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ORCIDLuca Ambrogioni
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Journal Articles
- 2024
[j7]Luca Ambrogioni:
In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks. Entropy 26(5): 381 (2024)- 2023
- [j6]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) - 2021
- [j5]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) - 2019
- [j4]Luca Ambrogioni, Eric Maris:
Complex-valued gaussian process regression for time series analysis. Signal Process. 160: 215-228 (2019) - 2018
- [j3]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: 775-785 (2018) - 2017
- [j2]Luca Ambrogioni, Marcel A. J. van Gerven, Eric Maris:
Dynamic decomposition of spatiotemporal neural signals. PLoS Comput. Biol. 13(5) (2017) - 2014
- [j1]Max Hinne, Luca Ambrogioni, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven:
Structurally-informed Bayesian functional connectivity analysis. NeuroImage 86: 294-305 (2014)
Conference and Workshop Papers
- 2024
- [c16]Luca Ambrogioni:
Stationarity without mean reversion in improper Gaussian processes. ICML 2024 - 2023
- [c15]Gianluigi Silvestri, Daan Roos, Luca Ambrogioni:
Deterministic training of generative autoencoders using invertible layers. ICLR 2023 - [c14]Gabriel Raya, Luca Ambrogioni:
Spontaneous symmetry breaking in generative diffusion models. NeurIPS 2023 - 2022
- [c13]Julia Berezutskaya, Luca Ambrogioni, Nick F. Ramsey, Marcel A. J. van Gerven:
Towards Naturalistic Speech Decoding from Intracranial Brain Data. EMBC 2022: 3100-3104 - [c12]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 - 2021
- [c11]Luca Ambrogioni, Kate Lin, Emily Fertig, Sharad Vikram, Max Hinne, Dave Moore, Marcel van Gerven:
Automatic structured variational inference. AISTATS 2021: 676-684 - [c10]Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven:
Automatic variational inference with cascading flows. ICML 2021: 254-263 - 2020
- [c9]Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni:
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. NeurIPS 2020 - [c8]Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Marcel van Gerven, François Laviolette:
The Indian Chefs Process. UAI 2020: 600-608 - 2019
- [c7]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: 777-786 - [c6]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: 787-795 - [c5]Lars Bokkers, Luca Ambrogioni, Umut Güçlü:
Segmentation of Photovoltaic Panels in Aerial Photography Using Group Equivariant FCNs. BNAIC/BENELEARN 2019 - [c4]Gabrielle Ras, Luca Ambrogioni, Umut Güçlü, Marcel van Gerven:
Temporal Factorization of 3D Convolutional Kernels. BNAIC/BENELEARN 2019 - 2018
- [c3]Luca Ambrogioni, Eric Maris:
Integral Transforms from Finite Data: An Application of Gaussian Process Regression to Fourier Analysis. AISTATS 2018: 217-225 - [c2]Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris:
Wasserstein Variational Inference. NeurIPS 2018: 2478-2487 - 2017
- [c1]Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris:
GP CaKe: Effective brain connectivity with causal kernels. NIPS 2017: 950-959
Informal and Other Publications
- 2024
- [i22]Luca Ambrogioni, Louis Rouillard, Demian Wassermann:
Robust and highly scalable estimation of directional couplings from time-shifted signals. CoRR abs/2406.02545 (2024) - 2023
- [i21]Gabriel Raya, Luca Ambrogioni:
Spontaneous symmetry breaking in generative diffusion models. CoRR abs/2305.19693 (2023) - [i20]Luca Ambrogioni:
In search of dispersed memories: Generative diffusion models are associative memory networks. CoRR abs/2309.17290 (2023) - [i19]Luca Ambrogioni:
Stationarity without mean reversion: Improper Gaussian process regression and improper kernels. CoRR abs/2310.02877 (2023) - [i18]Luca Ambrogioni:
The statistical thermodynamics of generative diffusion models. CoRR abs/2310.17467 (2023) - 2022
- [i17]Gianluigi Silvestri, Daan Roos, Luca Ambrogioni:
Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. CoRR abs/2205.09546 (2022) - 2021
- [i16]Luca Ambrogioni, Gianluigi Silvestri, Marcel van Gerven:
Automatic variational inference with cascading flows. CoRR abs/2102.04801 (2021) - [i15]Sander Dalm, Nasir Ahmad, Luca Ambrogioni, Marcel van Gerven:
Scaling up learning with GAIT-prop. CoRR abs/2102.11598 (2021) - [i14]Luca Ambrogioni:
Knowledge is reward: Learning optimal exploration by predictive reward cashing. CoRR abs/2109.08518 (2021) - [i13]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 abs/2110.06021 (2021) - 2020
- [i12]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 abs/2001.10657 (2020) - [i11]Luca Ambrogioni, Max Hinne, Marcel van Gerven:
Automatic structured variational inference. CoRR abs/2002.00643 (2020) - [i10]Nasir Ahmad, Luca Ambrogioni, Marcel A. J. van Gerven:
Spike-Timing-Dependent Inference of Synaptic Weights. CoRR abs/2003.03988 (2020) - [i9]Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni:
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error. CoRR abs/2006.06438 (2020) - [i8]Gabriëlle Ras, Luca Ambrogioni, Pim Haselager, Marcel A. J. van Gerven, Umut Güçlü:
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations. CoRR abs/2006.15983 (2020) - 2019
- [i7]Luca Ambrogioni, Umut Güçlü, Marcel van Gerven:
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport. CoRR abs/1907.04050 (2019) - [i6]Max Hinne, Marcel A. J. van Gerven, Luca Ambrogioni:
Causal inference using Bayesian non-parametric quasi-experimental design. CoRR abs/1911.06722 (2019) - [i5]Gabriëlle Ras, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven:
Temporal Factorization of 3D Convolutional Kernels. CoRR abs/1912.04075 (2019) - [i4]Gabriëlle Ras, Ron Dotsch, Luca Ambrogioni, Umut Güçlü, Marcel A. J. van Gerven:
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks. CoRR abs/1912.09831 (2019) - 2018
- [i3]Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, Eric Maris:
Wasserstein Variational Inference. CoRR abs/1805.11284 (2018) - [i2]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 abs/1805.11542 (2018) - [i1]Luca Ambrogioni, Umut Güçlü, Yagmur Güçlütürk, Marcel van Gerven:
Wasserstein Variational Gradient Descent: From Semi-Discrete Optimal Transport to Ensemble Variational Inference. CoRR abs/1811.02827 (2018)
Coauthor Index
aka: Marcel A. J. van Gerven
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