# LossPlot: A Better Way to Visualize Loss Landscapes

arXiv: https://arxiv.org/abs/2111.15133 · HTML: https://mlsanity.com/paper/2111.15133/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Essentially No Barriers in Neural Network Energy Landscape](https://mlsanity.com/paper/1803.00885/) (2018)
- [Qualitatively characterizing neural network optimization problems](https://mlsanity.com/paper/1412.6544/) (2014)
- [A survey of dimensionality reduction techniques](https://mlsanity.com/paper/1403.2877/) (2014)
- [Direct-Manipulation Visualization of Deep Networks](https://mlsanity.com/paper/1708.03788/) (2017)
- [Loss Patterns of Neural Networks](https://mlsanity.com/paper/1910.03867/) (2019)

## Built on by (most cited first, 5 of 5)

- [Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments](https://mlsanity.com/paper/2205.07015/) (2022)
- [Deep Loss Convexification for Learning Iterative Models](https://mlsanity.com/paper/2411.10649/) (2024)
- [Visualizing the Loss Landscape of Winning Lottery Tickets](https://mlsanity.com/paper/2112.08538/) (2021)
- [FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks](https://mlsanity.com/paper/2204.04511/) (2022)
- [ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization](https://mlsanity.com/paper/2505.04647/) (2025)

## Abstract (arXiv)

Investigations into the loss landscapes of deep neural networks are often laborious. This work documents our user-driven approach to create a platform for semi-automating this process. LossPlot accepts data in the form of a csv, and allows multiple trained minimizers of the loss function to be manipulated in sync. Other features include a simple yet intuitive checkbox UI, summary statistics, and the ability to control clipping which other methods do not offer.