Summary of Multiway Multislice Phate: Visualizing Hidden Dynamics Of Rnns Through Training, by Jiancheng Xie et al.
Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training
by Jiancheng Xie, Lou C. Kohler Voinov, Noga Mudrik, Gal Mishne, Adam Charles
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recurrent neural networks (RNNs) are powerful tools for analyzing sequential data, but their internal workings remain opaque. Developing ideal architectures and optimization strategies requires understanding the functional principles of these networks. Previous studies have focused on network representations after training, overlooking the evolution process during training. This paper presents MM-PHATE, a novel method for visualizing RNNs’ hidden states throughout training. MM-PHATE uses graph-based embeddings with structured kernels across time, epoch, and units to capture community structures among units and identify information processing and compression phases. The authors demonstrate MM-PHATE’s effectiveness on various datasets, showing that it uniquely preserves representation structure and identifies key dynamics during RNN training. This embedding enables users to “look under the hood” of RNNs, providing an intuitive strategy for understanding internal workings and drawing conclusions about model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping us understand how recurrent neural networks (RNNs) work during training. RNNs are powerful tools for analyzing data that changes over time, but they’re often mysterious “black boxes”. The authors want to change this by creating a new way to visualize what’s happening inside the network as it learns. They call this method MM-PHATE and use it to study how different parts of the network work together and learn from the training data. This helps us understand why one RNN might be better than another or how changing the architecture affects how well it learns. The goal is to make RNNs more transparent and easier to work with. |
Keywords
» Artificial intelligence » Embedding » Optimization » Rnn
