Jean-Pascal Pfister

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 26

Top Topics

Quasi Monte Carlo

Poisson Process

Intrinsic Dimensionality

Diffusion Process

Top Venues

PLoS Comput. Biol.

Publications

Hui-An Shen, Jean-Pascal Pfister, Robert Graczyk
Optimal Activation Functions via Causal Wyner-Ziv Coding. ISIT (Workshops) (2024)
Christian Horvat, Jean-Pascal Pfister
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models. CoRR (2024)
Christian Horvat, Jean-Pascal Pfister
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models. ICLR (2024)
Hui-An Shen, Stefan M. Moser, Jean-Pascal Pfister
A Generalization of the Equal Coding Theorem. ITW (2023)
Camille Gontier, Simone Carlo Surace, Igor Delvendahl, Martin Müller, Jean-Pascal Pfister
Efficient sampling-based Bayesian Active Learning for synaptic characterization. PLoS Comput. Biol. 19 (8) (2023)
Christian Horvat, Jean-Pascal Pfister
Density estimation on low-dimensional manifolds: an inflation-deflation approach. J. Mach. Learn. Res. 24 (2023)
Christian Horvat, Jean-Pascal Pfister
Intrinsic dimensionality estimation using Normalizing Flows. NeurIPS (2022)
Hui-An Shen, Stefan M. Moser, Jean-Pascal Pfister
Rate-Distortion Problems of the Poisson Process based on a Group-Theoretic Approach. CoRR (2022)
Ehsan Abedi, Simone Carlo Surace, Jean-Pascal Pfister
A Unification of Weighted and Unweighted Particle Filters. SIAM J. Control. Optim. 60 (2) (2022)
Jannes Jegminat, Maya A. Jastrzebowska, Matthew V. Pachai, Michael H. Herzog, Jean-Pascal Pfister
Correction: Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput. Biol. 18 (3) (2022)
Jannes Jegminat, Simone Carlo Surace, Jean-Pascal Pfister
Learning as filtering: Implications for spike-based plasticity. PLoS Comput. Biol. 18 (2) (2022)
Hui-An Shen, Stefan M. Moser, Jean-Pascal Pfister
Rate-Distortion Problems of the Poisson Process: a Group-Theoretic Approach. ITW (2021)
Christian Horvat, Jean-Pascal Pfister
Denoising Normalizing Flow. NeurIPS (2021)
Christian Horvat, Jean-Pascal Pfister
Density estimation: an inflation-deflation approach. CoRR (2021)
Matthieu Gilson, Jean-Pascal Pfister
Propagation of Spiking Moments in Linear Hawkes Networks. SIAM J. Appl. Dyn. Syst. 19 (2) (2020)
Ehsan Abedi, Simone Carlo Surace, Jean-Pascal Pfister
A Unification of Weighted and Unweighted Particle Filters. CoRR (2020)
Hui-An Shen, Stefan M. Moser, Jean-Pascal Pfister
Sphere Covering for Poisson Processes. ITW (2020)
Jannes Jegminat, Maya A. Jastrzebowska, Matthew V. Pachai, Michael H. Herzog, Jean-Pascal Pfister
Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput. Biol. 16 (5) (2020)
Camille Gontier, Jean-Pascal Pfister
Identifiability of a Binomial Synapse. Frontiers Comput. Neurosci. 14 (2020)
Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni Brea
On the choice of metric in gradient-based theories of brain function. PLoS Comput. Biol. 16 (4) (2020)
Simone Carlo Surace, Anna Kutschireiter, Jean-Pascal Pfister
Asymptotically Exact Unweighted Particle Filter for Manifold-Valued Hidden States and Point Process Observations. IEEE Control. Syst. Lett. 4 (2) (2020)
Simone Carlo Surace, Jean-Pascal Pfister
Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes. IEEE Trans. Autom. Control. 64 (7) (2019)
Simone Carlo Surace, Anna Kutschireiter, Jean-Pascal Pfister
How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and without Importance Weights. SIAM Rev. 61 (1) (2019)
Matthieu Gilson, Jean-Pascal Pfister
Propagation of moments in Hawkes networks. CoRR (2018)
Arko Ghosh, Jean-Pascal Pfister, Matthew Cook
Optimised information gathering in smartphone users. CoRR (2017)
Jean-Pascal Pfister, Arko Ghosh
Quantifying the priority placed on scale-free smartphone actions. CoRR (2016)
Johanni Brea, Walter Senn, Jean-Pascal Pfister
Sequence learning with hidden units in spiking neural networks. NIPS (2011)
Guillaume Hennequin, Wulfram Gerstner, Jean-Pascal Pfister
STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. Frontiers Comput. Neurosci. 4 (2010)
Jean-Pascal Pfister, Peter A. Tass
STDP in Oscillatory Recurrent Networks: Theoretical Conditions for Desynchronization and Applications to Deep Brain Stimulation. Frontiers Comput. Neurosci. 4 (2010)
Jean-Pascal Pfister, Peter Dayan, Máté Lengyel
Know Thy Neighbour: A Normative Theory of Synaptic Depression. NIPS (2009)
Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner
Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution. Neural Comput. 19 (3) (2007)
Jean-Pascal Pfister, Taro Toyoizumi, David Barber, Wulfram Gerstner
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Comput. 18 (6) (2006)
Jean-Pascal Pfister, Wulfram Gerstner
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects. NIPS (2005)
Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model. NIPS (2004)
Jean-Pascal Pfister, David Barber, Wulfram Gerstner
Optimal Hebbian Learning: A Probabilistic Point of View. ICANN (2003)