Kristopher T Jensen — accepted AI-conference papers

2023

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

Martin Bjerke, Lukas Schott, Kristopher T Jensen, Claudia Battistin, David A. Klindt, Benjamin Adric Dunn

ICLR 2023poster

Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activ…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗

2022

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data

Marine Schimel, Ta-Chu Kao, Kristopher T Jensen, Guillaume Hennequin

ICLR 2022oral

Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories…

Cited by 31 Source ↗PDF ↗Scholar ↗

2021

Natural continual learning: success is a journey, not (just) a destination

Ta-Chu Kao, Kristopher T Jensen, Gido Martijn van de Ven, Alberto Bernacchia, Guillaume Hennequin

NeurIPS 2021poster

Biological agents are known to learn many different tasks over the course of their lives, and to be able to revisit previous tasks and behaviors with little to no loss in performance. In contrast, artificial agents are prone to ‘catastrophic forgetting’ whereby performance on previous tasks deterior…

Cited by 69 Source ↗PDF ↗code ↗Scholar ↗

2021

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models

Kristopher T Jensen, Ta-Chu Kao, Jasmine Talia Stone, Guillaume Hennequin

NeurIPS 2021poster

Latent variable models are ubiquitous in the exploratory analysis of neural population recordings, where they allow researchers to summarize the activity of large populations of neurons in lower dimensional ‘latent’ spaces. Existing methods can generally be categorized into (i) Bayesian methods that…

Cited by 25 Source ↗PDF ↗Scholar ↗