao Pham; Gabriel Raya; Matteo Negri; Mohammed J. Zaki; Luca Ambrogioni; Dmitry Krotov (CoRR, 2025)

Feedback Guidance of Diffusion Models. - Felix Koulischer; Florian Handke; Johannes Deleu; Thomas Demeester; Luca Ambrogioni (CoRR, 2025)

Measuring Semantic Information Production in Generative Diffusion Models. - Florian Handke; Felix Koulischer; Gabriel Raya; Luca Ambrogioni (CoRR, 2025)

CoVAE: Consistency Training of Variational Autoencoders. - Gianluigi Silvestri; Luca Ambrogioni (CoRR, 2025)