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Jean-Pascal Pfister
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2020 – today
- 2024
- [c12]Christian Horvat, Jean-Pascal Pfister:
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models. ICLR 2024 - [c11]Hui-An Shen
, Jean-Pascal Pfister, Robert Graczyk:
Optimal Activation Functions via Causal Wyner-Ziv Coding. ISIT (Workshops) 2024: 1-5 - [i7]Christian Horvat, Jean-Pascal Pfister:
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models. CoRR abs/2402.03845 (2024) - 2023
- [j16]Christian Horvat, Jean-Pascal Pfister:
Density estimation on low-dimensional manifolds: an inflation-deflation approach. J. Mach. Learn. Res. 24: 61:1-61:37 (2023) - [j15]Camille Gontier
Efficient sampling-based Bayesian Active Learning for synaptic characterization. PLoS Comput. Biol. 19(8) (2023) - [c10]Hui-An Shen
A Generalization of the Equal Coding Theorem. ITW 2023: 329-334 - 2022
- [j14]Jannes Jegminat
Learning as filtering: Implications for spike-based plasticity. PLoS Comput. Biol. 18(2) (2022) - [j13]Jannes Jegminat, Maya A. Jastrzebowska, Matthew V. Pachai, Michael H. Herzog
Correction: Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput. Biol. 18(3) (2022) - [j12]Ehsan Abedi
A Unification of Weighted and Unweighted Particle Filters. SIAM J. Control. Optim. 60(2): 597-619 (2022) - [c9]Christian Horvat, Jean-Pascal Pfister:
Intrinsic dimensionality estimation using Normalizing Flows. NeurIPS 2022 - [i6]Hui-An Shen, Stefan M. Moser, Jean-Pascal Pfister:
Rate-Distortion Problems of the Poisson Process based on a Group-Theoretic Approach. CoRR abs/2202.13684 (2022) - 2021
- [c8]Hui-An Shen
Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach. ITW 2021: 1-6 - [c7]Christian Horvat, Jean-Pascal Pfister:
Denoising Normalizing Flow. NeurIPS 2021: 9099-9111 - [i5]Christian Horvat, Jean-Pascal Pfister:
Density estimation: an inflation-deflation approach. CoRR abs/2105.12152 (2021) - 2020
- [j11]Simone Carlo Surace
Asymptotically Exact Unweighted Particle Filter for Manifold-Valued Hidden States and Point Process Observations. IEEE Control. Syst. Lett. 4(2): 480-485 (2020) - [j10]Camille Gontier, Jean-Pascal Pfister
Identifiability of a Binomial Synapse. Frontiers Comput. Neurosci. 14: 558477 (2020) - [j9]Simone Carlo Surace
On the choice of metric in gradient-based theories of brain function. PLoS Comput. Biol. 16(4) (2020) - [j8]Jannes Jegminat
Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput. Biol. 16(5) (2020) - [j7]Matthieu Gilson
Propagation of Spiking Moments in Linear Hawkes Networks. SIAM J. Appl. Dyn. Syst. 19(2): 828-859 (2020) - [c6]Hui-An Shen
Sphere Covering for Poisson Processes. ITW 2020: 1-5 - [i4]Ehsan Abedi
A Unification of Weighted and Unweighted Particle Filters. CoRR abs/2011.13804 (2020)
2010 – 2019
- 2019
- [j6]Simone Carlo Surace, Anna Kutschireiter
How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and without Importance Weights. SIAM Rev. 61(1): 79-91 (2019) - [j5]Simone Carlo Surace
Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes. IEEE Trans. Autom. Control. 64(7): 2814-2829 (2019) - 2018
- [i3]Matthieu Gilson, Jean-Pascal Pfister:
Propagation of moments in Hawkes networks. CoRR abs/1810.09520 (2018) - 2017
- [i2]Arko Ghosh, Jean-Pascal Pfister, Matthew Cook:
Optimised information gathering in smartphone users. CoRR abs/1701.02796 (2017) - 2016
- [i1]Jean-Pascal Pfister, Arko Ghosh:
Quantifying the priority placed on scale-free smartphone actions. CoRR abs/1612.03196 (2016) - 2014
- [r2]Walter Senn, Jean-Pascal Pfister:
Reinforcement Learning in Cortical Networks. Encyclopedia of Computational Neuroscience 2014 - [r1]Walter Senn, Jean-Pascal Pfister:
Spike-Timing-Dependent Plasticity, Learning Rules. Encyclopedia of Computational Neuroscience 2014 - 2011
- [c5]Johanni Brea, Walter Senn, Jean-Pascal Pfister:
Sequence learning with hidden units in spiking neural networks. NIPS 2011: 1422-1430 - 2010
- [j4]Jean-Pascal Pfister
STDP in Oscillatory Recurrent Networks: Theoretical Conditions for Desynchronization and Applications to Deep Brain Stimulation. Frontiers Comput. Neurosci. 4: 22 (2010) - [j3]Guillaume Hennequin, Wulfram Gerstner
STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. Frontiers Comput. Neurosci. 4: 143 (2010)
2000 – 2009
- 2009
- [c4]Jean-Pascal Pfister, Peter Dayan, Máté Lengyel:
Know Thy Neighbour: A Normative Theory of Synaptic Depression. NIPS 2009: 1464-1472 - 2007
- [j2]Taro Toyoizumi
Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution. Neural Comput. 19(3): 639-671 (2007) - 2006
- [j1]Jean-Pascal Pfister
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Comput. 18(6): 1318-1348 (2006) - 2005
- [c3]Jean-Pascal Pfister, Wulfram Gerstner:
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects. NIPS 2005: 1081-1088 - 2004
- [c2]Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner:
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model. NIPS 2004: 1409-1416 - 2003
- [c1]Jean-Pascal Pfister
Optimal Hebbian Learning: A Probabilistic Point of View. ICANN 2003: 92-98
Coauthor Index
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