Summary of Ngpt: Normalized Transformer with Representation Learning on the Hypersphere, by Ilya Loshchilov et al.
nGPT: Normalized Transformer with Representation Learning on the Hypersphere
by Ilya Loshchilov, Cheng-Ping Hsieh, Simeng Sun, Boris Ginsburg
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed normalized Transformer (nGPT) neural network architecture incorporates representation learning on the hypersphere. This novel approach normalizes all vectors forming embeddings, MLP, attention matrices, and hidden states. The input stream of tokens travels on the surface of a hypersphere, with each layer contributing a displacement towards target output predictions defined by the MLP and attention blocks. Experiments demonstrate that nGPT learns significantly faster, reducing training steps required to achieve the same accuracy by a factor of 4 to 20 depending on sequence length. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of neural network is being developed called the normalized Transformer (nGPT). This network uses a special way of learning representations that happens on the surface of a high-dimensional sphere. All the different components of the network, like the embeddings and attention mechanisms, are all forced to have lengths of one unit. This has the effect of making the network learn much faster than previous versions. In fact, it can be up to 20 times faster depending on how long the input sequences are. |
Keywords
» Artificial intelligence » Attention » Neural network » Representation learning » Transformer
