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Tiberiu Tesileanu

4 accepted papers

2024

Multiple Physics Pretraining for Spatiotemporal Surrogate Models

NeurIPS 2024poster

We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. In MPP, rather than training one model on a specific physical system, we train a backbone model to predict the dynamics o…

Cited by 3SourcePDFScholar
2023

An Online Algorithm for Contrastive Principal Component Analysis

ICASSP 2023accepted

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive lear…

Cited by 0SourceScholar
2022

Biological Learning of Irreducible Representations of Commuting Transformations

NeurIPS 2022accept

A longstanding challenge in neuroscience is to understand neural mechanisms underlying the brain’s remarkable ability to learn and detect transformations of objects due to motion. Translations and rotations of images can be viewed as orthogonal transformations in the space of pixel intensity vectors…

Cited by 4SourcePDFScholar
2022

Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy

NeurIPS 2022accept

Predictive coding (PC) has emerged as an influential normative model of neural computation with numerous extensions and applications. As such, much effort has been put into mapping PC faithfully onto the cortex, but there are issues that remain unresolved or controversial. In particular, current imp…