# Neural Teleportation

arXiv: https://arxiv.org/abs/2012.01118 · HTML: https://mlsanity.com/paper/2012.01118/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 18 of 18)

- [Hidden symmetries of ReLU networks](https://mlsanity.com/paper/2306.06179/) (2023)
- [Symmetry in Neural Network Parameter Spaces](https://mlsanity.com/paper/2506.13018/) (2025)
- [Symmetries, flat minima, and the conserved quantities of gradient flow](https://mlsanity.com/paper/2210.17216/) (2022)
- [Symmetry Teleportation for Accelerated Optimization](https://mlsanity.com/paper/2205.10637/) (2022)
- [Scale Equivariant Graph Metanetworks](https://mlsanity.com/paper/2406.10685/) (2024)
- [Improving Convergence and Generalization Using Parameter Symmetries](https://mlsanity.com/paper/2305.13404/) (2023)
- [Universal approximation and model compression for radial neural networks](https://mlsanity.com/paper/2107.02550/) (2021)
- [Path-conditioned training: a principled way to rescale ReLU neural networks](https://mlsanity.com/paper/2602.19799/) (2026)
- [Singular Value Representation: A New Graph Perspective On Neural Networks](https://mlsanity.com/paper/2302.08183/) (2023)
- [Continual Optimization with Symmetry Teleportation for Multi-Task Learning](https://mlsanity.com/paper/2503.04046/) (2025)
- [Teleportation With Null Space Gradient Projection for Optimization Acceleration](https://mlsanity.com/paper/2502.11362/) (2025)
- [Improving Learning to Optimize Using Parameter Symmetries](https://mlsanity.com/paper/2504.15399/) (2025)
- [Level Set Teleportation: An Optimization Perspective](https://mlsanity.com/paper/2403.03362/) (2024)
- [WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols](https://mlsanity.com/paper/2512.00272/) (2025)
- [A Theory of Synaptic Neural Balance: From Local to Global Order](https://mlsanity.com/paper/2405.09688/) (2024)
- [Advancing Neural Network Performance through Emergence-Promoting Initialization Scheme](https://mlsanity.com/paper/2407.19044/) (2024)
- [Discovering Symmetries in Neural Network Parameter Spaces](https://mlsanity.com/paper/2609.33527/) (2026)
- [Hidden Activations are not Enough I: Knowledge Matrices as Higher Representations](https://mlsanity.com/paper/2609.34166/) (2026)

## Abstract (arXiv)

In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation "teleports" a network to a new position in the weight space and preserves its function. This phenomenon comes directly from the definitions of representation theory applied to neural networks and it turns out to be a very simple operation that has remarkable properties. We shed light on surprising and counter-intuitive consequences neural teleportation has on the loss landscape. In particular, we show that teleportation can be used to explore loss level curves, that it changes the local loss landscape, sharpens global minima and boosts back-propagated gradients at any moment during the learning process. Our results can be reproduced with the code available here: https://github.com/vitalab/neuralteleportation